A bridge risk assessment method based on IFC

Abstract

The invention discloses an IFC-based bridge risk assessment method, which relates to the technical field of bridge engineering, and comprises the following steps: obtaining risk index data of different bridge structures; obtaining an original IFC dictionary file, and performing entity expansion on bridge structure components to form a bridge structure IFC file; performing attribute set expansion on the bridge structure to obtain a basic bridge risk IFC file; inputting risk index data into the basic bridge risk IFC file to obtain a bridge risk IFC file; constructing an assessment model, stipulating risk assessment levels and corresponding risk level index values, calculating risk index data to determine risk level index values ​​of different structures, associating them with corresponding structures, obtaining an updated bridge risk IFC file, and visually displaying the risk assessment levels of bridge structures to obtain bridge risk assessment results; the invention realizes standardized storage of information, visually displays bridge risks, enhances the intuitiveness of risk identification, and improves the efficiency and accuracy of risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge engineering, and in particular to an IFC-based bridge risk assessment method. Background Art

[0002] In the field of bridge risk assessment, traditional assessment methods are often limited by the dispersion and non-standardization of data. Therefore, bridge risk assessment often relies on dispersed data sources and non-standardized data formats, which makes data integration difficult and affects the accuracy of the assessment. Traditional assessment may not have formed a complete analysis system. The risk assessment process may require a lot of manual operations and experience judgments, which is inefficient and lacks the means to intuitively display risk levels, making risk identification less intuitive and difficult to quickly identify high-risk areas, resulting in limited efficiency and accuracy of risk identification and decision support. Summary of the invention

[0003] The present invention provides an IFC-based bridge risk assessment method to overcome the technical problems that the existing methods require a large amount of manual operation and experience judgment when evaluating bridge risks, data integration is difficult, and there is a lack of means to intuitively display risk levels, which makes risk identification less intuitive and difficult to quickly identify high-risk areas, resulting in limited efficiency and accuracy of risk identification and decision support.

[0004] In order to achieve the above object, the technical solution of the present invention is:

[0005] A bridge risk assessment method based on IFC, comprising:

[0006] S1: Obtain risk indicator data of different bridge structures, wherein the risk indicator data includes structural condition evaluation data, functional performance evaluation data, monitoring time data, structural identification data, and structural maintenance and operation evaluation data;

[0007] S2: obtaining an original IFC dictionary file, performing entity expansion on the bridge structure components, wherein the entity expansion is to add bridge structure elements to the original IFC dictionary file to obtain a bridge structure IFC dictionary file, and forming a bridge structure IFC file according to the bridge structure IFC dictionary file;

[0008] S3: Expanding the attribute set of the bridge structure element, wherein the attribute set expansion is to add the bridge risk assessment attribute corresponding to the bridge structure element in the bridge structure IFC file to obtain a basic bridge risk IFC file;

[0009] S4: inputting the risk indicator data into a basic bridge risk IFC file according to the bridge risk assessment attribute to obtain a bridge risk IFC file;

[0010] S5: construct an evaluation model, specify the risk evaluation level and the risk level index value corresponding to the risk evaluation level, calculate the risk index data in the bridge risk IFC file, determine the risk level index values ​​corresponding to different structures, and associate the risk level index values ​​and risk evaluation levels of different structures with the corresponding structures to obtain an updated bridge risk IFC file;

[0011] S6: Based on the risk level index value in the updated bridge risk IFC file, the risk assessment level of each bridge structure is visualized to obtain the bridge risk assessment result.

[0012] Furthermore, S5 constructs an evaluation model, specifies the risk evaluation level and the risk level index value corresponding to the risk evaluation level, calculates the risk index data in the bridge risk IFC file, determines the risk level index values ​​corresponding to different structures, and associates the risk level index values ​​and risk evaluation levels of different structures with the corresponding structures, and obtains an updated bridge risk IFC file, including:

[0013] S51, constructing an evaluation model, calculating the risk index data in the bridge risk IFC file, and obtaining a scatter image corresponding to the risk index data;

[0014] S52, specifying a risk assessment level, and obtaining a plurality of scattered point images corresponding to different risk assessment levels;

[0015] S53. Set a risk level index value for the risk assessment level, compare the scatter image corresponding to the risk index data with multiple scatter images corresponding to different risk assessment levels, obtain the risk assessment level and risk level index value of different structures, associate the risk assessment level and risk level index value of different structures with the corresponding structures, and obtain an updated bridge risk IFC file.

[0016] Furthermore, an evaluation model is constructed to calculate the risk index data in the bridge risk IFC file and obtain the scatter plot corresponding to the risk index data, including:

[0017] S511: Obtain risk index data corresponding to a certain bridge risk assessment attribute in the bridge structure element, and calculate a digital feature group of the risk index data, as shown in formulas (1), (2) and (3).

[0018] (1)

[0019] (2)

[0020] (3)

[0021] Formula (1) is used to solve the evaluation state average of the bridge risk assessment attribute. To evaluate the state average, , represents the bridge risk assessment attribute; Indicates the total number of time periods for risk indicator data monitoring; The risk index data representing the risk assessment attributes of bridges at different time periods, ;

[0022] Formula (2) is used to solve the evaluation state span number of bridge risk assessment attributes, Indicates the number of evaluation state spans;

[0023] Formula (3) is used to solve the evaluation state stability number of the bridge risk assessment attribute, Indicates the evaluation state stability number;

[0024] The digital feature set of risk indicator data is obtained through formulas (1)-(3): ;

[0025] S512: Determine the weight of each bridge risk assessment attribute under the bridge structure element by entropy weight method ;

[0026] S513: Calculation m The overall digital feature group of bridge risk assessment attributes is shown in formulas (4), (5) and (6).

