Road and bridge design method and system for BIM real scene model

Abstract

The invention relates to the technical field of road and bridge design, in particular to a road and bridge design method and system for a BIM live-action model, and the method comprises the following steps: obtaining a path construction item and an elevation value based on the BIM live-action model, extracting a slope difference index group, calculating a traffic index in combination with a traffic attribute, carrying out the grading, and screening out an unadapted structure combination. And the detection node responds to generate a load fluctuation group, constructs a fatigue score sequence, compares the initial parameters, and generates structure optimization suggestion information. According to the method, through the detection of the longitudinal section construction item and the path linear construction item, the terrain adaptability analysis of the road path is optimized, and the slope adaptability judgment of the terrain jump limit value and the construction grade value is combined, so that more accurate material selection and structure adjustment can be performed on the bridge design, and the reliability and economy of the structure are enhanced; by evaluating the fatigue load score of the node and the initial parameters of the structure, the accurate diagnosis and optimization suggestion of the structure weakening reason are realized.

Description

Technical Field

[0001] This invention relates to the field of road and bridge design technology, and in particular to a road and bridge design method and system for BIM real-scene models. Background Technology

[0002] The field of road and bridge design technology encompasses the planning, design, and construction of road and bridge structures. It primarily studies engineering design methods related to the structural layout, route selection, structural parameters, load analysis, and construction implementation of roads and bridges. This field covers multiple technical aspects, including route engineering, structural engineering, geological and hydrological analysis, construction techniques, and maintenance plans. It involves numerous design parameters such as alignment, longitudinal section design, cross-sectional construction, load standards, structural stress analysis, and safety redundancy. With the development of computer-aided design, building information modeling (BIM), geographic information systems (GIS), and intelligent modeling algorithms, road and bridge design technology is gradually evolving towards digitalization, automation, and visualization, playing a crucial role in improving design efficiency, enhancing construction accuracy, ensuring structural safety, and achieving full life-cycle management of projects.

[0003] Among them, the road and bridge design method for BIM reality models is a road and bridge design approach that integrates BIM modeling technology with reality data construction capabilities. It aims to construct a 3D building model based on a real geographical scene, and on this basis, conduct road and bridge layout, parametric modeling, and structural verification. This method is mainly applied to road and bridge scheme deduction, feasibility analysis, construction deployment, and operation and maintenance simulation in complex terrain or urban environments, improving the accuracy, visibility, and collaboration of the design. It is widely used in municipal construction, transportation infrastructure projects, and intelligent construction and management systems for transportation structures in digital twin scenarios.

[0004] Traditional design methods, when dealing with complex and highly variable terrain environments, fail to reflect real-time terrain changes in longitudinal profile and alignment design, leading to frequent mismatches between design schemes and actual terrain. Furthermore, traditional methods rely solely on experience-based judgment rather than detailed data analysis in classifying road use grades, resulting in deficiencies in traffic flow management and long-term durability. These methods also fail to adequately consider the dynamic adaptability between traffic loads and structural materials, causing bridges to require major repairs or reinforcements before reaching their design service life, thus increasing the overall maintenance costs of public infrastructure. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a road and bridge design method and system for BIM real-scene models.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a road and bridge design method for BIM reality models, comprising the following steps: S1: Based on the BIM real-scene model, obtain the longitudinal profile construction items and path alignment construction items corresponding to the road path, calculate the slope value and angle change ratio of three consecutive construction segments, extract the slope change sequence, and generate the road path slope difference index group. S2: Based on the road path slope difference index group, sort the path segments according to the corresponding position of each path segment, divide the traffic level segments, and generate the road path traffic level division result. S3: Based on the road route traffic level classification results, and for each level of road segment, the material number, paving method and construction level value defined in the structural type limitation table are used to determine the slope matching and compare the traffic intensity, filter out mismatched combinations, and generate a list of structural type matching combinations. S4: Based on the structure type adaptation combination list, obtain the BIM model node location data, main beam structural component size value and pier span value corresponding to the bridge segment, detect the vertical displacement response value and shear stress fluctuation amplitude generated by the node when the vehicle axle load passes, and perform cumulative calculation according to the vehicle passage time series to generate the node load response fluctuation group.

[0007] As a further aspect of the present invention, the road path slope difference index group includes the elevation change point of the path segment, the extreme value of the broken line change ratio, and the slope variation trend. The road path traffic level classification result specifically includes the traffic level number sequence, the weight interval division standard, and the path level mapping relationship. The structure type adaptation combination list specifically refers to the structure number selection table, the material matching level, and the construction layout combination sequence. The node load response fluctuation group includes the bridge segment node number set, the stress fluctuation value sequence, and the traffic time load mapping table.

[0008] As a further aspect of the present invention, the steps for obtaining the road path gradient index group are as follows: S101: Based on the BIM real-scene model, obtain the longitudinal profile construction items and path alignment construction items corresponding to the road path in the BIM real-scene model, extract the elevation points of the longitudinal profile segments and the path distance values, compare the elevation point differences with the path segment length to obtain the slope value sequence of each path segment, establish a correspondence between the slope value sequence and the path segment number, and generate a path segment slope value reference table. S102: Call the path segment slope value reference table, extract the slope values ​​corresponding to three consecutive numbered path segments, calculate the slope value difference between the two segments and perform quotient calculation to obtain the angle jump rate value, and sort the angle jump rate values ​​according to the path number to construct a sequence, generating the path segment angle jump rate sequence. S103: Based on the path segment angle jump rate sequence, extract the maximum angle jump rate value and the average change amplitude of each segment, combine the path number order to construct a slope trend line structure dataset, and construct a continuous change segment identification list according to the direction of rise and fall, generating a road path slope difference index group.

[0009] As a further aspect of the present invention, the steps for obtaining the road route traffic level classification results are specifically as follows: S201: Based on the road path gradient index group, obtain the daily average traffic flow data, heavy vehicle ratio data and traffic use classification number corresponding to the road path segment, and convert the three data into numerical units and perform unit conversion processing to generate the path segment traffic attribute matrix. S202: Call the path segment access attribute matrix, calculate the standardized access index of each path segment, sort the path segments according to the access index, and generate a path segment access weight sequence. S203: Based on the route segment traffic weight sequence, classify the route segments into corresponding level segments, establish the correspondence between route segment numbers and level intervals, and generate road route traffic level classification results.

