CN116258399A - Cable-stayed bridge safety assessment method based on multisource information-fuzzy analytic hierarchy process - Google Patents
Cable-stayed bridge safety assessment method based on multisource information-fuzzy analytic hierarchy process Download PDFInfo
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Abstract
The invention relates to a cable-stayed bridge safety assessment method based on multisource information-fuzzy analytic hierarchy process, which comprises the following steps: step S1: the method comprises the steps of merging multi-source disease data in daily inspection, regular detection and health monitoring of bridges, constructing a bridge disease database, and converting diseases into technical condition scores; step S2: based on the disease technical condition score, sequentially carrying out technical condition assessment on the components, the component population and the upper layer index, and finally obtaining the bridge population technical condition score; step S3: and providing a bridge maintenance operation strategy based on the technical condition scores of each part of the bridge. Compared with the prior art, the method introduces a multisource information fusion and fuzzy comprehensive evaluation technology, fuses massive multistairs multisource data generated in the maintenance operation of the cable-stayed bridge, establishes a cable-stayed bridge comprehensive evaluation model, can fully utilize bridge operation period data, and converts bridge disease data into specific grades and scores by considering uncertainty in reality, thereby providing decision support for maintenance work.
Description
Technical Field
The invention relates to the field of bridge safety, in particular to a cable-stayed bridge safety assessment method based on multisource information-fuzzy hierarchical analysis.
Background
China is the country with the largest number of cable-stayed bridges in the world. By 12 months in 2017, 78 of the whole world established cable-stayed bridges with the main span of 400 m are provided in China, and more than 80% of the cable-stayed bridges with the main span of 400 m are established and established in China. The cable-stayed bridge needs to be subjected to scientific and reasonable maintenance and evaluation work after being built, so that the maintenance and comprehensive evaluation of the cable-stayed bridge with a large span have huge market demands. However, for the evaluation work of the cable-stayed bridge, the existing specifications still have limitations, and the key mechanical indexes related to the structural safety performance and the operation environment indexes of the bridge applicability are lacking, so that the actual working state of the cable-stayed bridge cannot be scientifically and objectively evaluated. On the other hand, the standardized and health monitoring system of the daily inspection work of the bridge provides more data sources for the evaluation work, and the conventional evaluation method cannot effectively fuse multi-source data generated in the bridge operation process, so that the data is wasted.
The performance evaluation of the cable-stayed bridge still has more problems in practical engineering practice. For example, multi-platform data generated in the bridge operation period cannot be effectively utilized, important indexes related to the safety and applicability of the cable-stayed bridge structure are lacked, and a reasonable evaluation index system needs to be established to fuse the multi-platform data and determine corresponding index weights. In addition, aiming at the subjectivity problem possibly existing in the periodic detection of bridge inspection, the fuzzy analytic hierarchy process can better consider the ambiguity problem in engineering practice, and introduces uncertainty for evaluation work. And finally, the result of the bridge comprehensive evaluation model can be directly applied to bridge maintenance work, and a direct relation between bridge scores and maintenance work is established. The cable-stayed bridge performance evaluation method based on the work can directly obtain maintenance suggestions through component scores and component overall scores, and provides support for maintenance decisions.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a cable-stayed bridge safety evaluation method based on multi-source information-fuzzy analytic hierarchy process.
The aim of the invention can be achieved by the following technical scheme:
a cable-stayed bridge safety assessment method based on multisource information-fuzzy analytic hierarchy process comprises the following steps:
step S1: collecting disease data in a bridge daily inspection, periodic detection report and health monitoring system, extracting disease positions, disease descriptions and disease severity, constructing a bridge member disease database, and converting the disease data into grades and technical condition scores;
step S2: based on the technical condition scores of the bottom layer diseases of each component, sequentially carrying out technical condition assessment on the components, the parts and the upper layer indexes to obtain the scores of each part and the technical condition grades, and finally obtaining the total technical condition scores of the bridge;
step S3: based on the technical condition scores of all parts of the bridge, aiming at the component layer and the component overall score, a strategy between the technical condition score and a bridge maintenance operation strategy is formulated, so that accurate maintenance is realized.
The step S1 specifically includes:
step S11: collecting disease data in daily inspection and bridge periodic detection reports in a target time period, recording disease positions and disease descriptions, and inputting the disease positions and the disease descriptions into a component disease database;
step S12: collecting data of a health monitoring system (for collecting system facilities arranged on most cable-stayed bridges at present, distributing the data during bridge construction or supplementing the data during bridge operation) in a target time period, counting the proportion of abnormal points in each monitoring index by setting upper and lower thresholds, and recording the proportion in a corresponding component disease database;
step S13: and selecting the latest disease data of each component in the component disease database, converting the latest disease data into a deduction value DP according to the bottom disease scoring rule, and taking the deduction value DP as basic data for subsequent evaluation.