[0027] (4)

[0028] (5)

[0029] (6)

[0030] Formula (4) is used to solve the average number of evaluation states of bridge structural elements. Ta Evaluate condition uniformity for bridge structural elements;

[0031] Formula (5) is used to solve the span number of the bridge structure element evaluation state, Tb Evaluate the number of condition spans for bridge structural elements;

[0032] Formula (6) is used to solve the span number of the bridge structure element evaluation state, Pc Evaluate the number of condition spans for bridge structural elements;

[0033] The overall digital feature set is obtained through formulas (4)-(6) ;

[0034] S514: Ta is the mean, Tbis the standard deviation, randomly generates normal random numbers M ;by Tb is the mean, Pc is the standard deviation, randomly generates normal random numbers N ;

[0035] S515: Setting the membership function, as shown in formula (7),

[0036] (7)

[0037] is the membership function, in which M and N As the independent variable, the coordinate value obtained after output is The scattered coordinates of

[0038] S516: Repeat S514-S515 to obtain a total of Z scattered point coordinate sets, and put the scattered point coordinate sets into the coordinate system to form a risk indicator data scattered point image Q0.

[0039] Furthermore, the risk assessment level is specified, and multiple scatter images corresponding to different risk assessment levels are obtained, including:

[0040] S521: Specify risk assessment levels, including excellent, good, medium, poor and unsatisfactory, and set assessment intervals for each risk assessment level;

[0041] S522: Calculate the digital feature group of the standard monitoring and evaluation data, as shown in formulas (8), (9) and (10),

[0042] (8)

[0043] (9)

[0044] (10)

[0045] Formula (8) is used to solve the standard evaluation state mean of standard monitoring and evaluation data, is the average value of the standard evaluation status of the standard monitoring and evaluation data, and are the minimum and maximum values ​​for each specified evaluation interval respectively;

[0046] Formula (9) is used to solve the standard evaluation state span of standard monitoring and evaluation data, The number of state spans for the standard assessment;

[0047] Formula (10) is used to solve the standard evaluation state stability number of standard monitoring and evaluation data, is the standard evaluation state stability number, u is a constant;

[0048] The digital feature group is formed by formulas (8)-(10) ;

[0049] S523: Execute steps S514-S516 on the digital feature group of the standard monitoring evaluation data of each evaluation interval, and Ta Replace with ,Will Tb Replace with ,Will Pc Replace with , calculate and form 5 scatter images Q1-Q5 of standard monitoring and evaluation data corresponding to excellent, medium, poor and unqualified.

[0050] Further, a risk level index value is set for the risk assessment level, and the scatter image corresponding to the risk index data is compared with multiple scatter images corresponding to different risk assessment levels to obtain the risk assessment level and risk level index value of different structures, and the risk assessment level and risk level index value of different structures are associated with the corresponding structures to obtain an updated bridge risk IFC file, including:

[0051] S531: Setting a risk level indicator value for the risk assessment level;

[0052] S532: superimpose the scattered point images of Q0-Q5, compare them, check the overlap between Q0 and Q1-Q5, obtain the maximum overlap, and obtain the risk assessment level of different structures according to the maximum overlap;

[0053] S533: Associating the risk assessment levels and risk level index values ​​of different structures with corresponding bridge structure elements to obtain an updated bridge risk IFC file.

[0054] Further, the original IFC dictionary file is obtained, and the bridge structure components are physically expanded. The physical expansion is to add bridge structure elements to the original IFC dictionary file to obtain a bridge structure IFC dictionary file. The bridge structure IFC file is formed according to the bridge structure IFC dictionary file, including:

[0055] S21, obtaining the original IFC dictionary file;

[0056] S22. Based on the IFC standard, define the bridge space in the original IFC dictionary file;

[0057] S23, performing entity expansion on the bridge structure components, that is, adding the bridge structure elements into the bridge space to obtain the bridge structure IFC dictionary file; the bridge structure IFC dictionary file contains the content required for the bridge structure to form an IFC file;

[0058] S24. Form a bridge structure IFC file according to the required contents of the bridge structure in the bridge structure IFC dictionary file.

[0059] Furthermore, the attribute set of the bridge structure element is expanded. The attribute set expansion is to add the bridge risk assessment attributes corresponding to the bridge structure element in the bridge structure IFC file to obtain the basic bridge risk IFC file, including:

[0060] S31, read the bridge structure IFC file, and select the specified bridge structure element;

[0061] S32. Expand the attribute set of the bridge structure element, that is, add the risk assessment attribute to the corresponding bridge structure element to obtain the basic bridge risk IFC file.

[0062] Beneficial effects: The present invention provides an IFC-based bridge risk assessment method, which realizes standardized storage of information by adding IFC storage standards for various bridge families and their risk assessment index attributes, thereby solving the problems of data dispersion and non-standardization;

[0063] By combining risk indicator data with IFC files, the risks of bridge structures can be visualized, making risk identification more intuitive.

[0064] By designing an assessment model, the risk indicator data is calculated to obtain the risk level indicator values ​​corresponding to different structures, which are saved in the expanded IFC file, realizing deep mining and intelligent processing of information, and improving the efficiency and accuracy of risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0066] Figure 1 A method flow chart of a bridge risk assessment method based on IFC according to the present invention;

[0067] Figure 2 This is a schematic diagram of the IFC file of the bridge space structure of the present invention;

[0068] Figure 3 This is a schematic diagram of the IFC file of the bridge physical element of the present invention;

[0069] Figure 4 This is a scatter plot comparing risk assessment levels according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] This embodiment provides a bridge risk assessment method based on IFC, such as Figure 1 As shown, including:

[0072] S1: Obtain risk indicator data of different bridge structures, wherein the risk indicator data includes structural condition evaluation data, functional performance evaluation data, monitoring time data, structural identification data, and structural maintenance and operation evaluation data;

[0073] S2: obtaining an original IFC dictionary file, performing entity expansion on the bridge structure components, wherein the entity expansion is to add bridge structure elements to the original IFC dictionary file to obtain a bridge structure IFC dictionary file, and forming a bridge structure IFC file according to the bridge structure IFC dictionary file;

