CN113255811A - Beam bridge multi-source heterogeneous data fusion decision-making system based on BIM - Google Patents
Beam bridge multi-source heterogeneous data fusion decision-making system based on BIM Download PDFInfo
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Abstract
The invention discloses a BIM-based beam bridge multi-source heterogeneous data fusion decision-making system, which comprises an operation and maintenance basic data unit, a BIM visual model unit, a disease intelligent acquisition unit, a disease maintenance tracking unit, a technical condition evaluation unit, a structure monitoring and early warning unit, a load test analysis unit, a bearing capacity test unit, a maintenance investment decision-making unit, a virtual reality demonstration unit, a cloud server and a client. Aiming at the decision problems of maintenance and repair and reinforcement treatment schemes in the beam bridge maintenance process, the invention comprehensively applies multi-source heterogeneous data of disease investigation, monitoring and early warning, detection and analysis and bearing capacity detection and calculation to make a scientific maintenance investment scheme and guide scientific maintenance and management.
Description
Technical Field
The invention belongs to the technical field of building information, and particularly relates to a BIM-based beam bridge multi-source heterogeneous data fusion decision-making system.
Background
In recent years, with the rapid development of intelligent traffic, the disease investigation, monitoring and early warning and the like of bridges basically realize informatization and intellectualization; however, the data islanding phenomenon is obvious, and multisource heterogeneous data such as basic information, disease information, monitoring and detecting information and the like are not organically fused, so that the problems of low data utilization efficiency, inaccurate structural operation state identification and the like are caused.
Decision management platforms for bridges are common in large bridge management; however, with the innovation of the transportation industry of multiple provinces, the provincial maintenance investment plan management mode is implemented step by step, the center of gravity of the provincial bridge unified management and maintenance is a beam bridge in a common structural form, and a decision method and a platform based on big data are the core of problem solving.
The BIM technology is rapidly developed in bridge design and construction in recent years, however, as the maintenance process is complicated and long, a plurality of units and personnel are involved, the digitization degree is low, and a data isolated island phenomenon exists, the technology is rarely applied in the maintenance management process; however, the BIM visualization model is used as a carrier of information, and is applicable to all stages of bridge full life cycle management and control on a technical level.
Disclosure of Invention
The invention provides a BIM-based beam bridge multi-source heterogeneous data fusion decision-making system, which can realize network-level maintenance priority ordering based on a maintenance investment decision-making algorithm of an analytic hierarchy process, fuse service data such as frequent inspection, periodic inspection, load test, bearing capacity evaluation and the like, distinguish technical condition evaluation considering only apparent diseases and technical condition evaluation considering structural deformation by combining with actual inspection conditions, provide scientific support for selection of maintenance and reinforcement treatment, apply a BIM model as a data carrier, apply VR technology to strengthen the understanding of experts on bridge structural diseases and deformation by using virtual reality scenes, simultaneously look up various types of file parameters, develop online communication discussion, demonstrate maintenance and reinforcement treatment schemes suggested by a platform and ensure the rationality of the overall scheme.
The technical scheme is as follows:
a BIM-based beam bridge multi-source heterogeneous data fusion decision making system comprises: the system comprises an operation and maintenance basic data unit, a BIM visual model unit, a disease intelligent acquisition unit, a disease maintenance tracking unit, a technical condition evaluation unit, a structure monitoring and early warning unit, a load test analysis unit, a bearing capacity test unit, a maintenance investment decision unit, a virtual reality demonstration unit, a cloud server and a client;
the operation and maintenance basic data unit comprises highway bridge and culvert maintenance specifications, JTG H11-2004 and appendix A bridge basic department content, and is used for maintaining annual charge per kilometer calculated by investment decision, determining whether to overload road sections and determining whether to transport passage information of large pieces;
the BIM visual model unit is used for displaying the bridge structure in an imaging mode according to the actual size by applying a lightweight technology, converting three-dimensional information of diseases into two-dimensional information for displaying, displaying technical condition evaluation grades of different components, displaying a monitoring and early warning sensing system and real-time data, displaying a load test distribution scheme and corresponding structural response, and displaying the vibration mode and frequency acquired by a load test; the three-dimensional information of the diseases is converted into two-dimensional information for displaying, the structure surface attribution information and the coordinate information in the disease position description are combined with the BIM model, the structure surface and the specific position of the diseases in the model are accurately positioned, and roaming checking is realized;