The step S13 specifically includes:
step S131: the qualitative indexes in the bottom layer diseases of all the components are selected, the disease grade is determined according to the severity of the qualitative diseases, the grade can be divided into 1-5 classes, 1 class represents perfect, and 5 classes represent serious diseases;
step S132: selecting quantitative indexes in bottom layer diseases of each component, determining corresponding disease grades according to the value range of the quantitative indexes, wherein class 1 represents perfect, and class 5 represents severe diseases;
step S133: selecting health monitoring indexes, determining corresponding disease grades according to the proportion of abnormal points, wherein class 1 represents perfect, and class 5 represents serious diseases;
step S134: and determining the bottom disease deduction value DP according to the grade of each bottom disease. Grades 1-5 correspond to points DP of 0, 15, 35, 60, 100, respectively.
The step S2 specifically includes:
step S21: calculating a single structure according to the disease deduction value DP of each bottom layer of the componentStatus score GCI of a part i ：
When x=1
U 1 ＝DP i1
When x is greater than or equal to 2
When DP ij When=100
GCI il ＝0
Wherein: GCI i A score representing the ith component, the value range being 0 to 100 points; k represents the number of types of underlying diseases in which the ith component is buckled; u, x, y represent introduced variables; j represents the jth type underlying defect of the ith component; DP (DP) ij A catch value representing a jth type underlying defect of the ith component;
step S22: based on the scores GCI of all types of members i Calculating a technical condition score for the population of components based on an optimistic coefficient weight analysis:
wherein: SCI (SCI) i The score of the class i component of the cable-stayed bridge is expressed, and the value range is 0-100 points; n represents the total number of components that the i-th class of components generally comprises; w (W) k A weight of a kth member representing an ensemble of ith class members; GCI ik Representing class i building blocksScore of the kth member of the population; w (w) k The initial weight of the kth component of the total of the ith component is expressed, and 1/n is taken; s is S k A state variable weight parameter representing a kth component of the ith class of components overall; α represents an optimistic coefficient;
an arithmetic average of all component scores representing the class i component population;
step S23: and according to the technical condition score of the whole member, sequentially calculating the technical condition score of the upper-layer index by combining the evaluation index system and the weight, and finally obtaining the technical condition score and the grade of the whole bridge.
The step S23 specifically includes:
step S231: for each upper-layer index, combining with an evaluation index system, respectively determining the lower-layer index of the index to obtain diversity P= { P 1 ,p 2 ,…,p n And the weight set w= { w corresponding to each index 1 ,w 2 ,…,w n }；
Step S232: calculating a fuzzy membership matrix:
r i1 ＝(p i -90)/10 100≥p i ≥90
wherein R in R ij Representing the membership of the ith element in P with respect to the jth class, P i A state of the art score representing the i-th lower level indicator;
step S233: combining with the fuzzy judgment matrix R, the weight set w= { w 1 ,w 2 ,…,w n Score g= (100,85,70,40,0), calculate the state of the art score T of the upper level indicator:
wherein B is the membership degree of the upper layer index to each level, B j The j element in B, T is the technical condition score of the upper index, g j Is the j-th element in G;
step S234: according to the method, scores of the overall technical conditions of the lower structure, the bridge deck system, the support and limiting device, the mechanical property, the apparent condition, the operation environment and the bridge are calculated sequentially.
The step S3 specifically includes:
step S31: GCI score based on individual component state of the art i Developing a predetermined maintenance scheme for the individual components;
step S32: score SCI based on the state of the art of the component population i Systematic maintenance work is performed for all the components of the same type.
The multi-source information of the evaluation method comprises daily inspection of bridges, periodic detection and health monitoring systems.
The evaluation index system comprises three major indexes of apparent conditions, mechanical properties and operation environment.
The optimistic coefficient of the optimistic coefficient variable weight analysis method takes a value of-0.04.
The index weight is obtained from the investigation result of 30 experts, and the final weight result is calculated by the group decision method based on the fuzzy analytic hierarchy process.
The score set G takes a value of g= (100,85,70,40,0).