[0074] S3: Expanding the attribute set of the bridge structure element, wherein the attribute set expansion is to add the bridge risk assessment attribute corresponding to the bridge structure element in the bridge structure IFC file to obtain a basic bridge risk IFC file;

[0075] S4: inputting the risk indicator data into a basic bridge risk IFC file according to the bridge risk assessment attribute to obtain a bridge risk IFC file;

[0076] S5: construct an evaluation model, specify the risk evaluation level and the risk level index value corresponding to the risk evaluation level, calculate the risk index data in the bridge risk IFC file, determine the risk level index values ​​corresponding to different structures, and associate the risk level index values ​​and risk evaluation levels of different structures with the corresponding structures to obtain an updated bridge risk IFC file;

[0077] S6: Based on the risk level index value in the updated bridge risk IFC file, the risk assessment level of each bridge structure is visualized to obtain the bridge risk assessment result.

[0078] Specifically, first, risk indicator data of different bridge structures are obtained, and the risk indicator data include structural condition evaluation data, functional performance evaluation data, monitoring time data, structural identification data and structural maintenance and operation evaluation data. Risk indicator data is generated by monitoring various bridge data, so that more complete and accurate bridge risk evaluation results can be obtained later; secondly, the original IFC dictionary file is obtained, and the bridge structure components are entity expanded. The entity expansion is to add bridge structure elements to the original IFC dictionary file to obtain a bridge structure IFC dictionary file. A bridge structure IFC file is formed according to the bridge structure IFC dictionary file. By adding bridge structure elements to the original IFC dictionary file, an IFC dictionary file about the bridge can be obtained, which is more convenient for subsequent calls; the attribute set of the bridge structure elements is expanded. The attribute set expansion is to add bridge risk evaluation attributes corresponding to the bridge structure elements to the bridge structure IFC file to obtain a basic bridge risk IFC file. By adding the IFC storage standards of various bridge families and their risk evaluation indicator attributes, standardized storage of information is realized, thereby solving the problems of data dispersion and non-standardization; according to the bridge risk evaluation attributes, the risk indicator data is input into the basic In the bridge risk IFC file based on the existing data, the bridge risk IFC file is obtained, which can associate the attributes with the corresponding risk indicator data, so as to facilitate the subsequent calculation of the risk level index values ​​of different structures and correspond them to the attributes and structures; an evaluation model is constructed, the risk assessment level and the risk level index value corresponding to the risk assessment level are specified, the risk index data in the bridge risk IFC file is calculated, the risk level index values ​​corresponding to different structures are determined, and the risk level index values ​​and risk assessment levels of different structures are associated with the corresponding structures to obtain an updated bridge risk IFC file. After calculation using the data obtained by monitoring, the results are stored in the expanded IFC file, which realizes the deep mining and intelligent processing of information and improves the efficiency and accuracy of risk assessment; finally, based on the risk level index value in the updated bridge risk IFC file, the risk assessment level of each bridge structure is visualized to obtain the bridge risk assessment result, the risk assessment level of different bridge structures in the IFC file is analyzed, and the different structures of the bridge model are automatically highlighted in different colors, so that each part of the bridge can be intuitively distinguished according to its risk level, and the risk assessment of the bridge is realized.

[0079] In a specific embodiment, a scheme for obtaining risk indicator data of different bridge structures, wherein the risk indicator data includes structural condition evaluation data, functional performance evaluation data, monitoring time data, structural identification data, and structural maintenance and operation evaluation data is:

[0080] Divide the bridge into three parts: upper, middle and lower:

[0081] The bridge superstructure includes cable system, box girder system, pylon system and anchor system;

[0082] The data that needs to be collected for the cable system include: cable aging and wear assessment, aging degree of materials in the casing, stability assessment of anchor points and connectors, slip displacement, cable number, and collection time;

[0083] The data that need to be collected for the box girder system include: fatigue and corrosion assessment of box girder connection points, vibration and deformation assessment of box girder, acceleration assessment of box girder, bolt slippage, collection time, and box girder number;

[0084] The data that needs to be collected for the tower system include: corrosion, looseness, wear of connection nodes, tower vibration frequency and amplitude, cracks and damage of the tower concrete structure, collection time, and tower number;

[0085] The data that needs to be collected for the anchor system include: anchor corrosion and damage, anchor structure vibration and deformation, whether there are cracks in the anchor, collection time, and anchor number;

[0086] The data collected from the middle structure of the bridge include: wear and damage of the bridge deck surface, condition of the bridge deck drainage system, condition of guardrails and guardrails, maintenance of bridge deck lighting facilities, bridge deck expansion joints, and collection time;

[0087] The bridge substructure includes the pier system, abutment system, and pier-abutment system;

[0088] The data that needs to be collected for the pier system include: pier structure monitoring and evaluation, pier foundation drainage and anti-corrosion, pier support inspection, collection time, and pier number;

[0089] The data that needs to be collected for the abutment system include: abutment structure monitoring and evaluation, abutment foundation drainage and anti-corrosion, abutment bearing inspection, collection time, and abutment number;

[0090] The data that needs to be collected by the pier system include: aging and corrosion of pier structures, monitoring of pier settlement and deformation, soil loss and erosion at the piers, collection time, and pier numbers;

[0091] The types of data collected can be generally divided into the following categories:

[0092] 1. Structural condition evaluation data: including indicators such as corrosion, wear, cracks, and aging of various parts of the bridge. This type of data is key data for evaluating the structural integrity of the bridge;

[0093] 2. Functional performance evaluation data: dynamic response indicators of the bridge, such as vibration, deformation, displacement, acceleration, etc., used to evaluate the functional performance of the bridge during use;

[0094] 3. Monitoring time data: including the time when bridge monitoring data was collected, used to record changes in bridge status and the course of evaluation;

[0095] 4. Identification data: including cable number, box girder number, pylon number, anchor number, pier number, abutment number, etc. This type of data helps to uniquely identify and track each part of the bridge;

[0096] 5. Maintenance and operation data: including data on the condition of the bridge deck drainage system, the condition of guardrails and guardrails, the maintenance of bridge deck lighting facilities, and the condition of facilities directly related to bridge operation. This type of data reflects the maintenance needs and operation status of the bridge during long-term use;

[0097] The main types of manually collected data are bridge detail assessment issues, including but not limited to corrosion, looseness, and wear of connection nodes, wear and damage of bridge deck surfaces, conditions of guardrails and guardrails, etc.; the main types of automatically collected data are bridge structure issues, including but not limited to slippage and displacement, vibration and deformation of anchor structures, and pier settlement and deformation monitoring.