[0010] As a further aspect of the present invention, the formula for calculating the standardized traffic index of each path segment is as follows: ; in, Represents path segment Traffic index. Represents path segment The normalized value of the average daily traffic flow. Represents path segment The proportion of heavy-duty vehicles, Represents path segment The weight value corresponding to the type of traffic use. Represents path segment The set standardized ratio.

[0011] As a further aspect of the present invention, the step of obtaining the structure type adaptation combination list specifically includes: S301: Based on the road route traffic level classification result, call the broken line change rate value corresponding to the number in the road route slope difference index group, determine whether the broken line change rate exceeds the terrain jump limit threshold, and generate the road segment slope matching judgment result. S302: Based on the slope matching judgment result of the path segment, call the material number, paving method number and construction level number that match the number level in the structure type limitation table, establish the structure type attribute set for each path segment, and generate the structure type matching parameter set; S303: Based on the structure type matching parameter set, compare it with the marked slope matching status, filter out combinations that do not meet the slope change matching, and generate a list of structure type matching combinations.

[0012] As a further aspect of the present invention, the steps for obtaining the nodal load response fluctuation group are as follows: S401: Based on the structure type adaptation combination list, according to the determined bridge segment structure number, obtain the node position coordinate set in the corresponding BIM model, extract the cross-sectional width value and beam height value of the main beam structural segment to which the node belongs, and generate a bridge segment node construction parameter table. S402: Call the bridge segment node construction parameter table, collect the vehicle axle load value applied at each node position when vehicles pass through the bridge segment according to the coordinate position of the node, calculate the response value of the node under the action of vehicle passage, and generate the bridge segment passage load response quantity group. The formula for the response value of the computing node under the action of vehicle passage is: ; in, Indicates the first The node at the th Response value under the action of vehicle passage Represents a node In the vehicle The normalized value of the instantaneous vertical displacement under action. This represents the variation in the amplitude of the nodal shear stress. Represents a node The normalized value of the span of the bridge section. Indicates vehicle The area reference constant; S403: Based on the bridge section traffic load response group, the response values ​​are accumulated and synthesized in the order of vehicle traffic time sequence, and the response fluctuation trend sequence is reconstructed based on the number position. Combined with the traffic time node, the load sequence map of the path segment in the time domain is generated, and the node load response fluctuation group is established.

[0013] As a further aspect of the present invention, the method further includes: SS5: Call the nodal load response fluctuation group, filter the locations of stress concentration components caused by parameter deviation, mark the reasons for structural weakening and the adjustable direction, and generate structural design optimization suggestions; The structural design optimization suggestions specifically include component adjustment suggestions, fatigue risk trigger source identification, and design parameter adjustment directions.

[0014] As a further aspect of the present invention, the step of obtaining the structural design optimization suggestion information specifically includes: S501: Call the node load response fluctuation group, and obtain the fatigue load score value of the node under unit load based on the vertical displacement fluctuation value, shear stress change amplitude and cycle frequency data of each bridge segment node, and generate the node fatigue load score sequence. S502: Based on the node fatigue load scoring sequence, filter the component numbers with high scoring values ​​and structural configuration indicators that deviate from the upper and lower limits of the interval, and generate a list of component identification with parameter deviations. S503: Based on the parameter deviation component identification list, and combined with the deviation trend of the scoring offset direction and the corresponding initial structural item, mark the corresponding fatigue concentration characteristics, structural weakening location source and adjustable direction suggestion content, and establish structural design optimization suggestion information.

[0015] A road and bridge design system for BIM reality models, wherein the road and bridge design system for BIM reality models is used to implement the aforementioned road and bridge design method for BIM reality models, the system comprising: The path slope difference analysis module is based on the BIM real scene model. It obtains the longitudinal profile construction items and path alignment construction items corresponding to the road path, calculates the slope value and angle change ratio of three consecutive construction segments, extracts the slope change sequence, and generates a road path slope difference index group. Based on the road path slope difference index group, the traffic level analysis module sorts the path segments according to the corresponding location of each path segment, divides the traffic level segments, and generates the road path traffic level classification results. The type adaptation analysis module, based on the road route traffic level classification results, and for each level of road segment, uses the material number, paving method and construction level value defined in the structural type limitation table to perform slope matching judgment and traffic intensity comparison, filters out mismatched combinations, and generates a list of structural type adaptation combinations. The load response analysis module, based on the structure type adaptation combination list, detects the vertical displacement response value and shear stress fluctuation amplitude generated at the nodes when the vehicle axle load passes, and performs cumulative calculations according to the vehicle passage time series to generate a node load response fluctuation group. The structural design optimization module calls the nodal load response fluctuation group, filters the locations of stress concentration components caused by parameter deviations, marks the reasons for structural weakening and the adjustable directions, and generates structural design optimization suggestions.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by detecting longitudinal profile structural items and path alignment structural items, the terrain adaptability analysis of road paths is optimized. This allows for a detailed assessment of the terrain features and slope changes of each road segment, ensuring the rationality and safety of road design. By integrating daily traffic volume, heavy vehicle ratio, and traffic use classification, a road segment classification based on traffic index is implemented, improving traffic flow management efficiency, optimizing road lifespan and maintenance costs. Combining terrain change limits and slope adaptability judgment with structural grade values ​​enables more precise material selection and structural adjustments for bridge design, enhancing structural reliability and economy. Through fatigue load scoring of nodes and evaluation of initial structural parameters, accurate diagnosis and optimization suggestions for structural weakening are achieved, extending the bridge's service life and reducing maintenance frequency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flowchart of S1 of the present invention; Figure 3 This is a detailed flowchart of the S2 process of the present invention; Figure 4 This is a detailed flowchart of the S3 process of the present invention; Figure 5 This is a detailed flowchart of the S4 process of the present invention; Figure 6 This is a detailed flowchart of S5 of the present invention; Figure 7 This is a system flowchart of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] Please see Figure 1 This invention provides a technical solution: a road and bridge design method for BIM reality models, comprising the following steps: S1: Based on the BIM real-scene model, obtain the longitudinal profile construction items and path alignment construction items corresponding to the road path, detect the terrain elevation value of each longitudinal path segment, calculate the slope value and angle change ratio of three consecutive construction segments, extract the maximum slope difference and broken line change trend of all path segments as a slope change sequence, and generate a road path slope difference index group. S2: Based on the road path gradient index group, the daily average traffic flow, heavy vehicle ratio and traffic use classification number are obtained according to the corresponding location of each path segment. The three attribute data are weighted and evaluated to calculate the traffic index. The path segments are sorted according to the traffic index and the traffic level segments are divided to generate the road path traffic level classification result. S3: Based on the road route traffic level classification results, according to the road segment corresponding to each level, call the broken line change rate value in the road route slope difference index group to determine whether it is higher than the set terrain jump limit value, and refer to the material number, paving method and construction level value defined in the structure type limitation table for each level of road segment to perform slope matching judgment and traffic intensity comparison, filter out mismatched combinations, and generate a list of structure type adaptation combinations; S4: Based on the structural type adaptation combination list, according to the determined bridge segment structure number, obtain the BIM model node location data, main beam structural component size value and pier span value corresponding to the bridge segment, detect the vertical displacement response value and shear stress fluctuation amplitude generated by the node when the vehicle axle load passes, and perform cumulative calculation according to the vehicle passage time series to generate the node load response fluctuation group. S5: Call the nodal load response fluctuation group. Based on the vertical displacement fluctuation value, shear stress change amplitude and cycle frequency data of each bridge segment node, and with the bridge segment span, structural unit size and traffic axle load density as the benchmarks, perform amplitude normalization and frequency weight conversion to establish a nodal fatigue load scoring sequence. Compare the nodal fatigue load scores with the plate thickness value, main beam reinforcement ratio and connection member arrangement in the initial structural design parameters. Filter the positions of stress concentration members caused by parameter deviations, mark the reasons for structural weakening and the adjustable directions, and generate structural design optimization suggestions. The road path slope difference index group includes elevation change points of the path segment, extreme values ​​of the broken line change ratio, and slope variation trend. The road path traffic level classification results are specifically the traffic level number sequence, weight interval division standard, and path level mapping relationship. The structural type adaptation combination list specifically refers to the structural number selection table, material matching level, and construction layout combination sequence. The node load response fluctuation group includes the bridge segment node number set, stress fluctuation value sequence, and traffic time load mapping table. The structural design optimization suggestion information specifically includes component adjustment suggestions, fatigue risk trigger source identification, and design parameter adjustment direction.

