CN114626119A

CN114626119A - Maintenance intelligent decision method and system based on bridge big data

Info

Publication number: CN114626119A
Application number: CN202111230734.5A
Authority: CN
Inventors: 刘松; 林杰; 李长杰; 王晨; 赖敏芝; 黄思璐; 万里
Original assignee: Hubei Communications Investment Intelligent Detection Co ltd
Current assignee: Hubei Communications Investment Intelligent Detection Co ltd
Priority date: 2021-10-21
Filing date: 2021-10-21
Publication date: 2022-06-14
Anticipated expiration: 2041-10-21
Also published as: CN114626119B

Abstract

The invention discloses a maintenance intelligent decision-making method and system based on bridge big data, which realize intelligent and digital management of beam type bridge maintenance, and realize electronic informatization and form standardization of daily inspection, regular inspection and special inspection of a bridge. The method can be used for mastering the technical condition and safety of the beam bridge in real time, scientifically and reasonably determining the maintenance scheme, improving the maintenance management efficiency, fully playing the maintenance fund role and playing an important role in guaranteeing the bridge structure safety and traffic safety. The system can find diseases in time, evaluate and classify the diseases in time, count and analyze the diseases in time, predict and early warn in time, select a maintenance scheme in time, eliminate the diseases in time, ensure the safety of the bridge structure and the life and property safety of people to the maximum extent, prolong the service life of the bridge, and provide an intelligent and effective solution for the huge maintenance and management work of the public bridge system in China.

Description

Maintenance intelligent decision method and system based on bridge big data

Technical Field

The invention relates to the field of highway bridge safety maintenance, in particular to a maintenance intelligent decision method and system based on bridge big data.

Background

China is a genuine bridge big country, and by the end of 2018, the number of highway bridges in China reaches 85.15 ten thousand and 5568.59 ten thousand meters, so that traffic development and economic and social development are effectively promoted. The beam type bridge is the bridge type which is most widely applied to roads in China and is also the bridge type with the most common diseases. Once the bridge has structural damage, if the bridge is not maintained in time, the bridge is continuously developed under the repeated action of vehicle load, so that the bearing capacity of the bridge is continuously reduced, and the safety of the bridge structure and the traffic safety are seriously influenced. However, what is not in good order with bridges is that the development of national maintenance management technology lags behind, the inspection and detection of bridges mainly depends on manual daily inspection, periodic inspection and special inspection, the digitization of bridge structure assets and the intelligent classification and evaluation of technical conditions are not realized, diseases are difficult to find in time and eliminate in time, bridge structure safety accidents are difficult to avoid, the life and property safety of people are damaged, and the service life of bridges is influenced.

Although the national standard has relevant regulations on methods for maintaining, detecting and monitoring highway bridges and plays an important role in evaluating the technical conditions of the bridges, the method has defects in analyzing the causes of bridge diseases and analyzing the influence of the causes on the bearing capacity or service life of the bridges. The existing maintenance and maintenance scheme of the bridge mainly depends on the evaluation result of the current technical condition, the maintenance scheme is determined by means of manual experience, expert investigation and review meetings, and the bridge maintenance scheme is difficult to be scientifically and reasonably determined due to the lack of analysis of big data early warning bridge diseases of highway bridges and the evolution rules of the bridge diseases. Therefore, it is urgent to establish a set of maintenance scheme intelligent decision system based on bridge, especially highway beam type bridge big data.

The existing bridge management system lacks comprehensiveness, standardization and systematicness in detection data acquisition and entry, and lacks a corresponding medium-long term prediction and early warning mechanism. The maintenance of the crack diseases of the bridge member is mainly characterized in that a maintenance scheme is determined by detecting the depth of the crack under special conditions according to the width of the crack. The method cannot comprehensively and accurately reflect the influence of cracks on the safety of the bridge structure, and is difficult to avoid causing misjudgment, so that the decision of the maintenance scheme is not accurate enough. The severity of the bridge crack disease is not only related to the crack width, but also closely related to the development speed and the technical condition evolution rule of the bridge crack disease, so that an intelligent decision-making system with strong pertinence, more accuracy and more comprehensiveness for bridge maintenance and maintenance is formulated according to the current bridge technical condition and by comprehensively analyzing the type, severity, deterioration speed and disease reason of the disease in combination with the bridge technical condition evolution rule.

Disclosure of Invention

Aiming at the current bridge maintenance technical situation in China, the invention aims to provide an intelligent maintenance decision method and system based on bridge big data, which realize the scientific decision of the bridge maintenance of a highway, solve the problems of the existing bridge maintenance scheme that the decision is not accurate enough, comprehensive enough, early warning is not timely enough and the like, ensure that a bridge is effectively maintained in time and ensure the safety of the bridge structure and the life and property safety of people.

The application provides a maintenance intelligent decision-making method based on bridge big data, the highway bridge big data is the synthesis of various data of a plurality of bridges, including:

setting an application database corresponding to each bridge, wherein the application database comprises a bridge information database and a model database; the bridge information database comprises bridge structure asset big data, bridge technical condition big data and bridge health monitoring big data, and the bridge health monitoring big data correspond to a continuous steel bridge or/and a continuous cast-in-place box girder bridge; the big data of the bridge structure assets are basic information data of the bridge, and the basic information data comprise types, quantity, geometric dimensions and physical and mechanical properties of all components of the bridge;

systematically numbering all the components of the bridge, and giving each component a unique identity mark which is used as an anchoring carrier for disease and technical condition big data of the component; the bridge technical condition big data comprise all technical parameters reflecting bridge technical conditions and detection data of diseases generated by the components;

a serial number is given to each disease generated by the member, and a one-to-one corresponding relation between the member and the disease generated by the member is established;

analyzing and counting the disease detection data generated by the member to obtain the technical condition score of the member, obtaining the technical condition scores of all the components of the bridge according to the technical condition score of the member, and obtaining the total technical condition score of the bridge according to the technical condition scores of all the components of the bridge;

classifying all the components, parts and the whole bridge according to preset regulations, and classifying the components, parts and the whole bridge into 5 classes according to grade marks;

acquiring monitoring data, including acquiring monitoring data of displacement, deformation, deflection, strain and crack width of a key structure part of a continuous steel bridge or/and a continuous cast-in-place box girder bridge corresponding to the bridge health monitoring big data in real time through real-time monitoring equipment, and acquiring humiture environment monitoring data, traffic data obtained by video monitoring and video data of vehicle type characteristics;

setting an early warning value, establishing a bridge finite element model, obtaining a theoretical calculation value, and setting yellow early warning values and red early warning values of strain and deflection according to the theoretical calculation value;

establishing the model base, wherein the model base comprises a forecasting mathematical model of each key index of displacement, deformation, deflection, strain and crack width by utilizing the big data of the bridge technical condition and the big data of the bridge health monitoring, generating evolution rule information for forecasting the extension of the bridge and the member along with the service time through the forecasting mathematical model of the key index, and setting the evolution rule information as the related information of bridge disease diagnosis;

setting an early warning value of a disease key index according to the prediction mathematical model of the key index; and when one monitoring data exceeds the early warning value, executing early warning operation and generating a corresponding maintenance intelligent decision scheme.

Optionally, the real-time monitoring device comprises a sensor and/or a video monitoring system, the sensor comprises a displacement sensor, a deformation sensor, a deflection sensor, a strain sensor, a crack width sensor, a temperature sensor and a humidity sensor which are arranged at the designated position of the beam bridge, and data collected by the sensor is uploaded to the cloud platform in a wireless transmission mode to be stored.

Optionally, the highway beam type bridge comprises a common reinforced concrete beam type bridge, a prestressed reinforced concrete beam type bridge, a continuous steel structure bridge and a continuous cast-in-place box girder.

Optionally, the bridge component includes a prestressed concrete structure, a rectangular plate of a non-prestressed concrete structure, a T-beam, a hollow plate beam, an I-beam, and a box beam, and the T-beam includes a web, a diaphragm, and a wing plate.

Optionally, the crack data in the big bridge technical condition data includes key parameters of cracks generated in the bridge member, and the key parameters include data of crack length, width, position and direction.

Optionally, the acquiring monitoring data comprises:

acquiring big data for monitoring the bridge health, determining the types and the number of monitoring parts and sensors on the component, and respectively endowing unique identification marks on each monitoring part and each sensor; and collecting the measuring point data acquired by various sensors corresponding to the monitoring part in real time, converting the measuring point data into a monitoring time course curve trend chart of the monitoring indexes of the designated part of the bridge, and analyzing the evolution rule information of the key indexes of the bridge.

Optionally, the obtaining method of the bridge disease key index prediction mathematical model includes: and performing curve fitting on the detection and/or monitoring data acquired by each key index at different time, taking the time as an abscissa and the detection or monitoring data as an ordinate, performing curve fitting by adopting different functions, and selecting the function with the highest correlation coefficient as a certain key index prediction mathematical model of the bridge for analyzing the development trend prediction of each key index and analyzing and evaluating the disease severity.