the disease intelligent acquisition unit comprises a structure analysis standard, a position description standard, a characteristic description standard, an image file standard, an evaluation model library and an AI image recognition algorithm, wherein the evaluation model library comprises a component library, a sub-component library and a disease type library;
the disease maintenance tracking unit establishes the incidence relation of diseases with different time attributes, is used for recording the change of disease characteristics only during field inspection to improve the field inspection efficiency, and simultaneously realizes the development tracking and prediction of the disease length, width, area and maximum seam width characteristics according to a time axis so as to track and evaluate maintenance measures;
the technical condition evaluation unit automatically scores components, parts and parts according to the stability of diseases and generates a disease development graph and an evaluation report according to the technical condition evaluation standard of highway bridges, JTG T H21-2011;
the structure monitoring and early warning unit is used for pertinently deploying deformation, strain and fundamental frequency test elements aiming at the beam bridge with low evaluation score of the technical conditions of the upper structure and the lower structure, monitoring the mechanical behavior change of the structure, and setting an early warning threshold value to early warn abnormal response;
the load test analysis unit is used for evaluating a beam bridge with a lower score aiming at the technical condition that the structural monitoring and early warning unit captures the rigidity change of the bearing member or part of the superstructure, developing a load test according to the highway bridge load test regulation, JTG/T J21-01-2015, inputting a detection scheme and a result, automatically calculating a check coefficient and analyzing whether the rigidity of the structure is in a normal range;
the bearing capacity test unit inputs material conditions and state parameter data aiming at a beam bridge with structure displacement or deformation and stress overrun according to the road bridge bearing capacity detection evaluation rule, JTG/T J21-2011, and performs bridge evaluation by combining the result of a load test analysis unit which needs to perform bearing capacity evaluation;
the maintenance investment decision unit aims at maintenance, maintenance and reinforcement treatment, can automatically calculate cost according to disease maintenance and maintenance measures and structural reinforcement treatment measures, realizes maintenance priority ranking based on an analytic hierarchy process, and automatically generates a reasonable distribution scheme with limited expenditure and a reasonable distribution scheme aiming at a special target; the disease maintenance measures are to set maintenance methods, unit prices and units corresponding to different types of diseases of the beam bridge; the structure reinforcement treatment measures are that a reinforcement treatment scheme, unit price and unit which can be selected by the beam bridge are set; the maintenance priority ranking is realized based on the analytic hierarchy process, and a priority ranking algorithm is provided;
the virtual reality demonstration unit is used for viewing the apparent diseases of the bridge through VR roaming in virtual reality, monitoring historical data and analysis results, analyzing and calculating results of a load test, and material conditions and state parameters, and demonstrating a reinforcement treatment scheme adopted by a bridge structure plan;
the data cloud server and the client are used for realizing real-time calling by storing the data in a network and realizing friendly interaction with a user through the WEB client and the mobile client.
Preferably, the BIM visual model unit can directly display information such as disease information, structure monitoring measuring points and testing maximum values, load test testing positions and checking coefficients, real-time traffic flow of the bridge deck, vehicle type classification and the like, distinguish technical condition evaluation results of all components by colors, check corresponding electronic files, material conditions and state parameters after clicking a specific position, support VR roaming and realize the demonstration of the rationality of a reinforcing treatment scheme of the bridge structure in a virtual reality environment; the electronic files comprise drawings and report data of construction period, operation period and the like.
Preferably, the disease maintenance tracking unit is used for completing a confirmation function by adding field detection on the data cloud server and the client, checking whether all historical diseases are tracked and investigated, marking a crack width test position on a crack key disease field, fusing frequent inspection and regular inspection information to realize low-frequency monitoring on the disease development condition, and providing data support for judging the change of the stress characteristic of the structure; and tracking and monitoring the maintained and maintained diseases, realizing tracking and evaluation of maintenance and maintenance measures, and recommending good maintenance and maintenance measures and eliminating poor maintenance and maintenance measures.
Preferably, the technical condition evaluation unit performs comprehensive evaluation after judging the disease property according to the frequently checked and regularly checked disease data and by combining the low-frequency monitoring data of the disease maintenance tracking unit.