[0025] Please see Figure 2 The specific steps for obtaining the road path gradient index group are as follows: S101: Based on the BIM real-scene model, obtain the longitudinal profile construction items and path alignment construction items corresponding to the road path in the BIM real-scene model, extract the elevation points of the longitudinal profile segments and the path distance values, compare the elevation point differences with the path segment length to obtain the slope value sequence of each path segment, establish a correspondence between the slope value sequence and the path segment number, and generate a path segment slope value reference table. Based on the BIM real-world model, it is necessary to extract the construction data representing road paths from the model. Specifically, this involves extracting the "road path" identifier field from the model's data structure, and then calling the associated longitudinal profile construction item and path alignment construction item for each path. The longitudinal profile construction item contains elevation point data along the longitudinal direction of the path, while the path alignment construction item provides distance marker information for the start and end points of the path segment. In practice, taking a city road BIM model as an example, the path segment numbered A123 is extracted. Its longitudinal profile construction item contains several elevation points (e.g., point 1 is 103.2 meters, point 2 is 104.6 meters) and path distance values ​​(e.g., the corresponding distance is 20 meters). Then, for any... For any two adjacent elevation points, calculate their elevation difference and path segment length sequentially. For example, if the elevation difference of the first path segment is 1.4 meters and the path segment length is 20 meters, calculate the slope value using division: slope value = 1.4 ÷ 20 = 0.07. Repeat this process for all path segments to form a continuous sequence of slope values. To ensure that the path segment slope values ​​can be uniquely mapped, a dictionary structure needs to be established to associate each slope value with the path segment number. For example, path segment number A123-01 corresponds to a slope value of 0.07, and A123-02 corresponds to a slope value of 0.04. Finally, establish a table structure with the path number as the primary key to generate a path segment slope value lookup table.

[0026] S102: Call the path segment slope value reference table, extract the slope values ​​corresponding to three consecutive numbered path segments, calculate the slope value difference between the two segments and perform quotient calculation to obtain the angle jump rate value, and sort the angle jump rate values ​​according to the path number to construct a sequence, generating the path segment angle jump rate sequence; Call the path segment slope value lookup table. First, extract the slope value set of each group of three consecutive path segments in order of path number. For example, the slope values ​​corresponding to path segments A123-01 to A123-03 are 0.07, 0.04, and 0.09 respectively. Calculate the slope value difference of the first pair (0.07 and 0.04) as 0.03, and the difference of the second pair (0.04 and 0.09) as 0.05. Then, calculate the quotient of the slope change by dividing by the latter slope value to obtain the jump rate: the first pair is ( 0.07−0.04)÷0.04=0.75, the second pair is (0.09−0.04)÷0.04=1.25. This jump rate value represents the relative severity of the slope change. All three combinations in the entire path are processed in sequence to complete the calculation of all jump rate values. Then, these jump rate values ​​are sorted according to the path number order (such as group A123-01, group A123-02) to ensure that the subsequent trend processing is consistent with the path segment order. Finally, the sorting results are summarized to form the path segment angle jump rate sequence.

[0027] S103: Based on the path segment angle jump rate sequence, extract the maximum angle jump rate value and the average change amplitude of each segment, combine the path number order to construct a slope trend line structure dataset, and construct a continuous change segment identification list according to the direction of rise and fall to generate a road path slope difference index group. Based on the path segment angle jump rate sequence, the maximum jump rate value is first retrieved from the sequence. For example, the jump rate in path segment A123-04 is 1.65, which is the maximum value in the current sequence. Then, the values ​​of all jump rate values ​​are summed and divided by the number of path segment groups to obtain the average change amplitude value. For example, if the total jump rate is 18.2, the number of path segment groups is 12, and the average value is 1.5167, this average value and the maximum value are used as feature parameters in subsequent analysis. Furthermore, according to the path numbering order, each group of jump rate values ​​is mapped to a sequence line in a point-like manner to form a slope trend line. The structure is constructed with path number as the x-axis and jump rate value as the y-axis to generate a slope trend line structure dataset. At the same time, the intervals in the trend line where the jump rate continuously increases or decreases are identified. If the jump rate value shows a monotonically increasing or decreasing state in three or more consecutive path segment groups, it is identified as a continuous change segment. For example, the jump rates of segment A123-05 to A123-07 are 0.88, 1.02, and 1.19 respectively, which constitute a continuous increasing change segment. The start and end numbers of such continuous segments are recorded, and all change segments are summarized to form a list, thus completing the generation of the road path slope difference index group.