The application also provides a maintenance intelligence decision-making system based on bridge big data, includes:

the data module is used for setting an application database corresponding to each bridge, and the application database comprises a bridge information database and a model database; the bridge information database comprises bridge structure asset big data, bridge technical condition big data and bridge health monitoring big data, and the bridge health monitoring big data correspond to a continuous steel bridge or/and a continuous cast-in-place box girder bridge; the big data of the bridge structure assets are basic information data of the bridge, and the basic information data comprise types, quantity, geometric dimensions and physical and mechanical properties of all components of the bridge;

the component numbering module is used for carrying out system numbering on all the component components of the bridge and endowing each component with a unique identity mark, and the identity mark is used as an anchoring carrier of disease and technical condition big data of the component; the bridge technical condition big data comprise all technical parameters reflecting bridge technical conditions and detection data of diseases generated by the components;

the association module is used for granting a serial number to each disease generated by the component and establishing a one-to-one correspondence relationship between the component and the disease generated by the component;

the analysis module is used for analyzing and counting the disease detection data generated by the member to obtain the technical condition scores of the member, obtaining the technical condition scores of all the components of the bridge according to the technical condition scores of the member, and obtaining the total technical condition scores of the bridge according to the technical condition scores of all the components of the bridge;

the classification module is used for classifying all the components, parts and the whole bridge of the bridge according to preset regulations and classifying the components, parts and the whole bridge into 5 classes according to grade marks;

the data acquisition module is used for acquiring monitoring data, and comprises real-time monitoring data of displacement, deformation, deflection, strain and crack width of a key structure part of the continuous steel bridge or/and the continuous cast-in-place box girder bridge corresponding to the bridge health monitoring big data, as well as acquiring temperature and humidity environment monitoring data, traffic data obtained by video monitoring and video data of vehicle type characteristics;

an early warning module; setting an early warning value, establishing a bridge finite element model, obtaining a theoretical calculation value, and setting yellow early warning values and red early warning values of strain and deflection according to the theoretical calculation value;

the model base module is used for establishing the model base, the model base comprises a prediction mathematical model of each key index in displacement, deformation, deflection, strain and crack width by utilizing the big data of the bridge technical condition and the big data of the bridge health monitoring, evolution rule information for predicting the extension of the bridge and the structural member along with the service time is generated through the prediction mathematical model of the key index, and the evolution rule information is set as the relevant information for diagnosing the bridge diseases;

the scheme output module is used for setting a disease key index early warning value according to the prediction mathematical model of the key index; and when one monitoring data exceeds the early warning value, executing early warning operation and generating a corresponding maintenance scheme.

The application also provides maintenance scheme intelligent decision-making electronic equipment based on bridge big data, which comprises a processor and a memory, wherein the memory is stored with computer programs and bridge detection and monitoring data, and the processor executes the maintenance scheme intelligent decision-making electronic equipment to realize the following steps:

step 1, building highway bridge big data, including bridge structure asset big data, bridge technical condition big data and bridge health monitoring big data; the large data of the bridge structure assets comprise data formed by all the components of the bridge and key indexes such as the geometric dimension, the physical and mechanical properties and the like of the components; endowing all bridge component members with unique identity marks, serving as anchoring carriers of bridge diseases, and converting electronic statistical tables of various component members of the bridge structure assets of the cost system;

the method comprises the steps of collecting big data of the technical condition of the bridge, collecting all disease detection data of each bridge and the component members of the bridge in the system, establishing a one-to-one corresponding relation between the component members of the bridge and the diseases generated by the component members of the bridge, analyzing evolution rules of the technical condition of the bridge, and converting the evolution rules into standard statistical spreadsheets;

the bridge health monitoring big data is used for the bridges with complex structures such as continuous cast-in-place box girders, and the like, and a displacement, deformation, deflection, strain, crack width, temperature and humidity sensor and a video monitoring system are adopted to collect the displacement, deformation, deflection, stress strain, crack width, temperature, humidity environment and traffic load data of key structural parts of the bridges in a normal test state in real time, analyze the evolution rule of the key structural parts of the bridges along with the time extension, set an early warning value according to the structural design of the bridges, timely give an early warning when certain monitoring data exceeds the early warning value, and take necessary measures to ensure the structural safety and traffic safety of the bridges.

And 2, inputting the road bridge big data into a bridge and a bridge member technical condition evaluation and classification unit for processing, and automatically evaluating and classifying the technical conditions. The evaluation is a layered evaluation and comprises the evaluation of each component of the bridge; evaluating each part of the bridge; respectively evaluating a bridge deck system, an upper structure and a lower structure of the bridge and evaluating the overall technical condition of the bridge, classifying the technical conditions of the components, parts, the bridge deck system, the upper structure, the lower structure and the whole bridge of the bridge according to the technical condition evaluation result, and dividing the components into 5 types according to different severity degrees to generate a technical condition evaluation and technical condition classification grade spreadsheet of the bridge and the components thereof;

and 3, inputting the bridge big data in the step 1, the bridge technical condition evaluation and classification data in the step 2 and the prediction data of the bridge structural disease key index prediction mathematical model into a bridge disease intelligent diagnosis unit, automatically diagnosing the bridge diseases by a computer, and determining the disease attribute, the severity, the evolution rule and the disease reason.

And 4, according to the successful case of maintenance of the similar bridges and the components thereof at home and abroad, researching the disease types, severity, evolution rules and disease reasons applicable to various maintenance schemes, and establishing various disease maintenance scheme libraries for the bridges.

And step 5, a maintenance scheme intelligent optimization decision unit automatically optimizes the optimal maintenance scheme of the bridge from the maintenance scheme library of various diseases of the bridge in the step 4 by a computer according to the technical condition evaluation and classification results of the bridge and the components thereof in the step 2, and the bridge and the component technical condition index prediction mathematical model, the bridge health monitoring key index prediction mathematical model and the bridge structural crack width prediction mathematical model of the bridge, and by combining the disease attribute, the severity, the evolution rule and the disease reason determined by the bridge disease diagnosis in the step 3.

Optionally, in step 2, the bridge and its component technical condition evaluation and classification unit automatically evaluates and classifies the technical conditions of the bridge and its component to obtain the technical condition score and the technical condition classification level of the bridge and its component, and the bridge and its component technical condition evaluation and classification unit includes:

according to a method combining hierarchical comprehensive evaluation in Highway bridge technical condition evaluation Standard (JTGT H212011) and 5 types of bridge individual control indexes, the big data of the bridge structure assets, the big data of the bridge technical condition and the big data of the bridge health monitoring are evaluated, each component of the bridge is evaluated, then each component of the bridge is evaluated, a bridge deck system, an upper structure and a lower structure are evaluated respectively, and finally the overall technical condition of the bridge is evaluated; and dividing the evaluation scale of the technical condition of the main parts of the bridge into 1 type, 2 type, 3 type, 4 type and 5 type according to the evaluation result, and then carrying out the overall technical condition evaluation of the bridge, wherein the evaluation scale is divided into 1 type, 2 type, 3 type, 4 type and 5 type.

Optionally, the big data of the bridge structure assets refers to data including structural types, geometric dimensions and concrete strength of all component members of a highway beam bridge; the highway beam type bridge comprises a common reinforced concrete beam type bridge, a prestressed reinforced concrete beam type bridge, a continuous steel structure bridge and a continuous cast-in-place box girder; all the components of the highway beam type bridge comprise a T beam with a prestressed concrete structure or a non-prestressed concrete structure, a hollow slab beam, a box beam and each component refined to the bridge, such as the T beam is solidified to a web plate, a top plate and a diaphragm.

Optionally, the big data of the bridge technical condition refers to data which is formed by all technical parameters reflecting the bridge technical condition and physical parameters of a bridge fault, and includes detection data of the length, width, position and direction of a crack generated by a bridge member; the detection data of all faults occurring in each component of the bridge form a technical condition detection spreadsheet of the bridge contained in a certain section, a technical condition detection spreadsheet of all bridges managed by a certain unit, a technical condition grading and technical condition classification grade spreadsheet of the bridge and the components thereof, a statistical spreadsheet of the bridge components of each technical condition grade and an occupation map.

Optionally, the bridge health monitoring big data is data obtained by monitoring strain, deflection, displacement, deformation, crack width, temperature and humidity of key parts of the bridge in real time, traffic flow and vehicle load, and periodically monitoring the natural frequency; and each monitoring data utilizes measured data of a temperature sensor, a humidity sensor, a displacement sensor, a deformation sensor, a strain sensor and a crack width sensor which are arranged at the designated position of the beam type bridge, and the data is stored through wireless transmission and a cloud platform.

Optionally, the bridge health monitoring big data is according to technical rules of highway bridge structure safety monitoring system, the whole structure response monitoring content includes structure vibration, displacement and deformation, the vibration and deformation of various bridge types are monitored in real time, and the self-vibration frequency of the bridge is obtained by regularly monitoring the bridge, and the content is as follows:

(1) firstly, each monitoring part and each sensor are endowed with unique identity marks,

(2) arranging strain measurement points, and converting real-time monitoring data into a strain monitoring time-course curve trend graph of the designated part of the bridge;

(3) arranging deflection measuring points, and converting real-time monitoring data into a deflection monitoring time-course curve trend graph of the specified part of the bridge;

(4) arranging natural vibration frequency measuring points, and converting real-time monitoring data into a natural vibration frequency monitoring time course curve trend graph of a specified bridge component;

(5) setting an early warning value, establishing a bridge finite element model, obtaining a theoretical calculation value, and setting a yellow early warning value and a red early warning value of strain and deflection according to the theoretical calculation value;

(6) comparing the monitoring data of each measuring point of the highway bridge health monitoring with the theoretical calculation result of the secondary early warning value to generate a secondary early warning value electronic list;

(7) and carrying out data processing analysis by using health monitoring data for at least three months to obtain the evolution rule of the bridge disease.

Optionally, the bridge and the component technical condition evaluation and classification unit thereof are further configured to establish a bridge health monitoring key index prediction mathematical model according to key indexes of a continuous steel bridge or a continuous cast-in-place box girder bridge, wherein the key indexes include deformation, displacement, stress, strain and crack width, the bridge structural damage key index prediction mathematical model is used for performing curve fitting on scattered point monitoring data of each key index by establishing a one-to-one correspondence relationship between a bridge component and a damage generated by the bridge component, selecting a function with the highest correlation coefficient as a certain key index prediction mathematical model of the bridge structural damage, and fitting a trend curve by a scattered point distribution diagram for evaluating the severity of the damage and predicting and analyzing the development trend.

Optionally, the bridge disease intelligent diagnosis unit is used for performing bridge disease diagnosis and analysis on the bridge and the component members thereof according to the bridge and the component member technical condition evaluation values thereof and the prediction results of the bridge structural disease key index prediction mathematical model, and by combining bridge health monitoring data and evolution rule analysis thereof, determining the disease attribute, severity, evolution rule and disease cause of the bridge, and forming a structural disease key index statistical table of each bridge; and a key index statistical table of structural diseases of all bridges managed by a certain management unit;

optionally, the intelligent optimization decision unit compares the current technical condition evaluation and classification result of the bridge and the bridge member, the bridge technical condition evolution rule prediction result and the bridge disease diagnosis result with various maintenance schemes in a maintenance scheme library of various diseases of the bridge and different degrees of severity thereof, and finally optimizes the optimal maintenance scheme by a computer, and counts the maintenance scheme of the current diseases of the bridge and a bridge disease statistical table required to be maintained to generate a maintenance scheme statistical table of the diseases of all bridges managed by a certain bridge or a certain unit.