Preferably, the maintenance investment decision unit is used for performing maintenance, maintenance and reinforcement treatment, wherein maintenance and maintenance are supported by using frequent inspection and periodic inspection disease information as basic data, and a calculation result of the technical condition evaluation unit is directly supported by using the maintenance investment decision unit and fusing the operation and maintenance basic data unit to perform intelligent auxiliary decision; the reinforcement treatment is based on frequent inspection and regular inspection of apparent diseases, a structure monitoring and early warning unit and a load test analysis unit are integrated, and mid-span deflection evaluation and structure deflection evaluation are realized; when the technical condition evaluation structure of the component is controlled by mid-span deflection and structure deflection, the material condition and state parameter data of the bearing capacity test unit are fused to carry out bearing capacity check calculation; the maintenance investment decision unit follows the following general idea: the priority of reinforcement treatment is higher than that of maintenance and repair; bridges with four or five types of overall and lower structure technical status grades or five types of upper structures need to be treated; the maintenance investment decision unit establishes a maintenance priority evaluation index system based on an analytic hierarchy process, wherein the maintenance priority evaluation index system comprises a safety index and an importance index; wherein the safety indexes comprise: classifying annual charge per kilometer, route grade, bridge position and bridge length; the importance indicators include: the method comprises the following steps of evaluating technical condition total scores, evaluating upper structures, evaluating lower structures, evaluating bridge deck systems, operating years, special structural bridges, frequent annual inspection times, whether road sections are overloaded or not and whether large transportation channels are transported or not; the annual charge per kilometer, the route grade, the bridge position, the bridge length classification, the operation age, the special structure bridge, the annual frequent inspection times, whether the road section is overloaded or not and whether the large transport passage is obtained from the operation and maintenance basic data unit or not; the total technical condition evaluation score, the upper structure evaluation score, the lower structure evaluation score and the bridge deck system evaluation score are all obtained from a technical condition evaluation unit;
the maintenance investment decision unit determines the weight as an importance index of 0.33 and a safety index of 0.67 according to an expert survey method; the annual per kilometer charge amount is 0.58, the route grade is 0.26, the bridge position is 0.11, and the bridge length classification is 0.05; the technical condition evaluation total score is 0.13, the upper structure evaluation score is 0.23, the lower structure evaluation score is 0.11, the bridge deck system evaluation score is 0.03, the operation age is 0.07, the special structural bridge is 0.04, the annual frequent inspection frequency is 0.05, whether the road section is heavily loaded is 0.10, and whether the large transportation channel is 0.24.
Preferably, the maintenance investment decision unit determines the value of the selected decision parameter according to a 5-scale method, which is as follows:
the decision index value of charge per kilometer is as follows: 5- [2.5, + ∞), 4- [2.0, 2.5), 3- [1.5, 2.0), 2- [1.0, 1.5), 1- [0, 1.5);
taking the route grade decision index: 5-expressway, 4-first level road, 3-second level road, 2-third level road and 1-fourth level road;
bridge position decision index value: 5-main line bridge, 4-auxiliary road bridge, 3-ramp bridge, 2-overpass, 1-others;
bridge length classification index value: 5-super large bridge, 4-big bridge, 3-middle bridge and 2-small bridge;
the technical condition evaluation total score decision index value: 5- [60, 80), 3- [80, 95), 1- [95, 100 ];
evaluation score decision index value of the upper structure: 5- [40, 60), 4- [60, 80), 2- [80, 95), 1- [95, 100 ];
evaluation score decision index value of the lower structure: 5- [60, 80), 3- [80, 95), 1- [95, 100 ];
evaluation of bridge deck system scoring decision index value: 5- [0, 40), 4- [40, 60), 3- [60, 80), 2- [80, 95), 1- [95, 100 ];
and (3) operation age decision index value: 5- [0, 30), 4- [20, 30), 3- [10, 20), 4- [5, 10), 5- [0, 5);
taking the decision index of the special structure bridge: 5-special structural bridge, 1-conventional structural bridge;
frequent inspection decision index values: 5-0, 4- [0, 12), 3- [12, 24), 2- [24, 36), 1- [36, + ∞);
whether the heavily loaded road section decision index takes value: 5-is a heavy-load road section, 1-is a non-heavy-load road section;
whether the decision index of the large transport channel takes value is as follows: 5-is a large transport channel, 1-is a non-large transport channel.
Further, the maintenance investment decision unit supports the designated decision index not to participate in evaluation and the specific maintenance target, and includes: the technical conditions of the overall superstructure of the higher road network bridge are evenly divided, the number proportion of the lower three types of bridges and less is lower, and the length proportion of the lower three types of bridges and less is lower.