[0028] Please see Figure 3 The specific steps for obtaining the road route traffic level classification results are as follows: S201: Based on the road path gradient index group, obtain the daily average traffic flow data, heavy vehicle ratio data and traffic use classification number corresponding to the road path segment. Sample and extract the three data items respectively, establish a data index table structure according to the path segment number, and convert the three data items into numerical units and perform unit conversion processing to generate the path segment traffic attribute matrix. Based on the road path gradient index group, the identification numbers of each road segment are first matched sequentially according to the path segment number. For each numbered path segment, the data logs recorded by its historical traffic monitoring system are retrieved to extract the corresponding daily average traffic flow data. The data is retrieved on a daily basis, and the average value is obtained by summing the daily traffic flow values ​​over the past 30 days and dividing by 30. For example, if the traffic flow data of path segment A01 over the past 30 days are random values ​​between 1900 and 2200, then its average daily traffic flow can be set to 2050 vehicles. Next, the vehicle weight classification records in the road structure bearing capacity record system are retrieved, and the number of times heavy-load vehicles passed through the path segment within 30 days is extracted and divided by the total number of vehicles to obtain the heavy-load vehicle percentage. For example, if there are 420 heavy-load vehicles and the total number of vehicles is 2050, the heavy-load percentage is calculated. Next, the purpose of each route segment is categorized and read. Traffic purpose classification numbers are assigned according to preset classification standards and functional uses. For example, number 1 represents commuter traffic on main roads, number 2 represents industrial transport, and number 3 represents service traffic on secondary roads. Each classification number corresponds to a weight value assigned based on the expected vehicle traffic capacity. For example, main roads are assigned a value of 1.2, industrial transport 1.5, and service traffic on secondary roads 0.8. For different route segments, the numbers are read separately, and the corresponding weight values ​​are matched using a lookup table. After the three data points are extracted, a unified route segment data index table structure is constructed. The table structure uses the route segment number as the index field and records the three data items. Then, the dimensions are uniformly converted to dimensionless standardized values. The average daily traffic volume is processed using minimum-maximum normalization. The proportion of heavy-duty vehicles remains unchanged, and the weight of traffic use is directly assigned by looking up a table. After all data is converted, a unit conversion step is performed to uniformly convert it into pure numerical values ​​for subsequent multiplication and division operations, and finally the normalized daily traffic flow value is obtained. Heavy load ratio Application weight Standardized benchmark values By concatenating rows, a two-dimensional matrix is ​​formed, generating a path segment access attribute matrix.

[0029] S202: Call the path segment access attribute matrix, calculate the standardized access index of each path segment based on the three index values ​​of each path segment, sort the path segments according to the access index, and generate a path segment access weight sequence. The formula for calculating the standardized traffic index for each route segment is: ; in, Represents path segment Traffic index. Represents path segment The normalized value of the average daily traffic flow. Represents path segment The proportion of heavy-duty vehicles, Represents path segment The weight value corresponding to the type of traffic use. Represents path segment The set standardized ratio; The traffic flow index measures the combined impact of traffic volume, vehicle type distribution, and road use across different road segments. This index reflects traffic density and usage frequency on a road segment and is a key factor in determining road design parameters such as lane width and pavement material selection. (Molecular) The product of traffic volume, the proportion of heavy-duty vehicles, and the importance of road use provides a comprehensive assessment of road use intensity; the denominator... To standardize the factors and ensure consistency and comparability between the traffic index and actual road design parameters, a higher traffic index indicates greater usage frequency and load demand on the road segment, and more stringent requirements for road design, potentially requiring stronger pavement materials or wider lanes. Call the path segment access attribute matrix, traverse and read each row of data in path segment number order, and extract them respectively. , , and The four indicator values ​​are calculated according to the formula: ; Perform the operation, with the parameters described below: To normalize the average daily traffic flow, This represents the percentage of heavy-duty vehicles. Numerical weights corresponding to traffic uses, As a standardized baseline value, this value is set as a fixed constant to serve as a reference for uniformly evaluating the traffic capacity between different path segments. For example, it can be set as follows: Used for comparison with normal passage sections, if a certain segment of the path data is , , Then calculate its traffic index: ; The passage index is calculated for all path segments. The result of each segment is filled into the index table according to the number, and a complete passage index table is generated. After the index calculation of all path segments is completed, they are sorted in descending order of value, that is, the path segments with higher values ​​are placed at the front. If path segments with the same value are found during the sorting operation, their sorting positions are adjusted according to the dictionary order of the path segment numbers. Finally, the passage index sequence is output and the original path segment numbers are assigned to form a passage weight sequence.

[0030] S203: Based on the route segment traffic weight sequence, arrange the route segment number sequence from largest to smallest according to the traffic index, set the traffic level classification threshold range and perform level segmentation operation, classify the route segments into the corresponding level segments, establish the correspondence between the route segment number and the level range, and generate the road route traffic level classification result. Based on the route segment traffic weight sequence, the sorted route segment traffic index values ​​are read, and the maximum, minimum, and distribution range of all route segments are calculated sequentially. Based on the statistical results, a level classification threshold is set. The level classification standard is based on the traffic index value, dividing the route into four levels: Level 1 (traffic index...) Level 2 () Level 3 Level 4 The tiered classification uses a closed interval method (left closed, right open) to prevent overlap. Then, a numerical judgment operation is performed on the traffic index value of each path segment. For example, the traffic index of path segment A01 is... The index is determined to be between 0.25 and 0.50, and is classified as Level 3. The traffic index of route segment A05 is 0.813 and is classified as Level 1. After performing the classification operation on all route segments, a correspondence table between route segment number and level range is established. The record fields of this correspondence table include three columns of information: route segment number, traffic index value, and level category number. Finally, the result of this table is output as the road route traffic level classification result.