The application also provides another maintenance scheme intelligent decision system establishment method based on bridge big data, which comprises the following steps:

step S1: establishing a bridge big database, inputting the collected big data of the bridge structure assets, the big data of the bridge technical conditions and the big data of the bridge health monitoring into the bridge big database, automatically outputting a standardized acquisition list of the big data of the bridge based on the big data of the assets of the bridge structure, the big data of the technical condition of the bridge and the big data of the monitoring of the health of the bridge, the bridge big data standardized acquisition list comprises a statistical table of various component members of bridge structure assets, a bridge technical condition statistical table of each road section, a statistical table of all bridge technical conditions managed by each unit or a statistical table and a proportion map of bridge members of each technical condition grade, a statistical table of structural damage key indexes of each bridge or a statistical table of structural damage of all bridges managed by a certain management unit, and a physical parameter and damage record table of a concrete member of the big data of the road bridge structure assets;

step S2: establishing a bridge and a component technical condition evaluation and classification unit thereof, automatically evaluating and classifying the technical conditions of the bridge and the component thereof by utilizing the bridge structure asset big data, the bridge technical condition big data and the bridge health monitoring big data to obtain the technical condition grade and the technical condition classification grade of the bridge and the component thereof, and establishing a bridge structural disease key index prediction mathematical model by utilizing the technical condition evaluation data of the bridge and the component thereof obtained by multiple times of bridge detection data at different times; for the bridge structural crack, establishing a crack width prediction mathematical model of the bridge structural crack diseases by utilizing multiple crack width detection data at different times; the bridge structural damage key index prediction mathematical model is established by using key index health monitoring data of a continuous steel bridge or a continuous cast-in-place box girder bridge, wherein the key indexes comprise deformation, displacement, strain, deflection and crack width, the scattered point monitoring data of each key index is subjected to curve fitting by establishing a one-to-one corresponding relation between a bridge component and a damage generated by the bridge component, a function with the highest correlation coefficient is selected as a certain key index prediction mathematical model of the bridge structural damage, and a trend curve is fitted by a scattered point distribution diagram and is used for evaluating the severity of the damage and developing trend prediction analysis;

the automatic evaluation and classification of the technical conditions of the bridge and the component members thereof on the bridge structure asset big data, the bridge technical condition big data and the bridge health monitoring big data to obtain the technical condition score and the technical condition classification grade of the bridge and the component members thereof specifically comprises the following steps: according to a method of combining hierarchical comprehensive evaluation in highway bridge technical condition evaluation standards with single control indexes of 5 types of bridges, evaluating all bridge components, evaluating bridge deck systems, upper structures and lower structures respectively, and finally evaluating the overall technical condition of the bridges, dividing the overall technical condition evaluation grades of the bridges into 1 type, 2 types, 3 types, 4 types and 5 types according to evaluation results, and dividing the technical condition evaluation scales of main parts of the bridges into 1 type, 2 types, 3 types, 4 types and 5 types;

step S3: establishing a bridge disease intelligent diagnosis unit, establishing a one-to-one corresponding relation between bridge members and the generated diseases by applying the large data of the bridge structure assets, the large data of the technical conditions and the large data of the health monitoring, and performing disease diagnosis on the bridge and the members composed of the bridge by using the prediction result of the bridge structure disease key index prediction mathematical model obtained from the step 2 to obtain the attributes, the severity, the evolution rule and the disease reasons of the bridge and the members;

when the technical condition evaluation value of the bridge or the component reaches the second-class standard or above, or the deterioration speed of the bridge technical condition evaluation value is predicted to be accelerated through a mathematical model; or when the width of the structural crack is predicted through a mathematical model and the development speed of the structural crack is accelerated, the system timely carries out early warning, lists bridge parts needing to be maintained are listed, so that minor repair maintenance or maintenance reinforcement can be timely carried out on the bridge, and the preliminary maintenance scheme is determined according to the bridge and the technical condition evaluation classification of the bridge parts:

one, normal maintenance;

second, minor repair;

third, middle repair;

fourthly, major repair and reconstruction;

fifthly, dismantling and rebuilding;

step S4: the operation associates the successful cases of the maintenance of the similar bridges and the components thereof at home and abroad with the types, the severity, the evolution rule and the causes of the diseases one by one, and stores the cases in a scheme library for the maintenance of various diseases and bridges of the bridges;

step S5: establishing an intelligent optimization decision unit, and preferably selecting an optimal maintenance scheme as a final maintenance scheme of the bridge according to the current technical condition data of the bridge and the components thereof, the bridge technical condition evolution prediction result, the structural crack evolution rule prediction result generated by the bridge components, the diagnosis data of the disease attribute, the severity and the disease reason determined by the bridge disease diagnosis, and the comparison result of each maintenance scheme in the maintenance scheme library; the optimal maintenance reinforcement scheme of the similar diseases is extracted through a large amount of application experience analysis to form a machine learning algorithm, so that a technical basis is provided for later bridge maintenance scheme decision; and (4) utilizing the annual maintenance scheme of the bridge and the evolution rule analysis of the technical condition of the bridge to make an annual maintenance plan and a five-year maintenance plan of the bridge managed by a certain unit.

According to the present bridge technical situation, the type, the severity and the deterioration speed of the diseases are comprehensively analyzed by combining with the evolution rule of the bridge technical situation, and the maintenance scheme with strong pertinence is intelligently and preferably determined.

The method for determining the bridge maintenance scheme is more scientific and reasonable by determining the bridge maintenance scheme not only according to the technical condition evaluation result and the current disease situation determined by the current-year bridge detection, but also according to the technical condition or the disease evolution rule predicted and comprehensively determined by the multi-year detection.

The invention realizes the digitization of the assets and the digitization of the technical conditions of the bridge structure, and can provide digital services for customers, including bridge technical condition evaluation, bridge disease diagnosis and bridge maintenance scheme optimization decision; by using the detection data and the health monitoring data of years, the bridge technical condition prediction and the disease development rule prediction can be carried out, and digital services such as an annual maintenance scheme, an annual maintenance plan, a five-year maintenance plan and the like are provided for customers.

The invention realizes the intelligent and digital management of beam type bridge maintenance, the daily inspection, the regular inspection and the special inspection of the bridge, and the electronic informatization and the form standardization. The method can be used for mastering the technical condition and safety of the beam bridge in real time, scientifically and reasonably determining the maintenance scheme, improving the maintenance management efficiency, fully playing the maintenance fund role and playing an important role in guaranteeing the bridge structure safety and traffic safety. The system can find diseases in time, evaluate and classify the diseases in time, count and analyze the diseases in time, predict and early warn in time, select a maintenance scheme in time, eliminate the diseases in time, ensure the safety of the bridge structure and the life and property safety of people to the maximum extent, prolong the service life of the bridge, and provide an intelligent and effective solution for the huge maintenance and management work of the public bridge system in China.

Drawings

FIG. 1 is a schematic diagram of a system module of an intelligent maintenance scheme decision system based on bridge big data according to the present invention;

FIG. 2 is a schematic diagram of the composition of bridge big data according to the present invention;

FIG. 3 is a flow chart of a method for establishing an intelligent maintenance scheme decision system based on bridge big data according to the present invention;

FIG. 4 is a schematic longitudinal section arrangement diagram of a bridge strain sensor measuring point in embodiment 2;

FIG. 5 is a schematic view showing the cross-sectional arrangement of the bridge strain sensor measuring points in embodiment 2;

FIG. 6 is a schematic diagram of a longitudinal section arrangement of measurement points of a sensor of the bridge disturbance degree detector in embodiment 2;

FIG. 7 is a schematic diagram showing a cross-sectional arrangement of a sensor measuring point of the bridge disturbance degree detector in embodiment 2;

FIG. 8 is a schematic longitudinal section layout view of a bridge vibration sensor measuring point in embodiment 2;

FIG. 9 is a schematic view showing the cross-sectional arrangement of the measuring points of the bridge vibration sensor in the embodiment 2;

FIG. 10 is a schematic view of a bridge structure calculation model;

FIG. 11 is a strain monitoring time course curve of the first span midspan control section of the main bridge in the embodiment 2;

FIG. 12 is a strain monitoring time course curve of the first cross-pivot section of the main bridge in example 2;

FIG. 13 is a strain monitoring time course curve across section 1/4L at No. 2 of the main bridge in example 2;

FIG. 14 is a strain monitoring time course curve across a midspan section at No. 2 of the main bridge in example 2;

FIG. 15 is a cross-sectional strain monitoring time course curve of the main bridge No. 2 cross the fulcrum in example 2;

FIG. 16 is a strain monitoring time course curve across the mid-span cross section at No. 3 of the main bridge in the embodiment 2;

FIG. 17 is a strain monitoring time course curve across a mid-span cross section at the 4 th position of the main bridge in the embodiment 2;

FIG. 18 is a time course curve of monitoring the deflection of the main bridge No. 1 across the control section in the embodiment 2;

FIG. 19 is a time course curve of monitoring the deflection of the main bridge No. 2 across the control section in the embodiment 2;

FIG. 20 is a monitoring time course curve of the 3 rd span control section deflection of the main bridge in the embodiment 2;

FIG. 21 is a time course curve of monitoring the deflection of the 4 th span control section of the main bridge in the embodiment 2;

FIG. 22 shows the first self-oscillation frequency monitoring value (1.563Hz) in example 2;

FIG. 23 shows the second self-oscillation frequency monitoring value (1.563Hz) in example 2;

FIG. 24 shows the third self-oscillation frequency monitoring value (1.563Hz) in example 2;

FIG. 25 is a flow chart of disease diagnosis for a bridge and its component parts based on bridge technology status and bridge big data.

Detailed Description

In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.

In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.