Further, the maintenance investment decision unit has the following specific algorithm:
for more positive maintenance expenses SⅡThe bridge maintenance method is used for bridge maintenance which has obvious disease influence on operation safety, comprises reasonable distribution of reinforcement, and meets the following necessary conditions: the technical conditions are that the bridges with four and five types and the bridges with the upper structure score lower than 40 points or the bridges with the lower structure score lower than 60 points in the three types must be maintained;
assuming that the total number of bridges in a road network is n, and the set is A; not only special structural bridges and grand bridges, but also the situation that the evaluation result is four types and five types is set A1(ii) a Except for A1Four and five other types of bridges, set A2(ii) a Not only is a special structure bridge and a super bridge, but also the evaluation result is the case of three types, namely intersection A3(ii) a Except for A3The other three types of bridges are set A4(ii) a Except for A1、A3Bridges and grand bridges of special construction, otherwise, of set A5(ii) a Otherwise set A6；
Maintenance priority A of each aggregate1＞A2＞(A3、A4)＞A5＞A6Wherein A is1、A2Required to repair, A3To be repaired, A4Repairable (there may be some bridges in the set with priority higher than A3) Investing a total cost SⅡ。A5、A6Can be repaired or not, and the total investment cost is conditional maintenance cost SI(for bridge maintenance which has no obvious disease to affect operation safety);
assumption set AiThe number of the middle element is xi(xi≥0)，i＝1～6，
Element (bridge) is aijBridge aijHas a maintenance cost of sijMaintenance status of cij(a value of 1 indicates maintenance is determined, and a value of 0 indicates maintenance is not determined). Set AiThe maintenance cost of the middle bridge is
The curing is carried out according to the following formula:
to determine A3、A4Maintenance status of middle element cijCalculating a maintenance priority evaluation index according to the decision index level and the weight; index PyHas a weight of WyY is the decision index number, bridge aijEach decision index PyIs taken as py-ij. Then bridge aijMaintenance priority score of Iij＝20∑Wypy-ijThe value range is 0-100; when the appointed decision index does not participate in evaluation, the decision index P is introducedyUsing a state parameter dy(when the participation evaluation value is 1, the non-participation evaluation value is 0), y is the number of the decision index, and the initial weight is W0-yCalculating the weight
A3、A4The maintenance priority scores of the medium elements are sequentially J from large to small1，J2… …, corresponding maintenance cost s1，s2… …, when satisfied
When, J△The corresponding bridge maintenance state is 1(Δ ═ 1 to m), a3、A4The maintenance state of other elements in the system is 0;
for the calculation of maintenance priority evaluation indexes considering special requirements, for convenient calculation, the method aims at a specific bridge aijThe following parameters were introduced:
the score contribution index is as follows:
Representing a bridge aijThe ratio of the expenses required for each 1-point increase in the overall (superstructure) technical condition score to the average level of all three types of bridges in the road network; dr-ijIs a bridge aijOverall technical condition score, SPCIr-ijIs a bridge aijUpper structure technical status score of (1);
quantitative contribution index:
representing a bridge aijThe ratio of the expenses required for maintenance to the average level of all three types of bridges in the road network;
length contribution index:
representing a bridge aijThe ratio of the expense required by maintenance per linear meter to the average level of all three types of bridges in the road network;
setting the participation coefficient of a specific maintenance target as eα、eβ、eγWhen the maintenance target is required to be met, the value is 1, and when the maintenance target is not required, the value is 0; then bridge aijThe maintenance priority score of (1) is calculated according to the formula:
Iij＝20[1+min{Wy}(eααij+eββij+eγγij)]∑Wypy-ij
preferably, the virtual reality demonstration unit supports the bridge needing reinforcement treatment to intuitively know the operation condition of the bridge at any time and any place, consults electronic archive data, carries out voice discussion in a platform and simulates an expert field demonstration scene.
The BIM-based beam bridge multi-source heterogeneous data fusion decision method and platform provided by the invention have the following advantages:
(1) the maintenance investment decision algorithm based on the analytic hierarchy process can realize the network-level maintenance priority ranking and support the intelligent generation of the maintenance cost distribution scheme under the conventional target and the special target;
(2) service data such as frequent inspection, periodic inspection, load test, bearing capacity evaluation and the like are fused, and the technical condition evaluation considering only apparent diseases and the technical condition evaluation considering structural deformation are distinguished by combining with actual inspection conditions, so that scientific support is provided for selection of maintenance and treatment and reinforcement;
(3) the BIM model is used as a data carrier, the VR technology is used for enhancing the knowledge of experts on bridge structure diseases and deformation through virtual reality scenes, meanwhile, various file parameters can be consulted, online communication discussion is developed, maintenance and repair and reinforcement treatment schemes suggested by the platform are demonstrated, and the rationality of the overall scheme is guaranteed.
Drawings
FIG. 1 is an overall structure diagram of a BIM-based beam bridge multi-source heterogeneous data fusion decision making system;
FIG. 2 is a general step implementation diagram of a BIM-based beam bridge multi-source heterogeneous data fusion decision making system.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the following describes in detail a BIM-based beam bridge multi-source heterogeneous data fusion decision-making system provided by the present invention with reference to an embodiment. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention.