A cable-stayed bridge safety evaluation system based on multi-source information-fuzzy analytic hierarchy process realizes multi-source operation and maintenance information fusion and structural performance evaluation of a large-span cable-stayed bridge, comprising:
health monitoring system: the sensors which are distributed during bridge construction are built or utilized to be connected with each other to form a health monitoring system, data can be collected in real time, and the data can be sent to a background server through a communication module at a target time interval; wherein, each sensor detailed information:
overhead offset sensor: the high-precision inclinometer or displacement meter can be used for acquiring the rotation angle change or displacement change of the top of the cable tower and corresponding to the geometric deflection of the structure in the evaluation index system;
structural deflection sensor: a differential pressure sensor can be adopted for acquiring deflection changes of each measuring point of the girder and corresponding to geometric deflection of the structure in an evaluation index system;
the girder longitudinal displacement sensor: the telescopic device system is arranged at the support of the girder end of the girder, and can adopt a fiber grating displacement sensor for acquiring the longitudinal displacement change value of the girder and corresponds to the telescopic device system in the evaluation index system;
structural strain sensor: the strain sensor is arranged at the representative section of the girder, and can be used for acquiring strain variation values of various measuring points of the girder, and correspondingly evaluating one girder stress in an index system;
a cable sensor: the cable force sensor is arranged at the representative stay cable and can be used for acquiring vibration data of each stay cable so as to calculate the cable force of the stay cable and correspondingly evaluate the cable force in an index system;
structural vibration sensor: the system is arranged at a structural representative section, and a force balance acceleration sensor can be used for acquiring vibration acceleration of each measuring point so as to calculate the self-vibration frequency of the structure and correspondingly evaluate the structural frequency in an index system;
traffic load monitoring sensor: the dynamic weighing system can be adopted to acquire the total weight, the speed, the axle weight and other information of each lane, and the traffic volume in the corresponding evaluation index system;
wind speed sensor: three ultrasonic or mechanical wind speed and direction sensors can be used for acquiring wind speed and wind direction information and corresponding to the wind speed in the evaluation index system;
temperature sensor: the fiber bragg grating temperature sensor is arranged at the representative section of the structure and is used for acquiring the structure temperature and the environment temperature and correspondingly evaluating the environment temperature in the index system;
bridge member disease database: the system is arranged on a server and comprises disease data which are provided by daily inspection, regular detection report and health monitoring system transmission of bridges, disease position, disease description, disease severity and disease data, and grade and technical condition score data converted from the disease data;
and a bridge overall condition scoring module: the method comprises the steps of running on a server, sequentially carrying out technical condition assessment on components, parts and upper indexes based on the technical condition scores of bottom layer diseases of each component in a bridge component disease database, obtaining each part score and technical condition grade, and finally obtaining the total technical condition score of the bridge;
bridge maintenance operation strategy module: the method comprises the steps of running on a server, based on the technical condition scores of all parts of the bridge provided by a bridge overall condition scoring module, aiming at component layers and component overall scores, making a strategy between the technical condition scores and a bridge maintenance operation strategy;
and a terminal module: the user utilizes the strategy between the technical condition score and the bridge maintenance operation strategy, which are operated and output by the bridge maintenance operation strategy module, so as to realize accurate maintenance.
The bridge member disease database counts the proportion of abnormal points in each monitoring index by setting upper and lower thresholds, and records the proportion into the corresponding member disease database;
selecting the latest disease data of each component in the component disease database, converting the latest disease data into a deduction value DP according to a bottom disease scoring rule, and taking the deduction value DP as basic data for subsequent evaluation;
designing disease grades, including: the qualitative indexes in the bottom layer diseases of all the components are selected, the disease grade is determined according to the severity of the qualitative diseases, the grade can be divided into 1-5 classes, 1 class represents perfect, and 5 classes represent serious diseases; selecting quantitative indexes in bottom layer diseases of each component, determining corresponding disease grades according to the value range of the quantitative indexes, wherein class 1 represents perfect, and class 5 represents severe diseases; selecting health monitoring indexes, determining corresponding disease grades according to the proportion of abnormal points, wherein class 1 represents perfect, and class 5 represents serious diseases; determining a bottom disease deduction value DP according to the grade of each bottom disease; grades 1-5 correspond to points DP of 0, 15, 35, 60, 100, respectively.
The bridge overall situation scoring module comprises:
calculation module 1: calculating the technical condition score GCI of a single component according to the disease deduction value DP of each bottom layer of the component i ：
When x=1
U 1 ＝DP i1
When x is greater than or equal to 2
When DP ij When=100
GCI il ＝0
Wherein: GCI i A score representing the ith component, the value range being 0 to 100 points; k represents the number of types of underlying diseases in which the ith component is buckled; u, x, y represent introduced variables; j represents the jth type underlying defect of the ith component; DP (DP) ij Class j representing the ith componentThe deduction value of the bottom layer diseases;
calculation module 2: based on the scores GCI of all types of members i Calculating a technical condition score for the population of components based on an optimistic coefficient weight analysis:
wherein: SCI (SCI) i The score of the class i component of the cable-stayed bridge is expressed, and the value range is 0-100 points; n represents the total number of components that the i-th class of components generally comprises; w (W) k A weight of a kth member representing an ensemble of ith class members; GCI ik A score for the kth member representing the population of the ith member; w (w) k The initial weight of the kth component of the total of the ith component is expressed, and 1/n is taken; s is S k A state variable weight parameter representing a kth component of the ith class of components overall; alpha represents an optimistic coefficient, taking the value-0.04;
an arithmetic average of all component scores representing the class i component population;
calculation module 3: and according to the technical condition score of the whole member, sequentially calculating the technical condition score of the upper-layer index by combining the evaluation index system and the weight, and finally obtaining the technical condition score and the grade of the whole bridge.