[0098] Monitoring various bridge data generates risk indicator data, which can subsequently provide more complete and accurate bridge risk assessment results.

[0099] In a specific embodiment, an original IFC dictionary file is obtained, and a bridge structure component is physically expanded. The physical expansion is to add a bridge structure element to the original IFC dictionary file to obtain a bridge structure IFC dictionary file. A scheme for forming a bridge structure IFC file according to the bridge structure IFC dictionary file is:

[0100] S21, obtaining the original IFC dictionary file;

[0101] S22. Based on the IFC standard, define the bridge space in the original IFC dictionary file;

[0102] S23, performing entity expansion on the bridge structure components, adding the bridge structure elements into the bridge space, and obtaining an IFC dictionary file of the bridge structure; the IFC dictionary file of the bridge structure contains the content required for forming an IFC file of the bridge structure;

[0103] S24, forming a bridge structure IFC file according to the required content of the bridge structure in the bridge structure IFC dictionary file;

[0104] This solution takes the bridge superstructure as an example and expands the entity "cable system", which is not defined in the IFC standard:

[0105] First, obtain the original IFC dictionary file. Based on the IFC standard, define the bridge space in the original IFC dictionary file. After clarifying the inheritance relationship in the IFC model framework, customize the "IfcCable" entity. Use the EXPRESS language to complete the definition of the new bridge entity and its corresponding type enumeration. On this basis, add the customized bridge entity and type to its parent object. At the same time, you need to add attributes such as type enumeration, such as Figure 2 As shown, Figure 2 It is a schematic diagram of the newly added entity determined according to the specific structure of the bridge and the inheritance relationship of the entity in the IFC model framework. The solid-line box in the figure is part of the entity of the existing IFC, and the content in the dotted-line box is the newly added entity determined according to the specific structure of the bridge and the inheritance relationship in the IFC model framework;

[0106] First, in the subclasses derived from all IFC spatial structure unit classes (IfcSpatialStructuralElement), the entity representing the overall space of the bridge (IfcBridge) and the entity representing the distribution space of the bridge (IfcBridgePart) are expanded to describe and express the spatial structure of the bridge, and the bridge form type attribute (IfcBridgeTypeEnum) is added through the predefined type attribute definition method (PreDefinedType).

[0107] By adding bridge structure elements to the original IFC dictionary file, an IFC dictionary file about the bridge can be obtained, which is more convenient for subsequent calls.

[0108] In a specific embodiment, the attribute set of the bridge structure element is expanded, and the attribute set expansion is to add the bridge risk assessment attribute corresponding to the bridge structure element in the bridge structure IFC file, and the scheme for obtaining the basic bridge risk IFC file is:

[0109] S31, read the bridge structure IFC file, and select the specified bridge structure element;

[0110] S32, expanding the attribute set of the bridge structure element, that is, adding the risk assessment attribute to the corresponding bridge structure element, and obtaining the basic bridge risk IFC file. The basic bridge risk IFC file only includes the names of the bridge structure element and the risk assessment attribute, and lacks the risk indicator data corresponding to the risk assessment attribute.

[0111] This solution uses Visual Studio 2022 as the development platform, based on the .NET Framework 4.7.2 framework, using the open source .NET software development kit, and using the C# language to develop the attribute set and its attribute extension information integration program;

[0112] Specific as Figure 3 As shown in the figure, add the physical civil engineering field element (IfcCivilElement) under the general class of IFC physical bridge structure elements (IfcElement) to make it at the same level as the building element entity (IfcBuildingElement). Then, based on IfcCivilElement, expand the abstract entity of bridge structure physical element (IfcBridgeElement) as the parent class of all physical component entities of the bridge. Under IfcBridgeElement, derive the physical elements that constitute the bridge structure, such as cable (IfcCable), box girder (IfcBeam), tower (IfcPylon), anchor (IfcanChorage), pier (IfcPier), abutment (IfcAbutment), pier (IfcAbutmentPier), bridge deck system and accessories (IfcAppurtenance), to describe the bridge structure.

[0113] By adding IFC storage standards for various bridge families and their risk assessment index attributes, standardized storage of information is achieved, thus solving the problems of data dispersion and non-standardization.

[0114] In a specific embodiment, the risk indicator data is input into a basic bridge risk IFC file according to the bridge risk assessment attribute, and the scheme for obtaining the bridge risk IFC file is:

[0115] The risk indicator data in S1 is stored in the monitoring database of SQL Server, and the risk indicator data is input into the corresponding attribute according to the IFC bridge risk assessment attribute established by the extended entity.

[0116] For example, if the monitoring data value is 15mm, it is matched with the newly extended attribute "slider displacement", that is, the form of slider displacement-15mm. Then, through the steps of parsing the IFC file, the risk indicator data in S1 is stored in the newly extended IFC bridge risk assessment attribute.

[0117] By adding risk indicator data to the IFC file, the attributes can be associated with the corresponding risk indicator data, which facilitates the subsequent calculation of risk level indicator values ​​of different structures and the correspondence between attributes and structures.