Example 1
As shown in fig. 2, the work of the BIM-based beam bridge multi-source heterogeneous data fusion decision making system provided by the present invention includes 14 steps:
s1: the method comprises the steps that WEB or a mobile client is applied to maintain operation and maintenance basic data of a road network bridge;
s2: building a BIM model according to the actual structure size of the bridge, evaluating components needing to be divided by combining technical conditions, and associating drawing, report and other data in the electronic files such as construction period, operation period and the like;
s3: the method comprises the steps of intelligently collecting newly found diseases on site through a mobile client, recording the characteristic development of the newly found diseases at the future time, and recording whether the maintained and maintained diseases continue to develop or not;
s4: according to the acquired apparent diseases, carrying out intelligent assessment on technical conditions to obtain the technical condition assessment grades of each component, part and the whole;
s5: according to the technical condition evaluation grade, dividing the bridge into three types, namely a bridge which only needs maintenance and repair, a bridge which possibly needs reinforcement and treatment and a bridge which needs reinforcement and treatment;
s6: carrying out structure deflection test and load test analysis (or structure monitoring and early warning) aiming at a bridge which is possibly required to be reinforced, wherein the structure deflection test concerns the permanent deflection of the structure, and the load test analysis (or structure monitoring and early warning) concerns the response of the structure under the load action;
s7: judging according to the deflection, deformation and stress conditions of the structure, and when the deflection, deformation and stress conditions are not over-limit, indicating that the damage of the structure only affects the durability; when one of the two exceeds the limit, the stress performance of the structure is not consistent with the design state, and diseases may have irreversible influence on the overall rigidity and section rigidity of the structure;
s8: aiming at the condition that the both are judged not to exceed the limit in the S7, calculating the maintenance and repair expenditure;
s9: the method for evaluating the bearing capacity of the bridge which is judged to be either overrun in S7 and needs to be reinforced comprises the following steps:
s9-1: detecting material conditions and state parameters, and executing according to the test and assessment regulations for road and bridge carrying capacity (JTG/T J21-2011);
s9-2: carrying out bridge structure detection and calculation, and automatically executing according to the road and bridge bearing capacity detection and evaluation regulation (JTG/T J21-2011);
s10: judging whether the bearing capacity meets the requirement, if so, executing S8, and if not, executing S11;
s11: selecting a reinforcement treatment scheme according to the bearing capacity detection and calculation condition, and calculating the expenses;
s12: comprehensively and preliminarily determining bridges and expenses to be maintained and reinforced, combining a conventional management and maintenance target and a special management and maintenance target, and performing maintenance priority ordering under limited expenses by adopting an analytic hierarchy process;
s13: expert demonstration for developing maintenance investment schemes comprises the following steps:
s13-1: the BIM model synchronizes data of diseases, deformation, displacement, material conditions and state parameters;
s13-2: the expert refers to relevant data and electronic archive data in the BIM through a VR virtual reality technology;
s13-3: performing on-line technical demonstration, and mainly aiming at a reinforcing scheme adopted by a bridge needing to be reinforced;
s14: and repairing, compiling and maintaining the investment scheme according to the online technical demonstration result.
The invention provides a BIM-based beam bridge multi-source heterogeneous data fusion decision method and platform, wherein a maintenance investment decision algorithm based on an analytic hierarchy process can realize network-level maintenance priority sequencing, a visual BIM model is taken as a multi-source heterogeneous data carrier, full life cycle data fusion can be applied to the research and selection of maintenance and repair and reinforcement treatment schemes, VR virtual reality is applied to an expert demonstration process, and scientific maintenance decision for a beam bridge based on big data analysis is realized.
The present invention is not limited to the above-described examples, and various changes can be made without departing from the spirit and scope of the present invention within the knowledge of those skilled in the art.