[0031] Please see Figure 4 The specific steps for obtaining the list of structure type adaptation combinations are as follows: S301: Based on the road route traffic level classification results, according to the route segment number of each level, call the broken line change rate value corresponding to the number in the road route slope difference index group, determine whether the broken line change rate exceeds the terrain jump limit threshold, and associate the judgment result with the route level to generate the route segment slope matching judgment result. Based on the road route traffic level classification results, firstly, according to the generated level classification structure, the list of route segment numbers corresponding to each level is read. Each number in the list is used as the primary key to call the road route slope difference index group, and the broken line change rate value of the route segment with that number is extracted. This value is obtained when the angle jump rate sequence is calculated and a trend line is formed. Specifically, the operation is to query the difference between adjacent changes in the angle jump rate sequence of the route segment, calculate the absolute slope change value of each trend line segment's rise or fall, and normalize it to obtain the broken line change rate. Suppose the broken line change rate extracted for route segment number A07 is 0.126, then it is judged whether it exceeds the preset terrain jump limit threshold. The terrain jump limit threshold is set to 0.10. According to actual engineering experience, this value represents the maximum allowable gradient change rate of local slope change. The judgment operation is: if the broken line change rate value is... satisfy ,in If the change is significant, the path segment is marked as "drastic change"; otherwise, it is marked as "mild change". The system continues to classify each judgment result according to the path level structure and combine it with its level label to construct a comprehensive information structure table containing path number, polyline change rate, judgment result, and level. After completing the judgment of all path segments, the path segment slope matching judgment result is output.

[0032] S302: Based on the slope matching result of the path segment, call the material number, paving method number and construction level number that match the number level in the structure type constraint table, establish the structure type attribute set for each path segment, and generate the structure type matching parameter set; Based on the path segment slope matching results, firstly, according to the path number and level information recorded in the judgment result structure, read the level markers line by line and match them with the corresponding items in the structure type limitation table. The structure type limitation table uses the level number as the primary key, and the record fields include three attributes: material number, paving method number, and construction level number. For example, when the level is level 1, the corresponding material number is M01, the paving method number is P02, and the construction level number is C1. For path segment A07 (level 1), the structure type is found to be [M01, P02, C1]. These three data items are combined with the path segment number to construct a path segment structure type attribute set. Continue to complete the table lookup for all path segments in the order of path number and generate a structure set. The structure set uses the path number as the index field, and the field value is a list composed of the three structural parameters. During this process, if there are multiple structure type combinations under the same level of path segment, all matching items need to be added to the set list. For example, the level of path segment A10 is level 2, and its corresponding limitation table has two sets of structure combinations: [M02, P01, C2] and [M03, P01, C2] and [M03, P01, C2], respectively. If P03, C2] are included in the set and recorded, no structural validity judgment is performed during the matching operation. It is only used for structural probability matching records, and finally forms a structural type matching parameter set.

[0033] S303: Based on the structure type matching parameter set, compare the combination of structure types of the path segment with the marked slope matching status, filter out combinations that do not meet the slope change matching, and build a list of remaining structure combinations according to the path segment number to generate a list of structure type matching combinations.

[0034] Based on the structure type matching parameter set, the list of structure combinations for each path segment is read one by one, and compared with the slope matching status of the path segment. If a structure combination is not suitable for conditions with drastic slope changes, the structure combination is filtered out. For example, for path segment A07, its matching structure type is [M01, P02, C1], and the slope matching is marked as "drastic change". The maximum allowable slope change value for this structure combination is read from the structure type constraint conditions as 0.08. If the broken line change rate of A07 is 0.126, a numerical comparison judgment is performed: If the condition is not met, then the structural combination is removed and marked as not satisfying. If another combination [M04, P02, C1] corresponds to a maximum slope change of 0.15 in the constraint table, then it is judged that... After retaining the structure combination and completing the structure combination screening for all path segments, list the remaining adaptable structure combination numbers for each path segment, construct a dictionary structure with the path segment number as the primary key and the structure combination list as the value, and finally output the structure type adaptation combination list.

[0035] Please see Figure 5 The specific steps for obtaining the nodal load response fluctuation group are as follows: S401: Based on the structural type adaptation combination list, according to the determined bridge segment structural number, obtain the node location coordinate set in the corresponding BIM model, and establish a node spatial layout reference table according to the structural number index method. Extract the cross-sectional width value and beam height value of the main beam structural segment to which the node belongs, and generate a bridge segment node construction parameter table. Based on the structural type adaptation and combination list, the structural number field is first extracted from the path segment numbers recorded as bridge segments in the list. Using the structural number as an index, the BIM real-scene model data file is called to retrieve the corresponding node dataset in the 3D model. The coordinate positions of all component nodes of the bridge segment are obtained from the structural topology. The node coordinate values ​​are point set data in the 3D coordinate system; for example, the position of node number N001 is (35.2, 78.6, ...). 12.3) After constructing the node location index table, locate the main beam segment to which each node belongs. Determine the main beam segment number through structural hierarchy information tracing. Then, call the construction parameter information of the main beam segment and read its cross-sectional width and beam height values. For example, the cross-sectional width of the main beam segment corresponding to node N001 is 3.2 meters and the beam height is 1.8 meters. After reading, record the geometric parameters and node coordinates together in the entry corresponding to the structure number. Organize the data according to the triple mapping relationship of structure number - node number - main beam segment number to form the coordinates and geometric parameter combination of all nodes under each bridge structure. Summarize them into a unified table structure. The output result is the bridge segment node construction parameter table.

[0036] S402: Call the bridge segment node construction parameter table, collect the vehicle axle load value applied at each node position when vehicles pass through the bridge segment according to the coordinate position of the node, and detect the vertical displacement data and shear stress change data of the bridge segment node under vehicle load. Perform continuous cumulative quantization processing based on the node response change per unit time, calculate the response value of the node under vehicle passage, integrate the response values ​​of each node and the vehicle passage sequence, obtain the dynamic stress distribution value set of the entire bridge segment, and generate the bridge segment passage load response value set. The formula for calculating the response value of a node under the action of vehicle passage is: ; in, Indicates the first The node at the th Response value under the action of vehicle passage Represents a node In the vehicle The normalized value of the instantaneous vertical displacement under action. This represents the variation in the amplitude of the nodal shear stress. Represents a node The normalized value of the span of the bridge section. Indicates vehicle The area reference constant; The response value is used to assess the structural response of a bridge under traffic loads, particularly the displacement and stress changes at the nodes. This value is a key indicator for judging the safety and stability of a bridge and is crucial for the design and maintenance of bridge structures. (Molecular) The vertical displacement and shear stress changes at the nodes caused by vehicle traffic are considered; their product provides a measure of the stress-displacement composite response of the nodes; the denominator... The bridge span and vehicle impact area at the node locations are used as adjustment factors to balance the impact of vehicles with different spans and sizes. This response value calculation provides an intuitive measure of the behavior of each node under actual traffic loads, thereby guiding bridge design optimization and structural safety assessment.