The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

In an embodiment of the present invention, the present application provides an intelligent maintenance decision method based on big bridge data, where the big bridge data is a combination of various data of a plurality of bridges, and the method includes:

setting an application database corresponding to each bridge, wherein the application database comprises a bridge information database and a model database; the bridge information database comprises bridge structure asset big data, bridge technical condition big data and bridge health monitoring big data, and the bridge health monitoring big data correspond to a continuous steel bridge or/and a continuous cast-in-place box girder bridge; the big data of the bridge structure assets are basic information data of the bridge, and the big data comprises the data of the type, the number, the geometric dimension, the physical and mechanical properties and the like of all the components of the bridge;

In the embodiment of the invention, the real-time monitoring equipment comprises a sensor and/or a video monitoring system, the sensor comprises a displacement sensor, a deformation sensor, a deflection sensor, a strain sensor, a crack width sensor, a temperature sensor and a humidity sensor which are arranged at the designated position of the beam bridge, and data acquired by the sensor is uploaded to a cloud platform in a wireless transmission mode to be stored.

In the embodiment of the invention, the road beam type bridge comprises a common reinforced concrete beam type bridge, a prestressed reinforced concrete beam type bridge, a continuous steel structure bridge and a continuous cast-in-place box girder.

In the embodiment of the invention, the bridge component comprises a prestressed concrete structure, a rectangular plate of a non-prestressed concrete structure, a T beam, a hollow plate beam, an I-shaped beam and a box beam, wherein the T beam comprises a web plate, a diaphragm plate and a wing plate.

In the embodiment of the invention, the crack data in the bridge technical condition big data comprises key parameters of cracks generated in the bridge member, and the key parameters comprise data of the length, the width, the position and the direction of the cracks.

In an embodiment of the present invention, the acquiring monitoring data includes:

In the embodiment of the invention, the method for obtaining the bridge disease key index prediction mathematical model comprises the following steps: and performing curve fitting on the detection and/or monitoring data acquired by each key index at different time, taking the time as an abscissa and the detection or monitoring data as an ordinate, performing curve fitting by adopting different functions, and selecting the function with the highest correlation coefficient as a certain key index prediction mathematical model of the bridge for analyzing the development trend prediction of each key index and analyzing and evaluating the disease severity.

In an embodiment of the present invention, the present application further provides a maintenance intelligent decision system based on bridge big data, including:

the data module is used for establishing an application database corresponding to each bridge, and the application database comprises a bridge information database and a model database; the bridge information database comprises bridge structure asset big data, bridge technical condition big data and bridge health monitoring big data, and the bridge health monitoring big data correspond to a continuous steel bridge or/and a continuous cast-in-place box girder bridge; the big data of the bridge structure assets are basic information data of the bridge, and the basic information data comprise types, quantity, geometric dimensions and physical and mechanical properties of all components of the bridge;

the component numbering module is used for carrying out system numbering on all the component components of the bridge and endowing each component with a unique identity mark, and the identity mark is used as an anchoring carrier of disease and technical condition big data of the component; the big bridge technical condition data comprise all technical parameters reflecting the technical conditions of the bridge and detection data of the faults of the components;

an early warning module; setting an early warning value, establishing a bridge finite element model, obtaining a theoretical calculation value, and setting a yellow early warning value and a red early warning value of strain and deflection according to the theoretical calculation value;

the model library module is used for establishing the model library, the model library comprises a prediction mathematical model for each key index of displacement, deformation, deflection, strain and crack width by utilizing the big data of the bridge technical condition and the big data of the bridge health monitoring, evolution rule information for predicting the extension of the bridge and the component along with the service time is generated through the prediction mathematical model for the key index, and the evolution rule information is set as the related information for diagnosing the bridge diseases;

The maintenance scheme intelligent decision-making system based on bridge big data is shown in figure 1 and comprises a bridge big database, a bridge and component technical condition evaluation and classification unit, a bridge disease intelligent diagnosis unit and a bridge maintenance scheme intelligent optimization decision-making unit.

Referring to fig. 3, the method for establishing the system of the invention comprises the following steps:

step 1: establishing a bridge big database, as shown in figure 2, inputting the relevant data of the highway bridge of the system into the big data of the bridge structure assets, inputting the data by a hand-held data acquisition terminal, establishing the big data of the bridge technical condition, acquiring monitoring data by a sensor arranged on site, the cloud platform is used for inputting into a computer to establish bridge health monitoring big data, a standardized acquisition list of the bridge big data of the system is generated, the list comprises a statistical table of various component members of the bridge structure assets, a statistical table of the bridge technical conditions of a certain road section, a statistical table of all bridge technical conditions managed by a certain unit or a statistical table and a proportion chart of bridge member of each technical condition grade, a statistical table of the structural damage key indexes of each bridge or a statistical table of the structural damage of all bridges managed by a certain management unit, and a physical parameter and damage record table of the road bridge structure assets big data-specific members.

A field-mounted sensor having strain gauge locations comprising: the bridge strain measuring points are arranged on the top of a main span box girder and the bottom plate of the box girder according to equal division points and the cross section of a pier top, the side spans are arranged on the cross section of a main span and the cross section of the pier top, and the measuring points of the deflection monitor are arranged in the main girder main span and the side span.

Step 2: the method comprises the steps of establishing bridges and a structural member technical condition evaluation and classification unit thereof, establishing a bridge finite element model by utilizing a computer statistical analysis method and a machine learning algorithm, evaluating bridge structural members, parts, a bridge deck system, an upper structure, a lower structure and a full bridge according to 3.1.1 highway bridge technical condition evaluation standard (JTGT H21-2011) in China, evaluating each structural member of the bridge, evaluating each part of the bridge, evaluating the bridge deck system, the upper structure and the lower structure respectively and evaluating the overall technical condition of the bridge by adopting a method of combining hierarchical comprehensive evaluation and 5 types of bridge single control indexes. 3.2.3 the evaluation grades of the overall technical conditions of the bridge are divided into 1 class, 2 class, 3 class, 4 class and 5 class. 3.2.4 the evaluation scale of the technical condition of the main parts of the bridge is divided into 1 type, 2 type, 3 type, 4 type and 5 type. Evaluating the technical conditions of a specified bridge and the components thereof, classifying the bridge and the components thereof according to a bridge technical condition evaluation standard, listing a technical condition classification statistical table and a technical condition classification proportion chart of the bridge and the components thereof, a technical condition classification statistical table of all bridge technical conditions managed by a certain unit, a technical condition evaluation result scoring table of a specific part of a certain highway bridge, and a two-stage early warning value list of each measuring point for monitoring the health of the highway bridge to obtain a bridge technical condition evaluation and prediction result, fitting by adopting a polynomial function of w (t) at3+ bt2+ ct + d, a logarithmic function of w (t) alnt + b and an exponential function of w (t) aet + b based on the bridge technical condition evaluation and prediction result to obtain the numerical values of a, b, c and d and the related coefficient r value of each function, and performing linear fitting and related analysis by using tracking detection data for more than 3 years, the method comprises the steps of taking a function with the highest correlation coefficient as a bridge disease key index prediction mathematical model, wherein the bridge disease key index prediction mathematical model is established according to key indexes of a continuous steel bridge or a continuous cast-in-place box girder bridge, the key indexes comprise deformation, displacement, stress, strain and crack width, and the bridge disease key index prediction mathematical model is used for fitting and analyzing correlation of detection data at different times by establishing a one-to-one correspondence relationship between bridge constituent members and diseases generated by the bridge constituent members. In one embodiment, where w represents the fracture width or technical status score and t represents time in calendar days.

And step 3: and (3) establishing a bridge disease intelligent diagnosis unit, and performing disease diagnosis on the bridge and the component members thereof by using the bridge disease key index prediction mathematical model obtained in the step (2) to obtain the attributes, severity, evolution rules and disease reasons of the bridge and the component members thereof.

For example, a mathematical model is predicted based on the technical condition of the bridge and the evolution rule of the representative structural crack width, the evolution rule of the bridge along with the time is analyzed and predicted, and when the evaluation value of the technical condition of the bridge or the component reaches two or more types of standards, or the evaluation value of the technical condition of the bridge predicts the rapid deterioration of the bridge through the mathematical model; or the key indexes of the structural diseases are predicted to develop faster through a mathematical model, the system timely performs early warning, and lists the bridge component lists needing to be maintained so as to timely perform small maintenance or maintenance reinforcement on the bridge. Setting yellow early warning value, orange early warning value and red early warning value for strain and deflection, and respectively taking 0.8, 0.9 and 1.0 of the calculated values. According to the technical conditions of the bridge and the parts thereof, the classification, grading, diagnosis and maintenance types are evaluated:

one, normal maintenance is diagnosed;

second, minor repair is diagnosed;

third, diagnosis is medium repair;

fourthly, diagnosis is overhaul and reconstruction;

and five, diagnosis is dismantling reconstruction.

And 4, step 4: collecting various bridge diseases and maintenance success schemes with different degrees of severity at home and abroad, and establishing a maintenance scheme library suitable for various diseases and different degrees of severity of beam bridges and component members thereof according to the characteristics of the bridge diseases.

And 5: establishing an intelligent optimal decision-making unit, preferably selecting an optimal maintenance scheme of the bridge according to the prediction result of the evolution rule of key indexes of the special technical condition of the bridge and the diseases generated by the bridge, the current technical condition data of the bridge and the components thereof, the disease attribute, the severity and the diagnosis data of the disease reason determined by bridge disease diagnosis, the statistical analysis method and the machine learning algorithm of a computer, and the comparison result with a maintenance scheme library, analyzing and refining the optimal maintenance reinforcement scheme of the same kind of diseases through a large amount of application experience, forming a machine learning algorithm, generating training guidance, and providing a technical basis for the decision-making of the later-stage bridge maintenance scheme; by utilizing the annual maintenance scheme of the bridge and the analysis of the evolution rule of the technical condition, the annual maintenance plan and the five-year maintenance plan of the bridge managed by a certain unit can be formulated.

The present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium. Computer-usable storage media include, but are not limited to, disk storage, CD-ROM, optical storage, and the like, and the invention is described below in connection with two specific embodiments.