The calculation module 3:
calculation module 3-1: for each upper level index, the lower level index of the index is determined by combining the evaluation index system shown in fig. 3 and the index weights shown in fig. 4 to obtain the diversity p= { P 1 ,p 2 ,…,p n -weights corresponding to the respective indicesThe weight set w= { w 1 ,w 2 ,…,w n }；
Calculation module 3-2: according to the membership function form as in fig. 5, a fuzzy membership matrix is calculated:
r i1 ＝(p i -90)/10 100≥p i ≥90
wherein R in R ij Representing the membership of the ith element in P with respect to the jth class, P i A state of the art score representing the i-th lower level indicator;
calculation module 3-3: combining with the fuzzy judgment matrix R, the weight set w= { w 1 ,w 2 ,…,w n Score g= (100,85,70,40,0), calculate the state of the art score T of the upper level indicator:
wherein B is the membership degree of the upper layer index to each level, and T is the technical condition score of the upper layer index;
calculation module 3-4: and sequentially calculating scores of the lower structure, the bridge deck system, the support and the limiting device, mechanical properties, apparent conditions, operation environments and general technical conditions of the bridge according to the calculation modules 3-1 to 3-4.
Compared with the prior art, the invention has the following beneficial effects:
1) Based on the multisource information fusion technology, the multisource data of the multiple platforms generated in the bridge operation and management process are comprehensively utilized, and the multisource data mainly comprise daily inspection, periodic detection and health monitoring system data.
2) Based on the characteristics of daily inspection and high-frequency updating of health monitoring data, the time period limit of the existing evaluation method is broken through, and the high-frequency updating of bridge evaluation results can be realized.
3) By establishing a complete evaluation index system, key mechanical indexes and operation environment indexes of the cable-stayed bridge are introduced, and the weights of the indexes are determined based on expert investigation results, so that comprehensive evaluation on the safety, durability and applicability of the cable-stayed bridge can be realized, and maintenance operation is guided.
4) Based on the theory of fuzzy analytic hierarchy process, the uncertainty in the bridge evaluation work is considered.
5) And the component scoring result is output in a refined mode, and the maintenance work can be directly guided through component scoring and component overall scoring.
Drawings
FIG. 1 is a complete workflow diagram of the present invention;
FIG. 2 is a schematic diagram of a disease database;
FIG. 3 is a schematic diagram of an evaluation index system according to the present invention;
FIG. 4 is a graph showing the weights of the indicators according to the present invention;
FIG. 5 is a diagram showing membership functions according to the present invention;
FIG. 6 is a schematic diagram of the calculation result of the cable-stayed bridge safety evaluation method;
FIG. 7 is a flow chart of a multi-source information-fuzzy analytic hierarchy process for cable-stayed bridge security assessment system.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
A cable-stayed bridge safety assessment method based on multisource information-fuzzy analytic hierarchy process (SSA), as shown in figure 1, comprises the following steps:
step S1: collecting the daily inspection of the bridge, periodically detecting and reporting the disease data in the health monitoring system, extracting the disease position, the disease description and the disease severity, constructing a bridge member disease database shown in figure 2, and converting the disease data into grade and technical condition scores;
step S2: based on the technical condition scores of the bottom layer diseases of each component, carrying out technical condition assessment on the components, the parts and the upper layer indexes in sequence to obtain the scores of each part and the technical condition grades, and finally obtaining the total technical condition scores of the bridge;
step S3: based on the technical condition scores of all parts of the bridge, aiming at the component layer and the component overall score, a strategy between the technical condition score and a bridge maintenance operation strategy is formulated, so that accurate maintenance is realized.
The step S1 specifically includes:
step S11: collecting daily inspection in a target time period, periodically detecting and reporting disease data in a report, recording the position of the disease, describing the disease, and inputting the disease into a component disease database;
step S12: collecting data of a health monitoring system (for collecting system facilities arranged on most cable-stayed bridges at present, distributing the data during bridge construction or supplementing the data during bridge operation) in a target time period, counting the proportion of abnormal points in each monitoring index by setting upper and lower thresholds, and recording the proportion in a corresponding component disease database;
step S13: and selecting the latest disease data of each component in the component disease database, converting the latest disease data into a deduction value DP according to the bottom disease scoring rule, and taking the deduction value DP as basic data for subsequent evaluation.