[0037] The bridge segment node construction parameter table is invoked, and the node number and spatial coordinate position are read one by one. Using an array of axle load sensors deployed beneath the bridge structure, the axle load values ​​recorded by the sensors when a vehicle passes through the current node's projection position are collected. Vehicle numbers are mapped one-to-one with node numbers using license plates or time series as indexes, forming a... The dataset also reads the vertical displacement of the node during vehicle passage. and shear stress variation value The vertical displacement value is obtained by sampling the instantaneous displacement value every 0.1 seconds using a displacement gauge. This instantaneous displacement value is then normalized to the interval [0,1] by dividing by the preset maximum allowable deflection. The shear stress variation value... The calculation involves taking the absolute value of the shear stress difference between the two moments before and after the vehicle passes, and then reading the normalized span of the bridge segment where the node is located. The maximum span of each bridge segment is uniformly normalized to [0,1], and the baseline value of the vehicle's effective area is obtained. This is set as an approximate value for the axle pressure coverage area of ​​the vehicle. For example, the axle contact area of ​​a heavy vehicle is approximately 1.44 square meters. For each group... Calculate node response value Substitute into the formula: ; in, For the first The node at the th The response intensity value under vehicle traffic, if taken , MPa , The calculation formula is: ; Based on this, the response values ​​of all vehicles corresponding to all nodes are calculated, a response matrix is ​​established, and finally integrated to form a bridge section traffic load response quantity group.

[0038] S403: Based on the bridge section traffic load response group, the response values ​​are accumulated and synthesized in the order of vehicle traffic time sequence, and the response fluctuation trend sequence is reconstructed based on the number position. Combined with the traffic time node, the load sequence map of the path segment in the time domain is generated, and the node load response fluctuation group is established.

[0039] Based on the bridge section traffic load response group, firstly, according to the time sequence of vehicle passage time records, assign each vehicle to each node. Sort by value, and perform a sequence of all vehicles under the same node number. The system performs an accumulation operation to obtain the cumulative response value. Then, it combines the response values ​​of all nodes at each time point to construct a response vector set. The response values ​​are sorted and combined according to the spatial position of the node numbers to form a ternary sequence matrix of node number-passage time-response value. Then, it performs horizontal indexing on each node number to mark the spatial distribution coordinates of the nodes. The responses of each node at different times are normalized and connected by a broken line using the timestamps corresponding to the vehicle passage times to generate a response fluctuation trend line. The trend line uses the passage time as the horizontal axis. The vertical axis represents the response value. It can overlay and display the response sequence changes of multiple nodes, and finally integrate the response trend maps of bridge nodes on all path segments into a load sequence map of the path segment in the time domain, and establish a node load response fluctuation group with the node number as the primary key.

[0040] Please see Figure 6 The specific steps for obtaining structural design optimization suggestions are as follows: S501: Call the nodal load response fluctuation group. Based on the vertical displacement fluctuation value, shear stress change amplitude and cycle frequency data of each bridge segment node, and with the span value of the bridge segment where the node is located, the cross-sectional size value of the structural unit and the traffic axle load density value as references, the three participating items are normalized in amplitude and weighted by frequency to obtain the fatigue load score value of the node under unit load and generate the nodal fatigue load score sequence. The nodal load response fluctuation group is invoked. First, three key response parameters corresponding to the node number of each bridge segment are extracted, namely the vertical displacement fluctuation value. Shear stress variation range and load cycle frequency ,in To calculate the difference between the maximum and minimum vertical displacement under vehicle load, instantaneous values ​​are obtained by sampling and recording at each moment using displacement gauges, and the difference between the maximum and minimum values ​​is calculated. For example, if node N12 records a maximum displacement of 0.026m and a minimum displacement of 0.010m within 10 minutes, then the fluctuation value is... Shear stress variation range The difference between the maximum and minimum shear stress values ​​is obtained from strain gauges. For example, if the shear stress variation at this node is 1.2 MPa, the load cycle frequency is calculated by dividing the number of vehicles passing this node during the measurement period by the time length to obtain the unit frequency value. For example, if 28 vehicles pass through within 10 minutes... The above three parameters need to be uniformly normalized and frequency-weighted with reference to the structural reference values. The normalization references are the spans of the bridge sections where the nodes are located. Cross-sectional dimensions Traffic axle load density ,in Let this be the span value of the main beam segment at this node. For example, if the span is 40m, then a normalization factor is set. Let the cross-sectional dimensions be... Traffic axle load density Perform normalization calculation: ; ; ; The above normalized values ​​are weighted using empirical weighting coefficients, and the average or weighted geometric mean is taken to construct the nodal fatigue load score. For example, if weighting coefficients of 0.4, 0.4, and 0.2 are used, the score value is: ; Finally, all node numbers are traversed to form the corresponding scoring sequence, and the node fatigue load scoring sequence is output.

[0041] S502: Based on the node fatigue load scoring sequence, call the corresponding plate thickness value, main beam reinforcement ratio value and connection member arrangement number in the initial structural parameters, compare them with the scoring values ​​one by one, filter out the member numbers with high scoring values ​​and structural configuration indicators that deviate from the upper and lower limits of the interval, and generate a parameter deviation member identification list. Based on the node fatigue load scoring sequence, the scoring values ​​are first extracted by node number. Then, call up the corresponding node plate thickness values ​​in the initial structural parameter table one by one. Main beam reinforcement ratio and the numbering of the arrangement of connecting components The plate thickness value is compared with the score value. The upper limit of the score threshold is set at 0.15. When the score value... Upon reaching the target node, the screening process begins. The upper and lower limits of the node's structural parameters are then read. The recommended range for slab thickness is set to 10mm–14mm, and the recommended range for reinforcement ratio is 0.75%–1.10%. If node N12 has a slab thickness of 8.5mm, a reinforcement ratio of 0.63%, and a score of 0.19336, then it is determined whether the slab thickness is below 10mm and the reinforcement ratio is below 0.75%, respectively. Conditional judgments are then executed. ; ; If any of the above conditions are true and If the node is identified as a high-fatigue-risk node, its component number, score, and abnormal parameter items are recorded. The node and component are added to the parameter deviation component identification list. Finally, the list is compiled and output by summarizing all node numbers and component entries with high scores and out-of-bounds parameters.