Example 1

The upper structure of a left side of a certain highway bridge is made of 3 multiplied by 20m prestressed reinforced concrete hollow slabs, each span is composed of 8 hollow slabs, the lower structure of the bridge is composed of a pile foundation, cylindrical piers and a gravity bridge abutment, a bridge deck pavement asphalt concrete pavement layer is formed, and reinforced concrete anti-collision guardrails are arranged on two sides of the bridge deck. According to the detection result: the main damage of the upper structure of the bridge is represented by concrete reinforcement rust expansion exposed ribs, support seat separation, shearing deformation and the like, and the main damage of bridge deck pavement is represented by separation of concrete in an anchoring area of an expansion joint and a road surface and rust expansion exposed ribs of reinforcement of an anti-collision guardrail. According to the invention, the bridge maintenance decision program is as follows:

firstly, the large asset data and the large technical condition data of the bridge structure are established, and the health monitoring of the bridge is not carried out due to the fact that the bridge structure belongs to a simply supported bridge, and the large health monitoring data do not need to be established.

(1) Bridge structure asset big data establishment

Left structure asset big data of a certain road bridge (Table 1)

Wherein, in table 1 above, ZJ represents pile foundation; ZJ0-1 represents

row

0, 1 st pile foundation; ZJ0-2 represents

row

0, 2 nd pile foundation; ZJ1-1 represents row 1, pile foundation 1; ZJ1-2 represents

row

1, 2 nd pile foundation; ZJ2-1 represents

row

2, 1 st pile foundation; ZJ2-2 represents

row

2, 2 nd pile foundation; ZJ3-1 represents row 3, pile foundation 1; ZJ3-2 represents

row

3, 2 nd pile foundation;

QT stands for abutment, QT0 stands for abutment No. 0; QT3 represents abutment No. 3;

QD stands for pier, QD1-1 for row 1, pier column 1; QD1-2 represents row 1, pier 2; QD2-1 represents row 2, pier column 1; QD2-2 represents row 2, pier column 2;

ZP represents a conical slope; ZP0 represents taper No. 0; ZP3 represents conus slope No. 3;

HP represents revetment; HP1 represents revetment No. 1; HP2 represents revetment No. 2;

HXL represents a transverse tie beam; HXL-1 represents a No. 1 transverse tie beam; HXL-2 represents a No. 2 transverse tie beam;

ML represents a cap beam; ML-1 represents the 1 st row of hat beams; ML-2 represents the row 2 cap beam; ML-3 represents the 3 rd row cap beam; ML-4 represents the 4 th row of cap beams;

ZZ represents a support, ZZ 1Q-1-16 represents the No. 1-16 support number of the 1 st span starting point; ZZ 2Q-1-16 represents No. 1-16 support base numbers of the No. 2 spanning starting points; ZZ 3Q-1-16 represents No. 1-16 support serial numbers of the No. 3 spanning starting point; ZZ3Z-1 ~ 16 represents No. 1 ~ 16 support serial numbers of 3 rd span terminal.

LB represents a beam slab, 1-8 are hollow slab numbers, and the hollow slabs are ordered from left to right;

1-LB 1-8 represents No. 1-8 hollow slabs of the No. 1 span; 2-LB 1-8 represents No. 1-8 hollow slabs of No. 2 span; 3-LB 1-8 represents No. 1-8 hollow slabs of the 3 rd span;

LQPZ represents asphalt concrete pavement; LQPZ-1 represents the 1 st span of asphalt concrete pavement; LQPZ-2 represents the 2 nd span asphalt concrete pavement; LQPZ-3 represents the 3 rd span asphalt concrete pavement;

FZQ-1-Z represents the 1 st cross left side anti-collision wall, and FZQ-1-Y represents the 1 st cross right side anti-collision wall;

FZQ-2-Z represents the No. 2 cross left side anti-collision wall, FZQ-2-Y represents the No. 2 cross right side anti-collision wall;

FZQ-3-Z represents the No. 3 cross left side anti-collision wall, FZQ-3-Y represents the No. 3 cross right side anti-collision wall;

SSF-1 represents the 1 st expansion joint; SSF-2 represents the 2 nd expansion joint.

(2) Big data establishment of bridge structure technical condition

Big data of left structure asset of certain road bridge-physical parameter and disease of pile foundation (Table 2)

Serial number	Component number	Length (m)	Cross section (cm)	Design strength (MPa)	Disease and disease
						1	ZJ0-1	26	φ110	20	Is free of
2	ZJ0-2	26	φ110	20	Is free of
						3	ZJ1-1	28	Φ110	20	Is free of
4	ZJ1-2	28	Φ110	20	Is free of
						5	ZJ2-1	26	Φ110	20	Is free of
6	ZJ2-2	26	φ110	20	Is free of
						7	ZJ3-1	26	Φ110	20	Is free of
8	ZJ3-2	26	φ110	20	Is free of

Big data of left structure assets of a certain road bridge-pier physical parameters and diseases (Table 3)

Big data of left structure asset of certain highway bridge-physical parameter and disease of horizontal tie beam (Table 4)

Serial number	Component number	Length (m)	Cross section (cm)	Design strength (MPa)	Disease and disease
						1	HXL-1	6	100×120	25	Is free of
2	HXL-2	6	100×120	25	Is free of

Big data of left structure asset of certain highway bridge-cap beam physical parameter and disease (Table 5)

Big data of a left structure asset of a certain highway bridge-ZZ 1Q1 No. 16 rubber support physical parameters and diseases (Table 6)

Serial number	Component number	Thickness (cm)	Cross section (cm)	Disease and disease
					1	ZZ1Q1	4	20×20	-
2	ZZ1Q2	4	20×20	-
					3	ZZ1Q3	4	20×20	Shear deformation
4	ZZ1Q4	4	20×20	-
					5	ZZ1Q5	4	20×20	-
6	ZZ1Q6	4	20×20	Transverse movement
					7	ZZ1Q7	4	20×20	Shear deformation, transverse movement
8	ZZ1Q8	4	20×20	-
					9	ZZ1Q9	4	20×20	Shear deformation and transverse movement
10	ZZ1Q10	4	20×20	Transverse movement
					11	ZZ1Q11	4	20×20	Shear deformation, transverse movement
12	ZZ1Q12	4	20×20	-
					13	ZZ1Q13	4	20×20	Transverse movement
14	ZZ1Q14	4	20×20	-
					15	ZZ1Q15	4	20×20	Transverse movement
16	ZZ1Q16	4	20×20	-

Big data of a left structure asset of a certain highway bridge-ZZ 2Q1 No. 16 rubber support physical parameters and diseases (Table 7)

Big data of a left structure asset of a certain road bridge-ZZ 3Q1 ~ 16 rubber support physical parameters and diseases (Table 8)

Big data of a left structure asset of a certain highway bridge-ZZ 3Z1 ~ 16 rubber support physical parameters and diseases (Table 9)

Big data of left structure assets of a certain highway bridge-No. 1-LB1 ~ 8 beam slab physical parameters and diseases (Table 10)

Left structure asset big data of a certain highway bridge-No. 2-LB1 ~ 8 beam slab physical parameters and diseases (Table 11)

Big data of left structure assets of a certain highway bridge-No. 3-LB1 ~ 8 beam slab physical parameters and diseases (Table 12)

Big data of left structure assets of a certain road bridge-LQPZ 1 ~ 3 physical parameters and diseases (Table 13)

Big data of left structure asset of a certain highway bridge-SSF 1 No. 2 physical parameters and diseases (Table 14)

Big data of left structure assets of a certain road bridge-FZQ 1 ~ 3 physical parameters and diseases (Table 15)

In the table 15, the thickness of the anti-collision wall is 20-30 cm, the width of 15cm at the upper part of the anti-collision wall is 20cm, and the width of the lower part of the anti-collision wall gradually expands from 20cm to 30cm under self-injury.

And secondly, evaluating the technical conditions of the bridges, namely evaluating the bridge members, the parts, the bridge deck system, the upper structure, the lower structure and the full bridge according to 3.1.1.1 highway bridge technical condition evaluation standards (JTGT H21-2011) in China, and evaluating the technical conditions of the highway bridges by adopting a method of combining hierarchical comprehensive evaluation and 5 types of bridge single control indexes, wherein the method comprises the steps of evaluating each member of the bridge, evaluating each part of the bridge, evaluating the bridge deck system, the upper structure and the lower structure respectively, and finally evaluating the overall technical conditions of the bridge. 3.2.3 the evaluation grades of the overall technical conditions of the bridge are divided into 1 class, 2 class, 3 class, 4 class and 5 class. 3.2.4 the evaluation scale of the technical condition of the main parts of the bridge is divided into 1 type, 2 type, 3 type, 4 type and 5 type. The invention is used for evaluating the technical conditions of the bridge and the component members thereof and classifying the technical conditions of the bridge and the component members thereof according to the bridge technical condition evaluation standard.

Technical status grade and technical status classification grade table (Table 16) of bridge and its component parts

Evaluation of left width of bridge for a certain road (Table 17)

Thirdly, disease diagnosis is carried out on the bridge and the component members thereof according to the technical condition of the bridge and the big data of the bridge, and the cause, the severity and the evolution rule of the disease are determined, which is shown in figure 25.

The bridge has the following diseases:

(1) upper bearing member concrete rusty exposed bar

(2) Support base movement

(3) Shear deformation of support

(4) Rust expansion of beam slab honeycomb and reinforcing steel bar

(5) Longitudinal concrete crack in expansion joint anchoring area

(6) Damage of rubber strip at expansion joint

(7) Expansion joint silt blockage

(8) Crack near the span of the 3# beam bottom plate, 3 transverse cracks appear, and after disease diagnosis, the 3 cracks are structural cracks, wherein the width of the 2 nd crack is out of limit, a polynomial function of w (t) ═ at3+ bt2+ ct + d, a logarithmic function of w (t) ═ alnt + b and an exponential function of w (t) ═ aet + b are adopted for fitting, and a crack width prediction mathematical model is established by utilizing 3-year tracking detection data (see table 18).