Claims (9)
1. A BIM-based beam bridge multi-source heterogeneous data fusion decision making system is characterized by comprising: the system comprises an operation and maintenance basic data unit, a BIM visual model unit, a disease intelligent acquisition unit, a disease maintenance tracking unit, a technical condition evaluation unit, a structure monitoring and early warning unit, a load test analysis unit, a bearing capacity test unit, a maintenance investment decision unit, a virtual reality demonstration unit, a cloud server and a client;
the operation and maintenance basic data unit comprises highway bridge and culvert maintenance specifications, JTG H11-2004 and appendix A bridge basic department content, and is used for maintaining annual charge per kilometer calculated by investment decision, determining whether to overload road sections and determining whether to transport passage information of large pieces;
the BIM visual model unit is used for displaying the bridge structure in an imaging mode according to the actual size by applying a lightweight technology, converting three-dimensional information of diseases into two-dimensional information for displaying, displaying technical condition evaluation grades of different components, displaying a monitoring and early warning sensing system and real-time data, displaying a load test distribution scheme and corresponding structural response, and displaying the vibration mode and frequency acquired by a load test; the three-dimensional information of the diseases is converted into two-dimensional information for displaying, the structure surface attribution information and the coordinate information in the disease position description are combined with the BIM model, the structure surface and the specific position of the diseases in the model are accurately positioned, and roaming checking is realized;
the disease intelligent acquisition unit comprises a structure analysis standard, a position description standard, a characteristic description standard, an image file standard, an evaluation model library and an AI image recognition algorithm, wherein the evaluation model library comprises a component library, a sub-component library and a disease type library;
the disease maintenance tracking unit establishes the incidence relation of diseases with different time attributes, is used for recording the change of disease characteristics only during field inspection to improve the field inspection efficiency, and simultaneously realizes the development tracking and prediction of the disease length, width, area and maximum seam width characteristics according to a time axis so as to track and evaluate maintenance measures;
the technical condition evaluation unit automatically scores components, parts and parts according to the stability of diseases and generates a disease development graph and an evaluation report according to the technical condition evaluation standard of highway bridges, JTG T H21-2011;
the structure monitoring and early warning unit is used for pertinently deploying deformation, strain and fundamental frequency test elements aiming at the beam bridge with low evaluation score of the technical conditions of the upper structure and the lower structure, monitoring the mechanical behavior change of the structure, and setting an early warning threshold value to early warn abnormal response;
the load test analysis unit is used for evaluating a beam bridge with a lower score aiming at the technical condition that the structural monitoring and early warning unit captures the rigidity change of the bearing member or part of the superstructure, developing a load test according to the highway bridge load test regulation, JTG/T J21-01-2015, inputting a detection scheme and a result, automatically calculating a check coefficient and analyzing whether the rigidity of the structure is in a normal range;
the bearing capacity test unit inputs material conditions and state parameter data aiming at a beam bridge with structure displacement or deformation and stress overrun according to the road bridge bearing capacity detection evaluation rule, JTG/T J21-2011, and performs bridge evaluation by combining the result of a load test analysis unit which needs to perform bearing capacity evaluation;
the maintenance investment decision unit aims at maintenance, maintenance and reinforcement treatment, can automatically calculate cost according to disease maintenance and maintenance measures and structural reinforcement treatment measures, realizes maintenance priority ranking based on an analytic hierarchy process, and automatically generates a reasonable distribution scheme with limited expenditure and a reasonable distribution scheme aiming at a special target; the disease maintenance measures are to set maintenance methods, unit prices and units corresponding to different types of diseases of the beam bridge; the structure reinforcement treatment measures are that a reinforcement treatment scheme, unit price and unit which can be selected by the beam bridge are set; the maintenance priority ranking is realized based on the analytic hierarchy process, and a priority ranking algorithm is provided;
the virtual reality demonstration unit is used for viewing the apparent diseases of the bridge through VR roaming in virtual reality, monitoring historical data and analysis results, analyzing and calculating results of a load test, and material conditions and state parameters, and demonstrating a reinforcement treatment scheme adopted by a bridge structure plan;
the data cloud server and the client are used for realizing real-time calling by storing the data in a network and realizing friendly interaction with a user through the WEB client and the mobile client.
2. The BIM-based multi-source heterogeneous data fusion decision-making system for the beam bridge is characterized in that the BIM visual model unit can directly display disease information, structure monitoring measuring points and a testing maximum value, a load test testing position and a checking coefficient, bridge deck real-time traffic flow and vehicle type classification information, distinguish technical condition evaluation results of all components by colors, check corresponding electronic files, material conditions and state parameters after clicking a specific position, support VR roaming and realize the rationality of a reinforcement treatment scheme of a bridge structure in a virtual reality environment; the electronic file comprises construction period drawings, operation period drawings and report data.
3. The BIM-based beam bridge multi-source heterogeneous data fusion decision making system is characterized in that the disease maintenance tracking unit is used for detecting whether all historical diseases are tracked and investigated by adding a field detection completion confirmation function in a data cloud server and a client, marking a crack width test position on a crack key disease field, fusing frequent detection and periodic detection information to realize low-frequency monitoring on the disease development condition, and providing data support for judging the change of the stress characteristic of a structure; and tracking and monitoring the maintained and maintained diseases, realizing tracking and evaluation of maintenance and maintenance measures, and recommending good maintenance and maintenance measures and eliminating poor maintenance and maintenance measures.