[0042] S503: Based on the parameter deviation component identification list, combined with the scoring offset direction and the deviation trend of the corresponding initial structural item, mark the corresponding fatigue concentration characteristics, structural weakening location source and adjustable direction suggestions, summarize the components that need to be adjusted in the structural design and the adjustment logic, and establish structural design optimization suggestion information.

[0043] Based on the parameter deviation component identification list, the scoring offset direction is extracted, i.e., the deviation direction of the specific parameter item corresponding to the scoring value exceeding the upper limit. Combined with the upper and lower limits of the initial structural parameters, the fatigue concentration characteristics exhibited by the structural component are determined. For example, when the scoring value is significantly high while the plate thickness and reinforcement ratio are simultaneously low, it is defined as fatigue concentration due to insufficient stiffness. If the scoring value is high and the arrangement method number points to a single-sided connection structure, it is determined to be a fatigue characteristic of symmetry defect in the connection structure. The source of fatigue concentration is further marked. If the node's spatial location is in the middle span section of the bridge and the span is large, the source of concentration is... The components are labeled as "concentrated load in the mid-span section" or "repeatedly loaded area at the constraint boundary" if they are near the supports. Each component is labeled with its fatigue type and spatial location source information. Then, the deviation trend of each structural index is marked with the adjustable direction. For example, if the plate thickness is too low, it is marked "increase the thickness level"; if the reinforcement ratio is too low, it is marked "increase the longitudinal reinforcement ratio"; if the connection arrangement is asymmetrical, it is marked "change to a symmetrical arrangement structure". Finally, all component numbers, corresponding fatigue categories, location sources, suggested adjustment directions and structural index types are summarized to construct a structural design optimization suggestion information table.

[0044] Please see Figure 7 A road and bridge design system for BIM reality models, the system being used to execute the aforementioned road and bridge design method for BIM reality models, the system comprising: The path slope difference analysis module is based on the BIM real scene model. It obtains the longitudinal profile construction items and path alignment construction items corresponding to the road path, calculates the slope value and angle change ratio of three consecutive construction segments, extracts the slope change sequence, and generates a road path slope difference index group. The traffic level analysis module is based on the road path slope difference index group. According to the corresponding location of each path segment, it sorts the path segments and divides them into traffic level sections, generating the road path traffic level classification results. The type adaptation analysis module is based on the road route traffic level classification results. For each level of road segment, it uses the material number, paving method and construction level value defined in the structure type limitation table to judge the slope matching and compare the traffic intensity, filter out mismatched combinations, and generate a list of structure type adaptation combinations. The load response analysis module, based on the structural type adaptation combination list, obtains the BIM model node location data, main beam structural component size value and pier span value corresponding to the bridge segment, detects the vertical displacement response value and shear stress fluctuation amplitude generated by the node when the vehicle axle load passes, and performs cumulative calculation according to the vehicle passage time series to generate the node load response fluctuation group. The structural design optimization module calls the nodal load response fluctuation group. Based on the vertical displacement fluctuation value, shear stress variation amplitude, and cycle frequency data of each bridge segment node, it performs amplitude normalization and frequency weight conversion based on the bridge segment span, structural unit size, and traffic axle load density, respectively, to establish a nodal fatigue load scoring sequence. The nodal fatigue load score is compared with the plate thickness value, main beam reinforcement ratio, and connection member arrangement in the initial structural design parameters. The module filters out the locations of stress concentration members caused by parameter deviations, marks the reasons for structural weakening and the adjustable directions, and generates structural design optimization suggestions.

[0045] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0046] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0047] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0048] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0049] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0050] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0051] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0052] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0053] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0054] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A road and bridge design method for BIM reality models, characterized in that, Includes the following steps: S1: Based on the BIM real-scene model, obtain the longitudinal profile construction items and path alignment construction items corresponding to the road path, calculate the slope value and angle change ratio of three consecutive construction segments, extract the slope change sequence, and generate the road path slope difference index group. S2: Based on the road path slope difference index group, sort the path segments according to the corresponding position of each path segment, divide the traffic level segments, and generate the road path traffic level division result. S3: Based on the road route traffic level classification results, and for each level of road segment, the material number, paving method and construction level value defined in the structural type limitation table are used to determine the slope matching and compare the traffic intensity, filter out mismatched combinations, and generate a list of structural type matching combinations. S4: Based on the structure type adaptation combination list, obtain the BIM model node location data, main beam structural component size value and pier span value corresponding to the bridge segment, detect the vertical displacement response value and shear stress fluctuation amplitude generated by the node when the vehicle axle load passes, and perform cumulative calculation according to the vehicle passage time series to generate the node load response fluctuation group.

2. The road and bridge design method for BIM reality models according to claim 1, characterized in that, The road path slope difference index group includes elevation change points of the path segment, extreme values ​​of broken line change ratio, and slope variation trend. The road path traffic level classification results specifically include traffic level number sequence, weight interval division standard, and path level mapping relationship. The structure type adaptation combination list specifically refers to the structure number selection table, material matching level, and construction layout combination sequence. The node load response fluctuation group includes bridge segment node number set, stress fluctuation value sequence, and traffic time load mapping table.