Table 18: crack width measured data unit (mm)

t	1	60	180	365	545	730	995
								w	0.1	0.11	0.13	0.15	0.18	0.21	0.24

The fitted function and correlation coefficient are as follows:

W＝0.0167lnt+0.0753R2＝0.5801 (1)

W＝0.1054e0.0009tR2＝0.9761 (2)

W＝6E(-11)t3+7E(-8)t2+0.0001t+0.1002R2＝0.9973 (3)

from the analysis of the function fitting effect, the polynomial function is optimal, the correlation coefficient of the polynomial function reaches 0.9973, the requirement of not less than 0.99 is met, and the polynomial function can be used as a crack width prediction mathematical model.

The cause of the disease is analyzed as follows:

(1) the rust-swelling and rib-exposing concrete is generally influenced by factors such as the pH value of a liquid phase of the concrete, the CL < - > content of the concrete, the compactness and the thickness of a protective layer of the concrete, the environmental conditions and the like.

(2) The support base moving reason is mainly that during construction and in the process of mounting the beam plate, the support base is driven when the position of the beam plate is locally adjusted, so that the support base deviates from the original position; the support has slippage and deviation under the action of factors such as temperature change, impact of vehicle load and the like.

(3) The shear deformation reasons of the support are mainly as follows: the expansion caused by heat and the contraction caused by cold drive the bridge to expand and contract, and the support is driven to shear and deform; during operation, the support shear deformation is caused by temperature change, concrete shrinkage and creep and the action of automobile braking force; the support has the quality problem, and the shearing deformation under the load action is overlarge due to the fact that the shearing-resisting elastic die does not meet the requirement; the longitudinal shear deformation is overlarge due to the overlarge design of the longitudinal slope of the bridge.

(4) The beam slab bottom plate honeycomb is mainly characterized in that concrete has poor fluidity or cannot be vibrated and compacted.

(5) The main reason for the expansion joint anchoring concrete crack is the shrinkage crack appearing in the later period due to the insufficient curing time of the anchoring concrete; the expansion joint device bears the repeated impact action of a vehicle running at high speed, and the concrete in the anchoring area is easy to damage and crack.

(6) The expansion joint rubber strip is damaged: the rubber strip is easy to age and crack under severe environment, and the service performance is reduced.

(7) Silt blockage of the expansion joint: the silt on the bridge floor or the road surface flows into the expansion joint and is not cleaned in time.

(8)3 transverse cracks appear near the span of the first span 3# beam bottom plate, and all the cracks are structural cracks through disease diagnosis. The main reason is that the plate is positioned right below the track line of the road wheel, and the stress of the plate is relatively large, so that the bottom plate is cracked; from the analysis of the width of 3 cracks, the crack in the midspan is the widest, and the width of the cracks on two sides is slightly smaller, which indicates that the cracks have a development trend and can cause the reduction of the bearing capacity of the bridge.

Severity and evolution law: most of diseases of the bridge belong to the initial development stage, the stress and safety influence on the bridge structure is small, and the bridge can be maintained, maintained and monitored in time in the daily management process; but 3 structural cracks appear near the span of the first 3# beam bottom plate span and show a development trend, and the width of the 2 nd structural crack reaches 0.24mm and is out of limit, so that the bearing capacity of the bridge is influenced to a certain extent, and timely maintenance and reinforcement are recommended.

And fourthly, establishing a maintenance scheme library of the bridges and the component members thereof by using successful experiences of maintenance and repair of the bridges and the component members thereof at home and abroad for reference.

Aiming at the defects of concrete surface layer defects (pitted surfaces, honeycombs, cavities, peeling and exposed ribs), concrete reinforcement rust expansion exposed ribs, insufficient thickness of a concrete protective layer and the like, the following reinforcement measures are adopted, and the measures are suitable for all structural bridges.

(1) Epoxy mortar repairing method

The epoxy mortar repairing method mainly comprises an artificial surface sealing coating repairing method and a pouring coating repairing method. The artificial surface sealing and repairing method is mainly used for small-area damages of weathering, peeling, exposed ribs and the like on the surface of a concrete structure.

(2) Cement mortar repairing method

The cement mortar repairing method comprises a cement mortar manual smearing method and a cement mortar spraying repairing method. The manual cement paste smearing method is mainly applied to defects of small areas, particularly defects of shallow damage depth. The guniting repair method is mainly applied to repair of large-area defects on the surface of concrete and repair of important concrete structures.

(3) Concrete repairing method

Concrete repair methods mainly include a direct pouring method, a spraying method, a grouting method, and the like. The method is mainly applied to defects of honeycombs, cavities, larger-range damage and the like in a concrete bridge structure, and can be generally repaired by adopting the concrete with good gradation.

The support is moved in a string mode, and the main beam can be lifted to be reset or directly replaced.

For support shear deformation, continuous monitoring or direct replacement of the jacking main beam can be adopted.

And (3) aiming at the expansion joint anchoring concrete crack, epoxy resin can be adopted for crack pouring in a slight degree, and if the crack develops to concrete crack, the concrete is chiseled off, and new concrete is newly constructed for maintenance and repair.

The rubber strip needs to be replaced in time aiming at the damage of the rubber strip.

Aiming at the expansion joint silt blockage, the cleaning work of the expansion joint is well done in the future maintenance, and the V-shaped groove is ensured not to be blocked by silt.

Aiming at the first 3 structural cracks of the No. 3 beam bottom plate, the bearing capacity of the bridge is influenced, and the bridge is required to be maintained and reinforced in time. According to successful maintenance experience of transverse structural cracks of bottom plates of hollow slab beams at home and abroad, 3 schemes of adhering carbon fiber plates to the bottom plates, adhering steel plates to the bottom plates, anchoring and spraying reinforced concrete to the bottom plates and the like are mainly adopted in the reinforcing scheme.

Fifthly, utilizing the bridge disease diagnosis result, and utilizing the method to automatically and preferably select the optimal maintenance scheme from the maintenance scheme library.

(1) And the exposed reinforcement of the reinforced concrete of the main beam is repaired by adopting epoxy mortar.

(2) And the main beam is jacked to reset aiming at the support vibration.

(3) And continuous monitoring or replacement is adopted for the shearing deformation of the support.

(4) And (4) performing maintenance and repair by adopting epoxy resin for pouring or chiseling reworking for the expansion joint anchoring concrete crack.

(5) And replacing the rubber strip aiming at the damage of the rubber strip.

(6) And (4) carrying out silt blockage on the expansion joint, and cleaning the expansion joint to ensure that no silt is blocked in the V-shaped groove.

(7) According to successful maintenance experience of transverse structural cracks of bottom plates of hollow slab beams at home and abroad, 3 modes such as bottom plate adhesion carbon fiber plates, bottom plate anchor spraying reinforced concrete, bottom plate adhesion steel plates and the like are mainly adopted for reinforcement, and a bottom plate adhesion carbon fiber reinforcement maintenance scheme is preferably determined through software of the patent. The practicality of the scheme is analyzed, the middle-span crack of the plate is widest and exceeds the limit, the width of the cracks on two sides is smaller, the bearing capacity of the plate is reduced, the plate gradually deteriorates, the carbon fiber plate is pasted on the full section of the bottom plate, the bearing capacity and the rigidity of the plate are improved, and the scheme has the best reinforcing effect.

Example 2

In the right part of a high-speed bridge, the upper structure adopts a (40+60+60+40) m variable cross-section continuous cast-in-place box girder, the lower structure of the bridge consists of a pile foundation, piers and a gravity type bridge abutment, a bridge deck pavement asphalt concrete pavement layer is formed, and concrete anti-collision guardrails are arranged on two sides of the bridge deck.

And (4) detecting according to the result: the main defects of the upper structure of the bridge are represented by longitudinal cracks of a top plate of a first span main beam, concrete stripping and exposed ribs, and vertical cracks of piers; according to the invention, the bridge maintenance decision program is as follows:

step 1: and establishing the big data of the bridge structure assets, the big data of the technical conditions and the big data of the health monitoring.

(1) Bridge structure asset big data establishment

Right structure asset big data of a high speed bridge (Table 19)

Wherein, in table 19 above, ZJ represents a pile foundation; ZJ0-1 represents row 0, 1 st pile foundation; ZJ0-2 stands for row 0, 2 nd pile foundation; ZJ0-3 represents row 0, root 3 pile foundation; ZJ0-4 represents row 0, 4 th pile foundation; ZJ1-1 represents row 1, pile foundation 1; ZJ1-2 stands for row 1, 2 nd pile foundation; ZJ1-3 represents row 1, 3 rd pile foundation; ZJ1-4 represents row 1, 4 th pile foundation; ZJ2-1 represents row 2, 1 st pile foundation; ZJ2-2 stands for row 2, 2 nd pile foundation; ZJ2-3 represents row 2, 3 rd pile foundation; ZJ2-4 represents row 2, 4 th pile foundation; ZJ3-1 represents row 3, pile foundation 1; ZJ3-2 represents row 3, 2 nd pile foundation; ZJ3-3 represents row 3, root 3 pile foundation; ZJ3-4 represents row 3, 4 th pile foundation; ZJ4-1 represents row 4, pile foundation 1; ZJ4-2 represents row 4, 2 nd pile foundation; ZJ4-3 represents row 4, pile foundation 3; ZJ4-4 represents row 4, pile foundation 4;

QT stands for abutment, QT0 stands for abutment No. 0; QT4 represents abutment No. 4;

QD represents pier, QD1 represents row 1 pier; QD2 represents pier row 2; QD3 represents pier row 3;

ZP1-Z represents the left side of the No. 1 conoid slope; ZP1-Y represents the right side of No. 1 conoid slope; ZP2-Z represents the left side of No. 2 conoid slope; ZP2-Y represents the right side of No. 2 conoid slope;

HP1 represents revetment No. 1; HP2 represents revetment No. 2;

ML stands for hat beam; ML-1 represents the 1 st row of cap beams; ML-2 represents the row 2 cap beam; ML-3 represents the 3 rd row cap beam; ML-4 represents the 4 th row of cap beams; ML-5 represents the 5 th row cap beam;

ZZ represents a support, ZZ 1Q-1-2 represents the No. 1-2 support serial number of the No. 1 span starting point; ZZ 2Q-1-2 represents No. 1-2 support base numbers of the No. 2 spanning starting points; ZZ 3Q-1-2 represents No. 1-2 support serial numbers of the No. 3 span starting point; ZZ 4Q-1-2 represents the No. 1-2 support serial number of the No. 4 span starting point; ZZ 4Z-1-2 represents the No. 1-2 support serial number of the No. 4 span terminal.