4. The BIM-based beam bridge multi-source heterogeneous data fusion decision making system according to claim 1, characterized in that the technical condition evaluation unit performs comprehensive evaluation after judgment on the nature of the disease according to the disease data of frequent inspection and regular inspection in combination with the low-frequency monitoring data of the disease maintenance tracking unit.
5. The BIM-based beam bridge multi-source heterogeneous data fusion decision-making system according to claim 1, characterized in that the maintenance investment decision-making unit is used for performing maintenance, repair and reinforcement treatment, wherein maintenance and repair are supported by using frequent inspection and periodic inspection disease information as basic data, and the operation and maintenance basic data unit is fused for intelligent auxiliary decision-making by using the calculation result of the technical condition evaluation unit as direct support; the reinforcement treatment is based on frequent inspection and regular inspection of apparent diseases, a structure monitoring and early warning unit and a load test analysis unit are integrated, and mid-span deflection evaluation and structure deflection evaluation are realized; when the technical condition evaluation structure of the component is controlled by mid-span deflection and structure deflection, the material condition and state parameter data of the bearing capacity test unit are fused to carry out bearing capacity check calculation; the maintenance investment decision unit follows the following general idea: the priority of reinforcement treatment is higher than that of maintenance and repair; bridges with four or five types of overall and lower structure technical status grades or five types of upper structures need to be treated; the maintenance investment decision unit establishes a maintenance priority evaluation index system based on an analytic hierarchy process, wherein the maintenance priority evaluation index system comprises a safety index and an importance index; wherein the safety indexes comprise: classifying annual charge per kilometer, route grade, bridge position and bridge length; the importance indicators include: the method comprises the following steps of evaluating technical condition total scores, evaluating upper structures, evaluating lower structures, evaluating bridge deck systems, operating years, special structural bridges, frequent annual inspection times, whether road sections are overloaded or not and whether large transportation channels are transported or not; the annual charge per kilometer, the route grade, the bridge position, the bridge length classification, the operation age, the special structure bridge, the annual frequent inspection times, whether the road section is overloaded or not and whether the large transport passage is obtained from the operation and maintenance basic data unit or not; the total technical condition evaluation score, the upper structure evaluation score, the lower structure evaluation score and the bridge deck system evaluation score are all obtained from a technical condition evaluation unit;
the maintenance investment decision unit determines the weight as an importance index of 0.33 and a safety index of 0.67 according to an expert survey method; the annual per kilometer charge amount is 0.58, the route grade is 0.26, the bridge position is 0.11, and the bridge length classification is 0.05; the technical condition evaluation total score is 0.13, the upper structure evaluation score is 0.23, the lower structure evaluation score is 0.11, the bridge deck system evaluation score is 0.03, the operation age is 0.07, the special structural bridge is 0.04, the annual frequent inspection frequency is 0.05, whether the road section is heavily loaded is 0.10, and whether the large transportation channel is 0.24.
6. The BIM-based beam bridge multi-source heterogeneous data fusion decision-making system according to claim 1, wherein the maintenance investment decision-making unit determines values of selected decision parameters according to a 5-scale method, specifically as follows:
the decision index value of charge per kilometer is as follows: 5- [2.5, + ∞), 4- [2.0, 2.5), 3- [1.5, 2.0), 2- [1.0, 1.5), 1- [0, 1.5);
taking the route grade decision index: 5-expressway, 4-first level road, 3-second level road, 2-third level road and 1-fourth level road;
bridge position decision index value: 5-main line bridge, 4-auxiliary road bridge, 3-ramp bridge, 2-overpass, 1-others;
bridge length classification index value: 5-super large bridge, 4-big bridge, 3-middle bridge and 2-small bridge;
the technical condition evaluation total score decision index value: 5- [60, 80), 3- [80, 95), 1- [95, 100 ];
evaluation score decision index value of the upper structure: 5- [40, 60), 4- [60, 80), 2- [80, 95), 1- [95, 100 ];
evaluation score decision index value of the lower structure: 5- [60, 80), 3- [80, 95), 1- [95, 100 ];
evaluation of bridge deck system scoring decision index value: 5- [0, 40), 4- [40, 60), 3- [60, 80), 2- [80, 95), 1- [95, 100 ];
and (3) operation age decision index value: 5- [30, + ∞), 4- [20, 30), 3- [10, 20), 4- [5, 10), 5- [0, 5);
taking the decision index of the special structure bridge: 5-special structural bridge, 1-conventional structural bridge;
frequent inspection decision index values: 5-0, 4- [0, 12), 3- [12, 24), 2- [24, 36), 1- [36, + ∞);
whether the heavily loaded road section decision index takes value: 5-is a heavy-load road section, 1-is a non-heavy-load road section;
whether the decision index of the large transport channel takes value is as follows: 5-is a large transport channel, 1-is a non-large transport channel.