The step S13 specifically includes:
step S131: the qualitative indexes in the bottom layer diseases of all the components are selected, the disease grade is determined according to the severity of the qualitative diseases, the grade can be divided into 1-5 classes, 1 class represents perfect, and 5 classes represent serious diseases;
step S132: selecting quantitative indexes in bottom layer diseases of each component, determining corresponding disease grades according to the value range of the quantitative indexes, wherein class 1 represents perfect, and class 5 represents severe diseases;
step S133: selecting health monitoring indexes, determining corresponding disease grades according to the proportion of abnormal points, wherein class 1 represents perfect, and class 5 represents serious diseases;
step S134: and determining the bottom disease deduction value DP according to the grade of each bottom disease. Grades 1-5 correspond to points DP of 0, 15, 35, 60, 100, respectively.
The step S2 specifically includes:
step S21: calculating the technical condition score GCI of a single component according to the disease deduction value DP of each bottom layer of the component i ：
When x=1
U 1 ＝DP i1
When x is greater than or equal to 2
When DP ij When=100
GCI il ＝0
Wherein: GCI i A score representing the ith component, the value range being 0 to 100 points; k represents the number of types of underlying diseases in which the ith component is buckled; u, x, y represent introduced variables; j represents the jth type underlying defect of the ith component; DP (DP) ij A catch value representing a jth type underlying defect of the ith component;
step S22: based on the scores GCI of all types of members i Calculating a technical condition score for the population of components based on an optimistic coefficient weight analysis:
wherein: SCI (SCI) i The score of the class i component of the cable-stayed bridge is expressed, and the value range is 0-100 points; n represents the total number of components that the i-th class of components generally comprises; w (W) k A weight of a kth member representing an ensemble of ith class members; GCI ik A score for the kth member representing the population of the ith member; w (w) k The initial weight of the kth component of the total of the ith component is expressed, and 1/n is taken; s is S k A state variable weight parameter representing a kth component of the ith class of components overall; alpha represents an optimistic coefficient, taking the value-0.04;
an arithmetic average of all component scores representing the class i component population;
step S23: and according to the technical condition score of the whole member, sequentially calculating the technical condition score of the upper-layer index by combining the evaluation index system and the weight, and finally obtaining the technical condition score and the grade of the whole bridge.
The step S23 specifically includes:
step S231: for each upper level index, the lower level index of the index is determined by combining the evaluation index system shown in fig. 3 and the index weights shown in fig. 4 to obtain the diversity p= { P 1 ,p 2 ,…,p n And the weight set w= { w corresponding to each index 1 ,w 2 ,…,w n }，
Step S232: according to the membership function form as in fig. 5, a fuzzy membership matrix is calculated:
r i1 ＝(p i -90)/10 100≥p i ≥90
wherein R in R ij Representing the membership of the ith element in P with respect to the jth class, P i A state of the art score representing the i-th lower level indicator;
step S233: combining with the fuzzy judgment matrix R, the weight set w= { w 1 ,w 2 ,…,w n Score g= (100,85,70,40,0), calculate the state of the art score T of the upper level indicator:
wherein B is the membership degree of the upper layer index to each level, B j The j element in B, T is the technical condition score of the upper index, g j Is the j-th element in G;
step S234: according to the method, scores of the overall technical conditions of the lower structure, the bridge deck system, the support and the limiting device, the mechanical properties, the apparent conditions, the operating environment and the bridge are calculated in sequence.
The step S3 specifically includes:
step S31: GCI score based on individual component state of the art i Developing a predetermined maintenance scheme for the individual components;
step S32: score SCI based on the state of the art of the component population i Systematic maintenance work is performed for all the components of the same type.