The 1-0# -7 # block represents a No. 1 span No. 0-7 box girder segment;

1-HLD represents the 1 st span box girder closure segment; 1-ZXD represents the 1 st span box girder straight line segment;

the 2-0# -7 # blocks represent No. 2 box girder segments spanning from the 1 row of piers to the direction of the closure section from 0 to 7;

the blocks 2-0 '# -7' #representNo. 0 '-7' box girder segments from the No. 2 spanning row of piers to the direction side of the folding section;

2-HLD represents a 2 nd span box girder closure section;

3-0# -7 # blocks represent No. 0-7 box girder segments from No. 3 spanning row 2 piers to the direction of the closure segment;

the 3-0 '# -7' # blocks represent the No. 0 '-7' box girder segment from the 3 rd row of piers to the direction of the closure segment;

3-HLD represents a 3 rd span box girder closure section;

4-0# to 7# blocks represent No. 4 span 0 to 7 box girder segments;

4-ZXD represents the 4 th span box girder straight line segment;

4-HLD represents a 4 th span box girder closure section;

LQPZ represents asphalt concrete pavement; LQPZ-1 represents the pavement of the asphalt concrete of the 1 st span; LQPZ-2 represents the 2 nd span asphalt concrete pavement; LQPZ-3 represents the 3 rd span asphalt concrete pavement; LQPZ-4 represents the 4 th span asphalt concrete pavement;

FZQ-4-Z represents the 4 th cross left side anti-collision wall, and FZQ-4-Y represents the 4 th cross right side anti-collision wall;

(2) Big data establishment of bridge technical condition

High-speed bridge structure asset big data-pile foundation physical parameter and disease (Table 21)

Big data of right structure asset of a high-speed bridge-physical parameters and diseases of pier (Table 22)

Right structure asset big data of a high speed bridge-horizontal tie beam physical parameter and disease (Table 23)

Serial number	Component number	Length (m)	Cross section (cm)	Design strength (MPa)	Disease and disease
						1	HXL-1	6	100×120	30	Is free of
2	HXL-2	-	-	30	Is free of
						3	HXL-3	-	-	30	Is free of

Assets big data of right structure of a high-speed bridge-cap beam physical parameters and diseases (Table 24)

Serial number	Component number	Length (m)	Cross section (cm)	Design strength (MPa)	Disease or illness
						1	ML-1	12	800×150	30	-
2	ML-2	12	800×150	30	-
						3	ML-3	12	800×150	30	-
4	ML-4	12	800×150	30	-
						5	ML-5	12	800×150	30	-

Big data of a high-speed bridge right structure asset-ZZ 1Q1 ~ 2 basin type support physical parameters and diseases (Table 25)

Serial number	Component number	Cross section (mm)	Thickness (mm)	Disease and disease
					1	ZZ1Q-1	280×235	80	Slight corrosion of the steel support
2	ZZ1Q-2	280×235	80	Slight corrosion of the steel support

Physical parameters and diseases of basin type support of certain high-speed bridge right-amplitude structure asset big data-ZZ 2Q1 ~ 2 (Table 26)

Serial number	Component number	Cross section (mm)	Thickness of	Disease or illness
					1	ZZ2Q-1	280×235	80	-
2	ZZ2Q-2	280×235	80	-

Right structure asset big data of a high speed bridge-ZZ 3Q1 ~ 2 basin type support physical parameters and diseases (Table 27)

Serial number	Component number	Cross section (mm)	Thickness (mm)	Disease and disease
					1	ZZ3Q-1	280×235	80	-
2	ZZ3Q-2	280×235	80	-

Big data of a high-speed bridge right structure asset-ZZ 4Q1 ~ 2 basin type support physical parameters and diseases (Table 28)

Serial number	Component number	Cross section (mm)	Thickness (mm)	Disease and disease
					1	ZZ4Q-1	280×235	80	-
2	ZZ4Q-2	280×235	80	-

Big data of a high-speed bridge right structure asset-ZZ 4Z1 ~ 2 basin type support physical parameters and diseases (Table 29)

Serial number	Component number	Cross section (mm)	Thickness (mm)	Disease or illness
					1	ZZ4Z-1	280×235	80	-
2	ZZ4Z-2	280×235	80	-

Right structure asset data of a high speed bridge-1 st span main beam physical parameter and disease (Table 30)

Right structure asset big data of a high speed bridge-2 nd span main beam physical parameter and disease (Table 31)

Right structure asset big data of a high speed bridge-3 rd span main beam physical parameter and disease (Table 32)

Right structure asset data of a high speed bridge-4 th span main beam physical parameter and disease (Table 33)

Big data-LQPZ 1 ~ 4 physical parameters and diseases of high speed bridge right structure (Table 34)

Big data of a high-speed bridge right-width structure asset-SSF 1 No. 2 physical parameters and diseases (Table 35)

Right structure asset big data-FZQ 1 to No. 4 physical parameter and disease of a high speed bridge (Table 36)

(3) Bridge health monitoring big data establishment

According to technical regulations of safety monitoring systems of highway bridge structures (JT/T1037-. 5.2.3 structural partial response monitor content selection meets the following specifications: a) monitoring the strain of the key component; the stress and deflection (deformation) of the bridge are monitored in real time, and the natural frequency is monitored periodically.

(1) The arrangement of the bridge strain measuring points is shown in figures 4 and 5

The bridge main span is arranged according to 4 equal points and the pier top section, the side span is arranged on the span middle section and the pier top section, strain sensors are arranged on 7 sections in total, the sensors are arranged on the top and the bottom plate of the box girder, the total number of the sensors is 28, and the arrangement schematic diagram of the strain monitoring sensors is shown as follows.

(2) The measuring point arrangement of the girder deflection monitor is shown in figures 6 and 7

7 deflection monitors are arranged on the two main spans, 1 deflection monitor is arranged in each side span, and the schematic layout diagram is shown as follows.

(3) The arrangement of the main beam natural vibration frequency measuring points is shown in figures 8 and 9

The self-vibration frequency of the girder is directly related to the structural rigidity, and the reliability is high by utilizing the self-vibration frequency to evaluate the rigidity of the bridge. When a structural member is defective, the natural frequency of vibration is generally lowered. The natural vibration frequency of the right box girder is regularly monitored, and data acquisition is carried out by using a DH5907N wireless vibration sensor according to the frequency of 1 time/month. Points C1, C2, C3, C4, C5 are located near the midspan and the impact wall.

(4) Early warning value setting

A bridge structure calculation model is built by adopting structure calculation software, the total number of the bridge structure calculation model is 77 nodes and 66 units, the automobile live load is of a grade of 20 according to a design drawing, the bridge structure calculation model is shown in figure 10, the structure fundamental frequency early warning value is 1.41Hz, and the strain and deflection early warning values are as follows.

And setting yellow early warning values, orange early warning values and red early warning values for the strain and the deflection, and respectively taking 0.8, 0.9 and 1.0 of calculated values (of envelope values). The early warning values of each measuring point of the strain and the deflection are shown in the following table. And (4) adopting yellow and red secondary early warning for monitoring items except for strain and deflection. a) Yellow early warning is carried out, the bridge management and maintenance unit is reminded to respond to the environment, load and whole or local structure to strengthen attention, and tracking observation is carried out; b) orange early warning is carried out, the bridge management and maintenance unit is warned to continuously and closely pay attention to the environment and the whole or local response of the structure, and the reason of warning is found out; c) red early warning, namely performing safety calculation and evaluation on the whole and local response of the structure, and adopting appropriate inspection and emergency management measures to ensure the safe operation of the bridge structure;

each strain measurement point monitoring and early warning value (unit: mu epsilon) (table q)

Each deflection measuring point monitoring early warning value (unit: mm) (meter r)

Monitoring cross section	Sensor numbering	Theoretical calculation value	Yellow early warning	Orange early warning	Red early warning
						1	N1	10.5	8.4	9.45	10.5
2	N2	11.5	9.2	10.35	11.5
						3	N3	21.7	17.36	19.53	21.7
4	N4	19.3	15.44	17.37	19.3
						5	N5	12.4	9.92	11.16	12.4
6	N6	12.4	9.92	11.16	12.4
						7	N7	21.7	17.36	19.53	21.7
8	N8	11.5	9.2	10.35	11.5
						9	N9	10.5	8.4	9.45	10.5

(5) Monitoring results

Three-month monitoring data are extracted to evaluate the stress condition of the bridge:

(1) according to the strain monitoring time-course curves of all the span control sections of the bridge, as can be seen from fig. 11 to 17, the strain monitoring time-course curve of the 1 st span strain control section YB1 has an alarm condition, the strain values of the other span control sections do not exceed the theoretical calculated value, the change trend of the strain monitoring time-course curve is stable, the alarm value exceeds the early warning value, but the amplitude is small, the searched video monitoring system monitors 24 minutes at 10 am on 4/17/2020, and 2 heavy trucks pass through the bridge; 6 heavy goods vehicles pass through the bridge densely at 40 minutes 11 am, 6 months, 19 days in 2020.

(2) According to the bridge deflection monitoring time-course curve, as shown in fig. 18-21, the change amplitude of each span control section deflection monitoring time-course curve is small, and the phenomenon of continuous rising or continuous falling does not occur, which indicates that the bridge deflection monitoring value is normal.

(3) According to the test results of the natural vibration frequency of the right box girder of the bridge, as shown in fig. 22, 23 and 24, the three-time monitored natural vibration frequency of the bridge is 1.563Hz, the natural vibration frequency is greater than a theoretical calculated value (the theoretical calculated value is 1.41Hz), and the natural vibration frequency does not decrease, which indicates that the bridge is not seriously damaged structurally.