7. The BIM-based beam bridge multi-source heterogeneous data fusion decision making system according to claim 1, wherein the maintenance investment decision making unit supports the designated decision index not to participate in evaluation and specific maintenance objectives, including: the technical conditions of the overall superstructure of the higher road network bridge are evenly divided, the number proportion of the lower three types of bridges and less is lower, and the length proportion of the lower three types of bridges and less is lower.
8. The BIM-based beam bridge multi-source heterogeneous data fusion decision-making system according to claim 1, characterized in that the maintenance investment decision-making unit has the following specific algorithm:
for more positive maintenance expenses SⅡThe bridge maintenance device is used for bridge maintenance with obvious diseases affecting operation safety, reasonable distribution of reinforcement is included, and the following necessary conditions are met: the state of the art is four and five types of bridges, and in three types of bridges the superstructure score is lower than 40 or the substructureBridges with a score below 60 must be repaired;
assuming that the total number of bridges in a road network is n, and the set is A; not only special structural bridges and grand bridges, but also the situation that the evaluation result is four types and five types is set A1(ii) a Except for A1Four and five other types of bridges, set A2(ii) a Not only is a special structure bridge and a super bridge, but also the evaluation result is the case of three types, namely intersection A3(ii) a Except for A3The other three types of bridges are set A4(ii) a Except for A1、A3Bridges and grand bridges of special construction, otherwise, of set A5(ii) a Otherwise set A6；
Maintenance priority A of each aggregate1＞A2＞(A3、A4)＞A5＞A6Wherein A is1、A2Required to repair, A3To be repaired, A4Preferably, part of the bridges in the set have priority higher than A3Investing a total cost SⅡ；A5、A6Selective maintenance with total cost of investment as conditional maintenance cost SIThe method is used for bridge maintenance without obvious damage affecting operation safety;
assumption set AiThe number of the middle element is xi，xi≥0，i＝1～6，
The element bridge is aijBridge aijHas a maintenance cost of sijMaintenance status of cijSet A, where a value of 1 indicates maintenance is determined and a value of 0 indicates non-maintenance is determinediThe maintenance cost of the middle bridge is
The curing is carried out according to the following formula:
to determine A3、A4Maintenance status of middle element cijCalculating a maintenance priority evaluation index according to the decision index level and the weight; index PyHas a weight of WyY is the decision index number, bridge aijEach decision index PyIs taken as py-ijThen bridge aijMaintenance priority score of Iij＝20∑Wypy-ijThe value range is 0-100; when the appointed decision index does not participate in evaluation, the decision index P is introducedyUsing a state parameter dyWhen the participation evaluation value is 1, the non-participation evaluation value is 0, y is the number of the decision index, and the initial weight is W0-yCalculating the weight
A3、A4The maintenance priority scores of the medium elements are sequentially J from large to small1，J2… …, corresponding maintenance cost s1，s2… …, when satisfied
When, J△The corresponding bridge maintenance state is 1, Delta is 1-m, A3、A4The maintenance state of other elements in the system is 0;
for the calculation of the maintenance priority evaluation index considering special requirements, aiming at the specific bridge aijThe following parameters were introduced:
the score contribution index is as follows:
Representing a bridge aijCost and total overhead required for each 1 point increase in the overall superstructure technical status scoreThe ratio of the average levels of all three types of bridges in the road network; dr-ijIs a bridge aijOverall technical condition score, SPCIr-ijIs a bridge aijUpper structure technical status score of (1);
quantitative contribution index:
representing a bridge aijThe ratio of the expenses required for maintenance to the average level of all three types of bridges in the road network;
length contribution index:
representing a bridge aijThe ratio of the expense required by maintenance per linear meter to the average level of all three types of bridges in the road network;
setting the participation coefficient of a specific maintenance target as eα、eβ、eγWhen the maintenance target is required to be met, the value is 1, and when the maintenance target is not required, the value is 0; then bridge aijThe maintenance priority score of (1) is calculated according to the formula:
Iij＝20[1+min{Wy}(eααij+eββij+eγγij)]∑Wypy-ij。
9. the BIM-based beam bridge multi-source heterogeneous data fusion decision making system according to claim 1, characterized in that the virtual reality demonstration unit supports the bridge needing reinforcement treatment to intuitively know the operation condition of the bridge at any time and any place, consults electronic archive data, carries out voice discussion in a platform and simulates an expert field demonstration scene.
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