A cable-stayed bridge safety evaluation system based on multi-source information-fuzzy analytic hierarchy process realizes multi-source operation and maintenance information fusion and structural performance evaluation of a large-span cable-stayed bridge, comprising:
health monitoring system: the method comprises the steps of rebuilding or utilizing various sensors (strain sensors, stay cable force sensors, structural deflection monitoring sensors, temperature sensors and the like) which are distributed during bridge construction, connecting and forming a health monitoring system, acquiring data in real time, and sending the data to a background server through a communication module at a target time interval; wherein, each sensor detailed information:
overhead offset sensor: the cable tower is arranged at the top of the cable tower, and a high-precision inclinometer (one in the longitudinal direction and the transverse direction) or a displacement meter (one in the longitudinal direction, the transverse direction and the vertical direction) can be adopted for acquiring the rotation angle change or the displacement change of the top of the cable tower, and the geometric deflection of the structure in the figure 3 corresponds to the rotation angle change or the displacement change;
structural deflection sensor: a representative section arranged on the girder of the bridge can adopt a differential pressure sensor for acquiring deflection change of each measuring point of the girder, and corresponds to geometric deflection of the structure in fig. 3;
the girder longitudinal displacement sensor: the device is arranged at a support at the girder end of the girder, and a fiber grating displacement sensor can be adopted for acquiring the longitudinal displacement variation value of the girder, which corresponds to the telescopic device system in fig. 3;
structural strain sensor: the strain sensor is arranged at the representative section of the girder, and can be used for acquiring the strain change value of each measuring point of the girder, which corresponds to one of the girder stresses in fig. 3;
a cable sensor: the vibration method cable force sensor can be used for acquiring vibration data of each stay cable to calculate the cable force of the stay cable, and the cable force corresponds to the cable force in fig. 3;
structural vibration sensor: a force balance acceleration sensor is arranged at a representative section of the structure and is used for acquiring the vibration acceleration of each measuring point so as to calculate the self-vibration frequency of the structure, which corresponds to the structure frequency in fig. 3;
traffic load monitoring sensor: the dynamic weighing system can be adopted to acquire the total weight, the vehicle speed, the axle weight and other information of each lane, and corresponds to the traffic volume in fig. 3;
wind speed sensor: three ultrasonic or mechanical wind speed and direction sensors can be used for acquiring wind speed and wind direction information, which corresponds to the wind speed in the graph 3, and are arranged at the tower top or the bridge deck;
temperature sensor: a fiber bragg grating temperature sensor can be used for acquiring the structure temperature and the environment temperature, which correspond to the environment temperature in FIG. 3, and is arranged at the representative section of the structure;
bridge member disease database: the system is arranged on a server and comprises disease data which are provided by daily inspection, regular detection report and health monitoring system transmission of bridges, disease position, disease description, disease severity and disease data, and grade and technical condition score data converted from the disease data;
and a bridge overall condition scoring module: the method comprises the steps of running on a server, sequentially carrying out technical condition assessment on components, parts and upper indexes based on the technical condition scores of bottom layer diseases of each component in a bridge component disease database, obtaining each part score and technical condition grade, and finally obtaining the total technical condition score of the bridge;
bridge maintenance operation strategy module: the method comprises the steps of running on a server, based on the technical condition scores of all parts of the bridge provided by a bridge overall condition scoring module, aiming at component layers and component overall scores, making a strategy between the technical condition scores and a bridge maintenance operation strategy;
and a terminal module: the user utilizes the strategy between the technical condition score and the bridge maintenance operation strategy, which are operated and output by the bridge maintenance operation strategy module, so as to realize accurate maintenance.
The bridge member disease database counts the proportion of abnormal points in each monitoring index by setting upper and lower thresholds, and records the proportion into the corresponding member disease database;
selecting the latest disease data of each component in the component disease database, converting the latest disease data into a deduction value DP according to a bottom disease scoring rule, and taking the deduction value DP as basic data for subsequent evaluation;
designing disease grades, including: the qualitative indexes in the bottom layer diseases of all the components are selected, the disease grade is determined according to the severity of the qualitative diseases, the grade can be divided into 1-5 classes, 1 class represents perfect, and 5 classes represent serious diseases; selecting quantitative indexes in bottom layer diseases of each component, determining corresponding disease grades according to the value range of the quantitative indexes, wherein class 1 represents perfect, and class 5 represents severe diseases; selecting health monitoring indexes, determining corresponding disease grades according to the proportion of abnormal points, wherein class 1 represents perfect, and class 5 represents serious diseases; determining a bottom disease deduction value DP according to the grade of each bottom disease; grades 1-5 correspond to points DP of 0, 15, 35, 60, 100, respectively.
The bridge overall situation scoring module comprises:
calculation module 1: calculating the technical condition score GCI of a single component according to the disease deduction value DP of each bottom layer of the component i ：
When x=1
U 1 ＝DP i1
When x is greater than or equal to 2
When DP ij When=100
GCI il ＝0
Wherein: GCI i A score representing the ith component, the value range being 0 to 100 points; k represents the number of types of underlying diseases in which the ith component is buckled; u, x, y represent introduced variables; j represents the jth type underlying defect of the ith component; DP (DP) ij A catch value representing a jth type underlying defect of the ith component;
calculation module 2: based on the scores GCI of all types of members i Calculating a technical condition score for the population of components based on an optimistic coefficient weight analysis:
wherein: SCI (SCI) i The score of the class i component of the cable-stayed bridge is expressed, and the value range is 0-100 points; n represents the total number of components that the i-th class of components generally comprises; w (W) k A weight of a kth member representing an ensemble of ith class members; GCI ik A score for the kth member representing the population of the ith member; w (w) k The initial weight of the kth component of the total of the ith component is expressed, and 1/n is taken; s is S k A state variable weight parameter representing a kth component of the ith class of components overall; alpha represents an optimistic coefficient, taking the value-0.04;
all member scores representing the class i member populationAn arithmetic mean; />
Calculation module 3: and according to the technical condition score of the whole member, sequentially calculating the technical condition score of the upper-layer index by combining the evaluation index system and the weight, and finally obtaining the technical condition score and the grade of the whole bridge.