3. The road and bridge design method for BIM reality models according to claim 2, characterized in that, The specific steps for obtaining the road path gradient index group are as follows: S101: Based on the BIM real-scene model, obtain the longitudinal profile construction items and path alignment construction items corresponding to the road path in the BIM real-scene model, extract the elevation points of the longitudinal profile segments and the path distance values, compare the elevation point differences with the path segment length to obtain the slope value sequence of each path segment, establish a correspondence between the slope value sequence and the path segment number, and generate a path segment slope value reference table. S102: Call the path segment slope value reference table, extract the slope values ​​corresponding to three consecutive numbered path segments, calculate the slope value difference between the two segments and perform quotient calculation to obtain the angle jump rate value, and sort the angle jump rate values ​​according to the path number to construct a sequence, generating the path segment angle jump rate sequence. S103: Based on the path segment angle jump rate sequence, extract the maximum angle jump rate value and the average change amplitude of each segment, combine the path number order to construct a slope trend line structure dataset, and construct a continuous change segment identification list according to the direction of rise and fall, generating a road path slope difference index group.

4. The road and bridge design method for BIM reality models according to claim 3, characterized in that, The specific steps for obtaining the road route traffic level classification results are as follows: S201: Based on the road path gradient index group, obtain the daily average traffic flow data, heavy vehicle ratio data and traffic use classification number corresponding to the road path segment, and convert the three data into numerical units and perform unit conversion processing to generate the path segment traffic attribute matrix. S202: Call the path segment access attribute matrix, calculate the standardized access index of each path segment, sort the path segments according to the access index, and generate a path segment access weight sequence. S203: Based on the route segment traffic weight sequence, classify the route segments into corresponding level segments, establish the correspondence between route segment numbers and level intervals, and generate road route traffic level classification results.

5. The road and bridge design method for BIM reality models according to claim 4, characterized in that, The formula for calculating the standardized traffic index for each path segment is as follows: ； in, Represents path segment Traffic index. Represents path segment The normalized value of the average daily traffic flow. Represents path segment The proportion of heavy-duty vehicles, Represents path segment The weight value corresponding to the type of traffic use. Represents path segment The set standardized ratio.

6. The road and bridge design method for BIM reality models according to claim 5, characterized in that, The specific steps for obtaining the list of structure type adaptation combinations are as follows: S301: Based on the road route traffic level classification result, call the broken line change rate value corresponding to the number in the road route slope difference index group, determine whether the broken line change rate exceeds the terrain jump limit threshold, and generate the road segment slope matching judgment result. S302: Based on the slope matching judgment result of the path segment, call the material number, paving method number and construction level number that match the number level in the structure type limitation table, establish the structure type attribute set for each path segment, and generate the structure type matching parameter set; S303: Based on the structure type matching parameter set, compare it with the marked slope matching status, filter out combinations that do not meet the slope change matching, and generate a list of structure type matching combinations.

7. The road and bridge design method for BIM reality models according to claim 6, characterized in that, The specific steps for obtaining the nodal load response fluctuation group are as follows: S401: Based on the structure type adaptation combination list, according to the determined bridge segment structure number, obtain the node position coordinate set in the corresponding BIM model, extract the cross-sectional width value and beam height value of the main beam structural segment to which the node belongs, and generate a bridge segment node construction parameter table. S402: Call the bridge segment node construction parameter table, collect the vehicle axle load value applied at each node position when vehicles pass through the bridge segment according to the coordinate position of the node, calculate the response value of the node under the action of vehicle passage, and generate the bridge segment passage load response quantity group. The formula for the response value of the computing node under the action of vehicle passage is: ； in, Indicates the first The node at the th Response value under the action of vehicle passage Represents a node In the vehicle The normalized value of the instantaneous vertical displacement under action. This represents the variation in the amplitude of the nodal shear stress. Represents a node The normalized value of the span of the bridge section. Indicates vehicle The area reference constant; S403: Based on the bridge section traffic load response group, the response values ​​are accumulated and synthesized in the order of vehicle traffic time sequence, and the response fluctuation trend sequence is reconstructed based on the number position. Combined with the traffic time node, the load sequence map of the path segment in the time domain is generated, and the node load response fluctuation group is established.

8. The road and bridge design method for BIM reality models according to claim 7, characterized in that, The method further includes: S5: Call the nodal load response fluctuation group, filter the locations of stress concentration components caused by parameter deviation, mark the reasons for structural weakening and the adjustable direction, and generate structural design optimization suggestions; The structural design optimization suggestions specifically include component adjustment suggestions, fatigue risk trigger source identification, and design parameter adjustment directions.

9. The road and bridge design method for BIM reality models according to claim 8, characterized in that, The specific steps for obtaining the structural design optimization suggestion information are as follows: S501: Call the node load response fluctuation group, and obtain the fatigue load score value of the node under unit load based on the vertical displacement fluctuation value, shear stress change amplitude and cycle frequency data of each bridge segment node, and generate the node fatigue load score sequence. S502: Based on the node fatigue load scoring sequence, filter the component numbers with high scoring values ​​and structural configuration indicators that deviate from the upper and lower limits of the interval, and generate a list of component identification with parameter deviations. S503: Based on the parameter deviation component identification list, and combined with the deviation trend of the scoring offset direction and the corresponding initial structural item, mark the corresponding fatigue concentration characteristics, structural weakening location source and adjustable direction suggestion content, and establish structural design optimization suggestion information.

10. A road and bridge design system for BIM reality models, characterized in that, The system is used to implement the road and bridge design method for BIM reality models as described in any one of claims 1-9, and the system includes: The path slope difference analysis module is based on the BIM real scene model. It obtains the longitudinal profile construction items and path alignment construction items corresponding to the road path, calculates the slope value and angle change ratio of three consecutive construction segments, extracts the slope change sequence, and generates a road path slope difference index group. Based on the road path slope difference index group, the traffic level analysis module sorts the path segments according to the corresponding location of each path segment, divides the traffic level segments, and generates the road path traffic level classification results. The type adaptation analysis module, based on the road route traffic level classification results, and for each level of road segment, uses the material number, paving method and construction level value defined in the structural type limitation table to perform slope matching judgment and traffic intensity comparison, filters out mismatched combinations, and generates a list of structural type adaptation combinations. The load response analysis module, based on the structure type adaptation combination list, detects the vertical displacement response value and shear stress fluctuation amplitude generated at the nodes when the vehicle axle load passes, and performs cumulative calculations according to the vehicle passage time series to generate a node load response fluctuation group. The structural design optimization module calls the nodal load response fluctuation group, filters the locations of stress concentration components caused by parameter deviations, marks the reasons for structural weakening and the adjustable directions, and generates structural design optimization suggestions.

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