Step 2:

bridge technical condition evaluation: according to the 3.1.1 highway bridge technical condition assessment in the 'highway bridge technical condition assessment Standard' (JTGT H21-2011) in China, the assessment comprises the assessment of bridge members, parts, a bridge deck system, an upper structure, a lower structure and a full bridge, the assessment of the highway bridge technical condition adopts a method of combining hierarchical comprehensive assessment and 5 types of bridge single control indexes, the assessment is firstly carried out on each member of the bridge, then the assessment is carried out on each part of the bridge, then the assessment is carried out on the bridge deck system, the upper structure and the lower structure respectively, and finally the assessment of the overall technical condition of the bridge is carried out. 3.2.3 the evaluation grades of the overall technical conditions of the bridge are divided into 1 class, 2 class, 3 class, 4 class and 5 class. 3.2.4 the evaluation scale of the technical condition of the main parts of the bridge is divided into 1 type, 2 type, 3 type, 4 type and 5 type. And evaluating the technical conditions of the bridge and the component members thereof, and classifying the technical conditions of the bridge and the component members thereof according to the bridge technical condition evaluation standard.

Evaluation result of right width of high speed bridge (table s)

Bridge health monitoring data evaluation:

(1) the change trend of the time-course curve of bridge strain monitoring is stable, except the alarm condition of the 1 st span strain control section YB1 of the main bridge, the strain values of the rest span control sections do not exceed the theoretical calculation value, the alarm value exceeds the early warning value, the amplitude is small, and the strain monitoring is normal. When the video monitoring is checked and the early warning value is found to be over, heavy trucks pass through the bridge densely.

(2) The change trend of the bridge deflection monitoring time-course curve is stable, the on-line monitoring system does not give an alarm for the deflection of each span control section of the bridge in the monitoring time period, and the deflection monitoring value is normal.

(3) The test values of the self-vibration frequency of the bridge are all 1.563Hz, which is larger than the theoretical calculation value, and the vibration frequency is normal.

And step 3: and diagnosing the bridge and the component members thereof according to the bridge technical condition and the bridge health monitoring big data, and determining the cause, the severity and the evolution rule of the diseases.

The bridge has the following diseases:

(1) longitudinal crack of girder top plate

(2) Main beam concrete stripping exposed rib

(3) Slight corrosion of the steel basin support

(4) Vertical crack of bridge pier

The cause of the disease is analyzed as follows:

(1) longitudinal cracks of the girder top plate: under the action of the wheel, transverse bending moment stress is generated, and when transverse reinforcing bars are insufficient, longitudinal cracks are formed.

(2) Stripping main beam concrete and exposing ribs: generally due to construction and environmental factors.

(3) Slight corrosion of the steel basin support: generally, the steel basin support is corroded due to the influence of factors such as the environment condition.

(4) Vertical crack of pier: the concrete is subjected to shrinkage deformation to initiate cracks; the corrosive environment causes the bridge steel bars to corrode and crack.

Severity and evolution law:

the crack diseases of the bridge are temperature shrinkage cracks, which belong to non-structural cracks and develop slowly, but have certain influence on the durability of the bridge structure; the damage of the anti-collision wall needs to cause the attention of a maintenance department; and repairing and maintaining other diseases.

And 4, step 4: and establishing a maintenance scheme library of the bridges and the component members thereof by using successful experiences of maintenance and repair of the bridges and the component members thereof at home and abroad for reference.

1. For non-structural cracks, preventive maintenance is suggested as a reinforcement strategy that is applicable to all structural style bridges.

(1) Surface repairing method (sealing seam)

The surface repair method is the most commonly used crack treatment method, and the method only aims at repairing some superficial cracks on the surface. The method is suitable for shallow and fine cracks with crack width less than or equal to 0.15 mm.

(2) Crack pouring

The grouting method for repairing concrete cracks is a widely applied technology in bridge maintenance at home and abroad. The adhesive mainly comprises epoxy resin, set cement and cement, and is reasonably selected according to the width of a crack. The grouting and repairing cracks are characterized in that internal tissues of the structure are combined into a whole again by the adhesive force of the adhesive to recover the required strength, and air and moisture are blocked from entering the beam body to avoid the corrosion of the steel bars. The method is suitable for shallow and fine cracks with the crack width larger than 0.15 mm.

(3) Method for pasting carbon fiber

The method for sticking the carbon fiber effectively sticks the crack to the surface of the concrete by using the bonding material such as epoxy resin and the like to effectively bond the crack with the original component after the crack is grouted, so as to effectively seal the crack on the surface of the concrete and restrain the generation and the expansion of the crack of the concrete structure. The method of adhering carbon fiber can be used for controlling the development of crack diseases of concrete members and the sealing of the existing cracks, and can also improve the bending resistance bearing capacity, the shearing resistance bearing capacity and the axial tensile bearing capacity of the tension member of the members, and improve the rigidity, the ductility and the like of the members. The method is suitable for reinforcing the structural member with dense non-structural cracks and reinforcing the structural cracks in the tension area of the bridge member so as to improve the bearing capacity and the durability of the bridge.

2. Aiming at the defects of concrete surface layer defects (pitted surfaces, honeycombs, cavities, peeling and exposed ribs), concrete reinforcement rust expansion exposed ribs, insufficient thickness of a concrete protective layer and the like, the following reinforcement measures are adopted, and the measures are suitable for all structural bridges.

(1) Epoxy mortar repairing method

(2) Cement mortar repairing method

(3) Concrete repairing method

3. And aiming at slight corrosion of the support, the support can be treated by coating antirust paint again after being derusted by abrasive paper.

And 5: and (4) optimizing the optimal maintenance scheme from the maintenance scheme library by using the bridge disease diagnosis result in the step (3).

1. And adopting a seam sealing maintenance mode aiming at the main beam cracks and the pier cracks.

2. And (3) repairing the concrete surface layer diseases by adopting an epoxy mortar mode.

3. And aiming at slight corrosion of the support, the rust-proof paint is recoated after sand paper is adopted for rust removal.

According to the bridge maintenance digital construction method, a new intelligent maintenance scheme optimization decision-making technology is provided by utilizing bridge technical condition evaluation and prediction results and bridge disease diagnosis results, a new bridge maintenance digital construction method is provided, and favorable conditions are created for bridge technical condition evaluation and prediction, disease diagnosis and maintenance scheme optimization decision. The intelligent disease diagnosis unit analyzes and determines the attribute, the severity, the disease reason and the evolution rule of the bridge disease by utilizing the existing technical condition of the bridge and the disease key technical index evolution mathematical model on the basis of establishing the one-to-one corresponding relation between the bridge component and the disease generated by the bridge component; the maintenance scheme intelligent optimization decision unit intelligently optimizes the bridge optimal maintenance scheme from the maintenance scheme library of a certain kind of diseases by the computer, and can provide a basis for the annual maintenance plan of the bridge or the five-year maintenance planning compilation of the bridge for all bridges managed by a certain unit. By applying the method, the intelligent decision of the beam type bridge maintenance scheme in a highway traffic system can be solved, and reliable and effective guarantee is provided for realizing long-term normal operation of hundreds of thousands of seat beam type bridges.

Those skilled in the art will appreciate that the drawings are merely schematic representations of preferred embodiments and that the blocks or flowchart illustrations are not necessary to practice the present application. Those skilled in the art can understand that the modules in the device in the implementation scenario may be distributed in the device in the implementation scenario according to the implementation scenario description, and may also be located in one or more devices different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.

The above application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be considered by those skilled in the art are intended to fall within the scope of the present application.

Claims

1. An intelligent maintenance decision-making method based on bridge big data, wherein the bridge big data is the integration of various data of a plurality of bridges, and the method is characterized by comprising the following steps:

systematically numbering all the components of the bridge, and giving each component a unique identity mark which is used as an anchoring carrier for disease and technical condition big data of the component; the big bridge technical condition data comprise all technical parameters reflecting the technical conditions of the bridge and detection data of the faults of the components;

a serial number is given to each disease generated by the component, and a one-to-one corresponding relation between the component and the disease generated by the component is established;

collecting monitoring data, including real-time monitoring data of displacement, deformation, deflection, strain and crack width of a key structure part of the continuous steel bridge or/and the continuous cast-in-place box girder bridge corresponding to the bridge health monitoring big data through real-time monitoring equipment, and collecting temperature and humidity environment monitoring data, traffic data obtained by video monitoring and video data of vehicle type characteristics;

2. A maintenance intelligent decision method according to claim 1, characterized in that: the real-time monitoring equipment comprises a sensor and/or a video monitoring system, the sensor comprises a displacement sensor, a deformation sensor, a deflection sensor, a strain sensor, a crack width sensor, a temperature sensor and a humidity sensor which are arranged at the designated position of the beam type bridge, and data acquired by the sensor are uploaded to a cloud platform in a wireless transmission mode to be stored.

3. A maintenance intelligence decision-making method according to claim 1, characterized by: the highway beam type bridge comprises a common reinforced concrete beam type bridge, a prestressed reinforced concrete beam type bridge, a continuous steel structure bridge and a continuous cast-in-place box girder.

4. A maintenance intelligence decision-making method according to claim 1, characterized by: the bridge component comprises a prestressed concrete structure, a rectangular plate of a non-prestressed concrete structure, a T beam, a hollow plate beam, an I-shaped beam and a box beam, wherein the T beam comprises a web plate, a diaphragm plate and a wing plate.

5. A maintenance intelligence decision-making method according to claim 1, characterized by: the crack data in the bridge technical condition big data comprise key parameters of cracks generated in the bridge member, and the key parameters comprise data of the length, the width, the position and the direction of the cracks.

6. A maintenance intelligence decision-making method according to claim 1, characterized by: the collecting monitoring data comprises:

7. A maintenance intelligence decision-making method according to claim 1, characterized by: the method for obtaining the bridge disease key index prediction mathematical model comprises the following steps: and performing curve fitting on the detection and/or monitoring data acquired by each key index at different time, taking the time as an abscissa and the detection or monitoring data as an ordinate, performing curve fitting by adopting different functions, and selecting the function with the highest correlation coefficient as a certain key index prediction mathematical model of the bridge for analyzing the development trend prediction of each key index and analyzing and evaluating the disease severity.

8. The utility model provides a maintenance intelligence decision-making system based on bridge big data which characterized in that includes:

the association module is used for granting a serial number to each fault of the member and establishing a one-to-one correspondence relationship between the member and the fault;