The calculation module 3:
calculation module 3-1: for each upper level index, the lower level index of the index is determined by combining the evaluation index system shown in fig. 3 and the index weights shown in fig. 4 to obtain the diversity p= { P 1 ,p 2 ,…,p n And the weight set w= { w corresponding to each index 1 ,w 2 ,…,w n }；
Calculation module 3-2: according to the membership function form as in fig. 5, a fuzzy membership matrix is calculated:
r i1 ＝(p i -90)/10 100≥p i ≥90
wherein R in R ij Representing the membership of the ith element in P with respect to the jth class, P i A state of the art score representing the i-th lower level indicator;
calculation module 3-3: combining with the fuzzy judgment matrix R, the weight set w= { w 1 ,w 2 ,…,w n Score g= (100,85,70,40,0), calculate the state of the art score T of the upper level indicator:
wherein B is the membership degree of the upper layer index to each level, and T is the technical condition score of the upper layer index;
calculation module 3-4: and sequentially calculating scores of the lower structure, the bridge deck system, the support and the limiting device, mechanical properties, apparent conditions, operation environments and general technical conditions of the bridge according to the calculation modules 3-1 to 3-4.
Examples
The multi-source information-fuzzy analytic hierarchy process-based cable-stayed bridge safety assessment constructed by the method is tried in a certain highway cable-stayed bridge, and is calculated sequentially according to the steps of fig. 1, and the component disease database shown in fig. 2 is generated by collecting disease data in daily inspection and periodic detection of the bridge and combining with health monitoring system data.
Health monitoring system: the sensors which are distributed during bridge construction are built or utilized to be connected with each other to form a health monitoring system, data can be collected in real time, and the data can be sent to a background server through a communication module at a target time interval;
bridge member disease database: the system is arranged on a server and comprises disease data which are provided by daily inspection, regular detection report and health monitoring system transmission of bridges, disease position, disease description, disease severity and disease data, and grade and technical condition score data converted from the disease data;
the bridge overall condition scoring module is operated on the server;
bridge maintenance operation strategy module: the method comprises the steps of running on a server, based on the technical condition scores of all parts of the bridge provided by a bridge overall condition scoring module, aiming at component layers and component overall scores, making a strategy between the technical condition scores and a bridge maintenance operation strategy;
and a terminal module: providing a user with a user interface.
The bridge overall situation scoring module is operated on the server, and based on the technical situation scores of the bottom layer diseases of each component in the bridge component disease database, the technical situation scores of the components, the components and the upper layer indexes are sequentially evaluated to obtain the scores of each part and the technical situation grades, and finally the bridge overall technical situation scores are obtained: the index system shown in fig. 3 and the index weight shown in fig. 4 are adopted, the membership function form shown in fig. 5 is adopted in the upper index calculation process, and the score of each part is finally calculated as shown in fig. 6.
And a terminal module: based on the result, the user uses the technical condition score (shown in fig. 6) output by the bridge maintenance operation policy module to perform corresponding maintenance operation measures to realize accurate maintenance. The evaluation system can be applied to all cable-stayed bridges to give out the performance evaluation result of the cable-stayed bridges and guide maintenance operation work.
The effect proves after the implementation of the example: the invention can effectively fuse the multi-source data generated in the bridge operation period; the method can break through the time period (1 year) limit of the existing assessment method, and realizes the online and high-frequency update of bridge assessment work; the output component scoring result can accurately guide bridge maintenance operation work; the evaluation system has high practicability; the method provides effective theoretical support for maintenance operation of the cable-stayed bridge.
Claims (7)
1. The cable-stayed bridge safety evaluation method based on multi-source information-fuzzy analytic hierarchy process is characterized by realizing multi-source operation and maintenance information fusion and structural performance evaluation of a large-span cable-stayed bridge, and comprises the following steps:
step S1: collecting disease data in a bridge daily inspection, periodic detection report and health monitoring system, extracting disease positions, disease descriptions and disease severity, constructing a bridge member disease database, and converting the disease data into grades and technical condition scores;
step S2: based on the technical condition scores of the bottom layer diseases of each component, sequentially carrying out technical condition assessment on the components, the parts and the upper layer indexes to obtain the scores of each part and the technical condition grades, and finally obtaining the total technical condition scores of the bridge;
step S3: based on the technical condition scores of all parts of the bridge, aiming at the component layer and the component overall score, a strategy between the technical condition score and a bridge maintenance operation strategy is formulated, so that accurate maintenance is realized.