[0118] In a specific embodiment, an evaluation model is constructed, risk evaluation levels and risk level index values ​​corresponding to the risk evaluation levels are specified, risk index data in the bridge risk IFC file is calculated, risk level index values ​​corresponding to different structures are determined, and risk level index values ​​and risk evaluation levels of different structures are associated with corresponding structures. The scheme for obtaining an updated bridge risk IFC file is:

[0119] S51. Construct an evaluation model, calculate the risk index data in the bridge risk IFC file, and obtain a scatter plot corresponding to the risk index data:

[0120] S511: Obtain risk index data corresponding to a certain bridge risk assessment attribute in the bridge structure element:

[0121] Take the bridge risk IFC file as input file, use IFC Engine DLL to parse and read the IFC file, and search for the statements in the IFC file;

[0122] Determine whether the statement in the search for bridge risk IFC file contains content describing bridge structural elements and bridge risk assessment attributes. If yes, proceed to the next step; otherwise, return to the previous step to search again.

[0123] Record the number of each bridge risk assessment attribute under the bridge structure element, and call out the risk indicator data stored in the IFC under each structural part according to the number of the bridge risk assessment attribute;

[0124] Calculate the digital feature set of risk indicator data, as shown in formulas (11), (12) and (13),

[0125] (11)

[0126] (12)

[0127] (13)

[0128] Formula (11) is used to solve the evaluation state average of the bridge risk assessment attribute, To evaluate the state average, , represents the bridge risk assessment attribute; n Indicates the total number of time periods for risk indicator data monitoring; The risk index data representing the risk assessment attributes of bridges at different time periods, ;

[0129] Formula (12) is used to solve the evaluation state span number of bridge risk assessment attributes, Indicates the number of evaluation state spans;

[0130] Formula (13) is used to solve the evaluation state stability number of the bridge risk assessment attribute, Indicates the evaluation state stability number;

[0131] The numerical feature set of risk indicator data is obtained through formulas (11)-(13): ;

[0132] S512: Determine the weight of each bridge risk assessment attribute under the bridge structure element by entropy weight method :

[0133] Shared h Experts scored, m The entropy method is used to calculate the risk assessment attributes of a bridge. The calculation process is as follows:

[0134] Calculate the i Under the bridge risk assessment attribute, j The proportion of the evaluation objects is shown in formula (14).

[0135] (14)

[0136] in, express i Under the bridge risk assessment attribute, j The proportion of evaluation objects; represents the evaluation matrix;

[0137] For i The attribute entropy value of the bridge risk assessment item is calculated as shown in formula (15):

[0138] (15)

[0139] in, Indicates i The attribute entropy value of bridge risk assessment of item, k=1 / ln(h) , is a constant, thus ensuring ;

[0140] Calculate the i The difference coefficient of the bridge risk assessment attribute is as shown in formula (16):

[0141] (16)

[0142] in, It represents the coefficient of difference. The coefficient of difference can indicate the importance of the selected indicator. The larger the coefficient of difference is, the more important the indicator is and the greater the impact of the indicator on the evaluation system is.

[0143] Calculate the weight of each bridge risk assessment attribute , as shown in formula (17),

[0144] (17)

[0145] S513: Calculation mThe overall digital feature group of bridge risk assessment attributes is shown in formulas (18), (19) and (20).

[0146] (18)

[0147] (19)

[0148] (20)

[0149] Formula (18) is used to solve the average number of evaluation states of bridge structural elements, Ta Evaluate condition uniformity for bridge structural elements;

[0150] Formula (19) is used to solve the span number of the bridge structure element evaluation state, Tb Evaluate the number of condition spans for bridge structural elements;

[0151] Formula (20) is used to solve the span number of the bridge structure element evaluation state, Pc Evaluate the number of condition spans for bridge structural elements;

[0152] The overall digital feature set is obtained by formulas (18)-(20): ;

[0153] S514: Ta is the mean, Tb is the standard deviation, randomly generates normal random numbers M ;by Tb is the mean, Pc is the standard deviation, randomly generates normal random numbers N ;

[0154] S515: Setting the membership function, as shown in formula (21),

[0155] (twenty one)

[0156] is the membership function, in which M and N As the independent variable, the coordinate value obtained after output is The scattered coordinates of

[0157] S516: Repeat S514-S515 to obtain a total of Z scattered point coordinate sets, establish a coordinate system based on the evaluation standard membership as the vertical coordinate and the evaluation interval as the horizontal coordinate, put the scattered point coordinate set into the coordinate system to form a risk indicator data scattered point image Q0. In this embodiment, the value of Z is 5000, that is, 5000 scattered point coordinates are obtained;

[0158] S52. Specify the risk assessment level, and obtain multiple scatter point images corresponding to different risk assessment levels:

[0159] S521: Specify risk assessment levels, which include excellent, good, medium, poor and unsatisfactory, and set assessment intervals for each risk assessment level:

[0160] The evaluation interval scores set in this embodiment are shown in Table 1.

[0161] Table 1 Evaluation interval score table

[0162]

[0163] S522: Calculate the digital feature group of the standard monitoring and evaluation data, as shown in formulas (22), (23) and (24),

[0164] (twenty two)

[0165] (twenty three)

[0166] (twenty four)

[0167] Formula (22) is used to solve the standard evaluation state mean of standard monitoring and evaluation data, is the average value of the standard evaluation status of the standard monitoring and evaluation data, and are the minimum and maximum values ​​for each specified evaluation interval respectively;

[0168] Formula (23) is used to solve the standard assessment state span of standard monitoring and evaluation data, The number of state spans for the standard assessment;

[0169] Formula (24) is used to solve the standard evaluation state stability number of standard monitoring and evaluation data, is the standard evaluation state stability number, u is a constant, take 0.1;

[0170] The digital feature group is formed by formulas (22)-(24) ;

[0171] S523: Execute steps S514-S516 on the digital feature group of the standard monitoring evaluation data of each evaluation interval, and Ta Replace with ,Will Tb Replace with ,Will Pc Replace with , calculate and form 5 standard monitoring evaluation data scatter images Q1-Q5 corresponding to excellent, medium, poor and unqualified;