2. The cable-stayed bridge safety evaluation method based on multi-source information-fuzzy analytic hierarchy process of claim 1, wherein the step S1 specifically comprises:
step S11: collecting daily inspection in a target time period, periodically detecting and reporting disease data in a bridge, recording disease positions and disease descriptions, inputting the disease positions and the disease descriptions into a component disease database,
step S12: collecting health monitoring system data in a target time period, counting the proportion of abnormal points in each monitoring index by setting upper and lower thresholds, recording the proportion into a corresponding component disease database,
step S13: selecting the latest disease data of each component in the component disease database, converting the latest disease data into a deduction value DP according to a bottom disease scoring rule, and taking the deduction value DP as basic data for subsequent evaluation;
the step S13 specifically includes:
step S131: the qualitative index in the bottom layer diseases of each component is selected, the disease grade is determined according to the severity of the qualitative diseases, the grade is classified into 1-5 types, 1 type represents perfect, 5 types represent serious diseases,
step S132: selecting quantitative indexes in the bottom layer diseases of all components, determining corresponding disease grades according to the value range of the quantitative indexes, wherein class 1 represents perfect, class 5 represents serious diseases,
step S133: selecting health monitoring index, determining corresponding disease grade according to abnormal point proportion, wherein class 1 represents perfect, class 5 represents serious disease,
step S134: determining a bottom disease deduction value DP according to the grade of each bottom disease; grades 1-5 correspond to points DP of 0, 15, 35, 60, 100, respectively.
3. The cable-stayed bridge safety evaluation method based on multi-source information-fuzzy analytic hierarchy process of claim 1, wherein the step S2 specifically comprises:
step S21: calculating the technical condition score GCI of a single component according to the disease deduction value DP of each bottom layer of the component i ：
When x=1
U 1 ＝DP i1
When x is greater than or equal to 2
When DP ij When=100
GCI il ＝0
Wherein: GCI i A score representing the ith component, the value range being 0 to 100 points; k represents the number of types of underlying diseases in which the ith component is buckled; u, x, y represent the introduced variables; j represents the jth type underlying defect of the ith component; DP (DP) ij A catch value representing a jth type underlying defect of the ith component;
step S22: based on the scores GCI of all types of members i Calculating a technical condition score for the population of components based on an optimistic coefficient weight analysis:
wherein: SCI (SCI) i The score of the class i component of the cable-stayed bridge is expressed, and the value range is 0-100 points; n represents the total number of components that the i-th class of components generally comprises; w (W) k A weight of a kth member representing an ensemble of ith class members; GCI ik A score for the kth member representing the population of the ith member; w (w) k The initial weight of the kth component of the total of the ith component is expressed, and 1/n is taken; s is S k A state variable weight parameter representing a kth component of the ith class of components overall; α represents an optimistic coefficient;
an arithmetic average of all component scores representing the class i component population;
step S23: and according to the technical condition score of the whole member, sequentially calculating the technical condition score of the upper-layer index by combining the evaluation index system and the weight, and finally obtaining the technical condition score and the grade of the whole bridge.
4. The cable-stayed bridge safety evaluation method based on multi-source information-fuzzy analytic hierarchy process of claim 3, wherein the step S23 specifically comprises:
step S231: for each upper-layer index, combining with an evaluation index system, respectively determining the lower-layer index of the index to obtain diversity P= { P 1 ,p 2 ,…,p n And the weight set w= { w corresponding to each index 1 ,w 2 ,…,w n }；
Step S232: calculating a fuzzy membership matrix:
r i1 ＝(p i -90)/10 100≥p i ≥90
wherein R in R ij Representing the membership of the ith element in P with respect to the jth class, P i A state of the art score representing the i-th lower level indicator;
step S233: combining with the fuzzy judgment matrix R, the weight set w= { w 1 ,w 2 ,…,w n Score g= (100,85,70,40,0), calculate the state of the art score T of the upper level indicator:
wherein B is the membership degree of the upper layer index to each level, and T is the technical condition score of the upper layer index;
step S234: according to the method, scores of the overall technical conditions of the lower structure, the bridge deck system, the support and the limiting device, the mechanical properties, the apparent conditions, the operating environment and the bridge are calculated in sequence.
5. The cable-stayed bridge safety evaluation method based on multi-source information-fuzzy analytic hierarchy process of claim 1, wherein the step S3 specifically comprises:
step S31: GCI score based on individual component state of the art i Developing a predetermined maintenance scheme for the individual components;
step S32: score SCI based on the state of the art of the component population i Systematic maintenance work is performed for all the components of the same type.
6. The cable-stayed bridge safety assessment method based on multi-source information-fuzzy analytic hierarchy process according to claim 1, wherein the index weight is obtained from the investigation result of 30 experts, and the final weight result is calculated by the group decision method based on the fuzzy analytic hierarchy process.
7. The cable-stayed bridge safety evaluation method based on multi-source information-fuzzy analytic hierarchy process of claim 1, wherein the score set G takes a value of g= (100,85,70,40,0).
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