[0172] S53, setting a risk level index value for the risk assessment level, comparing the scatter image corresponding to the risk index data with multiple scatter images corresponding to different risk assessment levels, obtaining the risk assessment levels and risk level index values ​​of different structures, associating the risk assessment levels and risk level index values ​​of different structures with the corresponding structures, and obtaining an updated bridge risk IFC file:

[0173] S531: Setting the risk level index value for the risk assessment level, as shown in Table 2,

[0174] Table 2 Risk level index value table

[0175]

[0176] S532: Overlay the scattered point images of Q0-Q5 for comparison, check the overlap between Q0 and Q1-Q5 respectively, to obtain the maximum overlap, and obtain the risk assessment level of different structures according to the maximum overlap. In this embodiment, Q1 represents excellent, Q2 represents good, Q3 represents medium, Q4 represents poor, and Q5 represents unsatisfactory. The maximum overlap refers to which evaluation interval has the highest overlap with Q0, and the evaluation interval with the highest overlap is used as the risk level of the structure corresponding to Q0.

[0177] S533: Associate the risk assessment levels and risk level index values ​​of different structures with the corresponding structures to obtain an updated bridge risk IFC file:

[0178] Using the IFC file as input file, the IFC Engine DLL is used to parse and read the IFC file, and the newly extended entities in the IFC file, namely the bridge structure elements, are searched;

[0179] The risk level index value is used as the attribute value of the bridge structure element, and the corresponding IFC attribute statement describing the risk assessment level is written;

[0180] Associate the risk assessment level IFC attribute statement written in the previous step with the corresponding structure, write the written attribute statement and association statement into the IFC file, and synchronously update the risk assessment level data into the IFC file.

[0181] After calculation using the data obtained from monitoring, the risk level index values ​​corresponding to different structures are obtained and saved in the expanded IFC file, which realizes the deep mining and intelligent processing of information and improves the efficiency and accuracy of risk assessment.

[0182] In a specific embodiment, the risk evaluation level of each bridge structure is visualized based on the risk level index value in the updated bridge risk IFC file, and the scheme for obtaining the bridge risk evaluation result is:

[0183] The Revit secondary development form of the modeling software Revit API is used to find the color codes corresponding to the risk assessment level index values ​​in the bridge BIM model; and these selected color codes are batch processed and replaced to indicate the risk level of the structure, which is convenient for management and maintenance personnel and designers to manage the operation and maintenance of the bridge. The specific process is as follows:

[0184] S61: Create a FilteredElementCollector object fillPatternElementFilter in the Revit API to filter elements from the Revit document doc and select the structural components to be displayed;

[0185] S62: Use the .OfClass method to filter out all elements of the FillPatternElement type, that is, filter out all structural components with basic colors;

[0186] S63: Use the .First method to select the structural member whose fill mode is solid fill (IsSolidFill) and the ID of the corresponding solid fill pattern element from the filtered set;

[0187] S64: Create an OverrideGraphicSettings object, which is used to store and apply color override setting parameters. Color override means overwriting the base color with the risk level color;

[0188] S65: Set the parameters in the ID in S64 according to the color override setting parameters of the overrideGraphicSettings object to complete the replacement:

[0189] The projection fill color of the overrideGraphicSettings object is set according to the risk assessment level index value, that is, its RGB value is adjusted according to the risk assessment level index value;

[0190] Apply the modified color override setting parameters overrideGraphicSettings to the structural components specified in the active view to change the color of the structural components.

[0191] In this solution, the modeling software parses the risk level index value in the IFC file and automatically renders different colors for each part of the bridge model to distinguish and express the risk level, realize visual risk assessment of the bridge, and ensure accurate control of the risky parts of the bridge.

[0192] Take the middle structure of a bridge as an example to conduct risk assessment:

[0193] According to the characteristics of the middle structure of the bridge, the entity is expanded. Taking the expansion of the bridge deck system and its attached entities and attributes as an example, the specific expansion content is shown in Table 3.

[0194] Table 3

[0195]

[0196] The entities and attributes of the IFC file in the risk assessment of the bridge deck system and its accessories are expanded. The expanded attributes cover 5 risk assessment indicators, and the monitoring frequency of each indicator evaluation data is 24 hours / time. According to the field measurement, the evaluation data of the 5 risk assessment indicators were collected for 14 days, as shown in Table 4.

[0197] Table 4

[0198]

[0199] The evaluation data under each risk evaluation index are respectively input into the evaluation model constructed by the present invention to obtain the evaluation state average number, evaluation state span number and evaluation state stability number of each index:

[0200] Take the attribute "bridge surface wear and damage" as an example:

[0201] ,

[0202]

[0203] ,

[0204] The result of calculation is that the average value of the evaluation state of the attribute "bridge surface wear and damage" is 87.923, the span value of the evaluation state is 1.281, and the stability value of the evaluation state is 0.661. The other attributes are calculated in the same way, and the results are shown in Table 5:

[0205] Table 5

[0206]

[0207] By conducting a questionnaire survey on experts in the field of bridge risk assessment, the entropy method was used to obtain the corresponding weights of each attribute of the extended entity, as shown in Table 6.

[0208] Table 6

[0209]

[0210] The overall digital characteristic group of the bridge structure elements is calculated, and the calculation process is as follows:

[0211] ,

[0212] ,

[0213] ,

[0214] ,

[0215] The overall digital feature group of the standard evaluation interval is calculated by the above formula. Taking the interval [0~40) as an example, the calculation process is as follows:

[0216] ,

[0217] ,

[0218] ,

[0219] The overall digital feature group of the overall standard evaluation interval is shown in Table 7:

[0220] Table 7 Overall digital feature group

[0221]

[0222] The overall digital feature group of the bridge structure elements and the overall digital feature group of the standard evaluation interval are respectively introduced into the membership function formula, and the parameters in the formula are replaced according to different digital feature groups. N is set to 5000, and the scatter image of the risk level data obtained is compared with the five scatter images of the standard evaluation interval, so as to obtain better visibility to view the risk evaluation level of the bridge deck system and its affiliated structures, such as Figure 4 As shown;

[0223] Depend on Figure 4 From the comparison of the scatter plots, we can see that the scatter image corresponding to the risk index data of this indicator is light blue. The overlap between the scatter image and the scatter images corresponding to the five evaluation intervals is judged respectively. From the figure, we can see that the evaluation interval corresponding to the maximum overlap is good. Therefore, the evaluation level of the risk of the bridge deck system and its affiliated structures is the "good" interval, and the state span number and state stability number of the bridge deck system and its affiliated structures are small, which proves that the fluctuation range of the evaluation results for this attribute is small and relatively stable, with a certain degree of credibility. Therefore, the risk level of the "bridge deck system and its affiliated structures" is "2" (good);

[0224] By using IFC Engine DLL to parse and read the IFC file, search for the entity of "bridge deck system and its affiliated structures" in the IFC file. Then, the risk level index value "2" is used as the attribute value of the risk level, and the corresponding IFC attribute statement describing the risk assessment level is written. By associating the risk assessment level IFC attribute statement with the "bridge deck system and its affiliated structures" entity. Finally, the attribute statement and the association statement are written into the IFC file to synchronize the risk assessment level data to the IFC file.

[0225] After saving the risk assessment level values ​​of the bridge deck system and its accessory structures to the bridge risk IFC file, a FilteredElementCollector object fillPatternElementFilter is created using the modeling software Revit API to filter elements from the Revit document doc. The .OfClass method is used to filter out all elements of the FillPatternElement type, that is, to filter out all structural components with basic colors. The .First method is used to select structural components whose fill mode is solid fill (IsSolidFill) and the corresponding solid fill pattern element ID from the filtered collection; an OverrideGraphicSettings object is created, and the parameters in the ID in S64 are set according to the color override setting parameters of the overrideGraphicSettings object to complete the replacement.

[0226] According to the risk assessment level index value "2", the projection fill color of the overrideGraphicSettings object is set to "orange", that is, its RGB value is adjusted according to the risk assessment level index value. Apply the modified color override setting parameter overrideGraphicSettings to the structural component specified in the active view to change the color of the structural component.

[0227] In this way, the modeling software can automatically render different parts of the bridge model in different colors by parsing the risk level index values ​​in the IFC file, so as to distinguish and express the risk level, realize visual risk assessment of the bridge, and ensure accurate control of the risky parts of the bridge.

[0228] The present invention has the following effects:

[0229] 1. Data integration and standardization: In traditional methods, bridge risk assessment often relies on scattered data sources and non-standardized data formats, which makes data integration difficult and affects the accuracy of assessment. This invention achieves standardized storage of information by adding IFC storage standards for various bridge families and their risk assessment index attributes, thereby solving the problem of data dispersion and non-standardization.

[0230] 2. Intuitiveness of risk assessment: Traditional assessment methods may lack the means to intuitively display risk levels, making risk identification less intuitive and difficult to quickly identify high-risk areas. The present invention uses monitoring data, combined with IFC files, to automatically highlight different structures of the bridge model in different colors through BIM modeling software, thereby enhancing the intuitiveness of risk identification.

[0231] 3. Intelligent processing: Traditional methods may require a lot of manual operations and empirical judgments in the risk assessment process, which is inefficient. The method proposed in the present invention uses intelligent processing to calculate the risk assessment results obtained by monitoring data and saves them in the expanded IFC file, thus realizing deep mining and intelligent processing of information and improving the efficiency and accuracy of risk assessment.

[0232] 4. Systematicity of risk assessment: Traditional assessment may not form a complete analysis system. However, the present invention, through the proposed evaluation method and combined with the data under the IFC standard, systematically analyzes and evaluates the risk factors in the bridge construction process, providing a new evaluation perspective.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bridge risk assessment method based on IFC, characterized in that: include: S1: Obtain risk indicator data of different bridge structures, wherein the risk indicator data includes structural condition evaluation data, functional performance evaluation data, monitoring time data, structural identification data, and structural maintenance and operation evaluation data; S2: obtaining an original IFC dictionary file, performing entity expansion on the bridge structure components, wherein the entity expansion is to add bridge structure elements to the original IFC dictionary file to obtain a bridge structure IFC dictionary file, and forming a bridge structure IFC file according to the bridge structure IFC dictionary file; S3: Expanding the attribute set of the bridge structure element, wherein the attribute set expansion is to add the bridge risk assessment attribute corresponding to the bridge structure element in the bridge structure IFC file to obtain a basic bridge risk IFC file; S4: inputting the risk indicator data into a basic bridge risk IFC file according to the bridge risk assessment attribute to obtain a bridge risk IFC file; S5: construct an evaluation model, specify the risk evaluation level and the risk level index value corresponding to the risk evaluation level, calculate the risk index data in the bridge risk IFC file, determine the risk level index values ​​corresponding to different structures, and associate the risk level index values ​​and risk evaluation levels of different structures with the corresponding structures to obtain an updated bridge risk IFC file; S6: Based on the risk level index value in the updated bridge risk IFC file, the risk assessment level of each bridge structure is visualized to obtain the bridge risk assessment result.

2. The IFC-based bridge risk assessment method according to claim 1, characterized in that: S5 constructs an evaluation model, specifies the risk assessment level and the risk level index value corresponding to the risk assessment level, calculates the risk index data in the bridge risk IFC file, determines the risk level index values ​​corresponding to different structures, and associates the risk level index values ​​and risk assessment levels of different structures with the corresponding structures to obtain an updated bridge risk IFC file, including: S51, constructing an evaluation model, calculating the risk index data in the bridge risk IFC file, and obtaining a scatter image corresponding to the risk index data; S52, specifying a risk assessment level, and obtaining a plurality of scattered point images corresponding to different risk assessment levels; S53. Set a risk level index value for the risk assessment level, compare the scatter image corresponding to the risk index data with multiple scatter images corresponding to different risk assessment levels, obtain the risk assessment level and risk level index value of different structures, associate the risk assessment level and risk level index value of different structures with the corresponding structures, and obtain an updated bridge risk IFC file.

3. The IFC-based bridge risk assessment method according to claim 2 is characterized in that: An evaluation model is constructed to calculate the risk index data in the bridge risk IFC file and obtain the scatter plot corresponding to the risk index data, including: S511: Obtain risk index data corresponding to a certain bridge risk assessment attribute in the bridge structure element, and calculate a digital feature group of the risk index data, as shown in formulas (1), (2) and (3). （1） （2） （3） Formula (1) is used to solve the evaluation state average of the bridge risk assessment attribute. To evaluate the state average, , represents the bridge risk assessment attribute; n Indicates the total number of time periods for risk indicator data monitoring; The risk index data representing the risk assessment attributes of bridges at different time periods, ; Formula (2) is used to solve the evaluation state span number of bridge risk assessment attributes, Indicates the number of evaluation state spans; Formula (3) is used to solve the evaluation state stability number of the bridge risk assessment attribute, Indicates the evaluation state stability number; The digital feature set of risk indicator data is obtained through formulas (1)-(3): ; S512: Determine the weight of each bridge risk assessment attribute under the bridge structure element by entropy weight method ; S513: Calculation m The overall digital feature group of bridge risk assessment attributes is shown in formulas (4), (5) and (6). （4） （5） （6） Formula (4) is used to solve the average number of evaluation states of bridge structural elements. Ta Evaluate condition uniformity for bridge structural elements; Formula (5) is used to solve the span number of the bridge structure element evaluation state, Tb Evaluate the number of condition spans for bridge structural elements; Formula (6) is used to solve the span number of the bridge structure element evaluation state, Pc Evaluate the number of condition spans for bridge structural elements; The overall digital feature set is obtained through formulas (4)-(6) ; S514: Ta is the mean, Tb is the standard deviation, randomly generates normal random numbers M ;by Tb is the mean, Pc is the standard deviation, randomly generates normal random numbers N ; S515: Setting the membership function, as shown in formula (7), （7） is the membership function, in which M and N As the independent variable, the coordinate value obtained after output is The scattered coordinates of S516: Repeat S514-S515 to obtain a total of Z scattered point coordinate sets, and put the scattered point coordinate sets into the coordinate system to form a risk indicator data scattered point image Q0.

4. The IFC-based bridge risk assessment method according to claim 3 is characterized in that: The risk assessment level is specified, and multiple scatter images corresponding to different risk assessment levels are obtained, including: S521: Specify risk assessment levels, which include excellent, good, medium, poor and unsatisfactory, and set assessment intervals for each risk assessment level; S522: Calculate the digital feature group of the standard monitoring and evaluation data, as shown in formulas (8), (9) and (10), （8） （9） （10） Formula (8) is used to solve the standard evaluation state mean of standard monitoring and evaluation data, is the average value of the standard evaluation status of the standard monitoring and evaluation data, and are the minimum and maximum values ​​for each specified evaluation interval respectively; Formula (9) is used to solve the standard evaluation state span of standard monitoring and evaluation data, The number of state spans for the standard assessment; Formula (10) is used to solve the standard evaluation state stability number of standard monitoring and evaluation data, is the standard evaluation state stability number, u is a constant; The digital feature group is formed by formulas (8)-(10) ; S523: Execute steps S514-S516 on the digital feature group of the standard monitoring evaluation data of each evaluation interval, and Ta Replace with ,Will Tb Replace with ,Will Pc Replace with , calculate and form 5 scatter images Q1-Q5 of standard monitoring and evaluation data corresponding to excellent, medium, poor and unqualified.

5. The IFC-based bridge risk assessment method according to claim 4 is characterized in that: Set the risk level index value for the risk assessment level, compare the scatter image corresponding to the risk index data with multiple scatter images corresponding to different risk assessment levels, obtain the risk assessment level and risk level index value of different structures, associate the risk assessment level and risk level index value of different structures with the corresponding structures, and obtain the updated bridge risk IFC file, including: S531: Setting a risk level index value for the risk assessment level; S532: superimpose the scattered point images of Q0-Q5, compare them, check the overlap between Q0 and Q1-Q5, obtain the maximum overlap, and obtain the risk assessment level of different structures according to the maximum overlap; S533: Associating the risk assessment levels and risk level index values ​​of different structures with corresponding bridge structure elements to obtain an updated bridge risk IFC file.

6. The IFC-based bridge risk assessment method according to claim 1, characterized in that: S2 obtains the original IFC dictionary file, performs entity expansion on the bridge structure components, wherein the entity expansion is to add bridge structure elements to the original IFC dictionary file to obtain a bridge structure IFC dictionary file, and forms a bridge structure IFC file according to the bridge structure IFC dictionary file, including: S21, obtaining the original IFC dictionary file; S22. Based on the IFC standard, define the bridge space in the original IFC dictionary file; S23, performing entity expansion on the bridge structure components, that is, adding the bridge structure elements into the bridge space to obtain the bridge structure IFC dictionary file; the bridge structure IFC dictionary file contains the content required for the bridge structure to form an IFC file; S24. Form a bridge structure IFC file according to the required contents of the bridge structure in the bridge structure IFC dictionary file.

7. The IFC-based bridge risk assessment method according to claim 1, characterized in that: S3 expands the attribute set of the bridge structure element, and the attribute set expansion is to add the bridge risk assessment attribute corresponding to the bridge structure element in the bridge structure IFC file to obtain a basic bridge risk IFC file, including: S31, read the bridge structure IFC file, and select the specified bridge structure element; S32. Expand the attribute set of the bridge structure element, that is, add the risk assessment attribute to the corresponding bridge structure element to obtain the basic bridge risk IFC file.

Images