A modular prefabricated steel structure trestle bridge design method and system

Abstract

This invention belongs to the field of computer-aided trestle design technology. It provides a modular prefabricated steel structure trestle design method and system, comprising: acquiring the driving speed sequence and real-time center of gravity offset of historical trestle transport vehicles; constructing a driving speed-center of gravity offset coupled model through linear analysis; calculating continuous center of gravity offsets to extract extreme point coordinates; identifying stress-weak intervals through cluster analysis; migrating the weak intervals to the trestle to be designed using normalized coordinate mapping to obtain candidate weak nodes; determining the actual stress-weak points through safety and reliability analysis; replacing the corresponding standard connection module with an asymmetric anti-torsional reinforcement module; and generating the final design scheme. This invention can accurately identify weak nodes under dynamic loads by combining historical engineering data, improving the torsional resistance of the trestle and reducing steel waste.

Description

Technical Field

[0001] This invention belongs to the field of computer-aided trestle design technology, specifically a modular prefabricated steel structure trestle design method and system. Background Technology

[0002] Modular prefabricated steel structure trestle bridges are often used in special projects such as mountain wind farm construction due to their quick assembly and strong adaptability. They are used to transport special goods such as wind turbine blades that are too long, too heavy and whose center of gravity is very easy to shift. During special transportation, when the vehicle turns or encounters crosswinds, a huge shift in the center of gravity will occur, which can easily lead to fatigue damage to local nodes of the trestle bridge.

[0003] Existing trestle bridge design methods typically employ a static theoretical calculation model starting from scratch. Designers input the span and alignment parameters of the new bridge, apply standard uniformly distributed loads, and then perform finite element analysis. However, this conventional method has significant technical drawbacks:

[0004] Existing methods are seriously out of touch with actual operating conditions. The shift of a vehicle's center of gravity is a complex dynamic process. Purely theoretical static models cannot accurately predict the extreme values ​​of transient center of gravity shift caused by speed changes during actual driving.

[0005] Existing methods fail to effectively utilize historical engineering data. In actual engineering projects, there are already a large number of similar trestle bridges that have been built. These trestle bridges have exposed real stress-weak areas during long-term operation. However, existing design methods lack data mining and experience transfer mechanisms, and cannot transform the real damage experience of historical similar trestle bridges into prior knowledge or constraints for the design of new bridges.

[0006] This leads to either blindly reinforcing all nodes with equal strength when designing new target trestle bridges, resulting in serious waste of steel, or overlooking potential dynamic torsional weak points, leaving safety hazards. Therefore, how to use the real operation data of existing trestle bridges of the same type to accurately guide the local torsional resistance design of new trestle bridges through data-driven and mapping migration methods is a technical problem that urgently needs to be solved in this field.

[0007] Therefore, the present invention provides a modular prefabricated steel structure trestle bridge design method and system. Summary of the Invention

[0008] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0009] The technical solution adopted by this invention to solve its technical problem is:

[0010] One of the objectives of this invention is to provide a modular prefabricated steel structure trestle design method, comprising the following steps:

[0011] Step S10: Obtain the driving speed sequence and real-time center of gravity offset of historical similar trestle transport vehicles, perform linear analysis on the driving speed sequence and real-time center of gravity offset, and construct a driving speed-center of gravity offset coupled model.

[0012] Step S20: Based on the driving speed-center of gravity offset coupling model, calculate the continuous center of gravity offset of different transport vehicles when driving on the same type of trestle in history, and extract the bridge surface coordinates corresponding to the extreme points of continuous center of gravity offset to form a set of center of gravity offset coordinate points.

[0013] Step S30: Based on the set of center of gravity offset coordinate points, a clustering analysis algorithm is used to extract the high-frequency overlapping center of gravity offset intervals and identify the stress weak intervals of historical similar trestle bridges.

[0014] Step S40: Obtain the linear parameters of the target trestle to be designed, and use the normalized coordinate mapping algorithm to proportionally map the stress weak zone to the corresponding position of the target trestle to be designed, thereby generating candidate weak nodes.

[0015] Step S50: Perform structural safety reliability analysis on the candidate weak nodes. If the reliability analysis fails, the candidate weak nodes are determined to be actual stress weak points.

[0016] Step S60: In the initial design model of the target trestle to be designed, the standard connection module at the actual stress weak point is replaced with an asymmetric anti-torsion reinforcement module to generate the final trestle design scheme.

[0017] Preferably, the specific process for obtaining the driving speed sequence and the real-time center of gravity offset is as follows:

[0018] Real-time speed data and cargo box tilt angle data of transport vehicles traveling on historically similar trestle bridges are collected through the dynamic weighing system WIM and the vehicle-mounted inertial navigation system IMU.

[0019] Based on the tilt angle data of the carriage and the cargo loading height of the transport vehicle, the center of gravity offset is calculated using trigonometric functions. The real-time speed data is sorted according to timestamps to generate a driving speed sequence. The center of gravity offset is then time-aligned with the driving speed sequence to obtain the real-time center of gravity offset.

[0020] Preferably, the specific process for performing linear analysis on the driving speed sequence and real-time center of gravity offset is as follows:

[0021] Using the driving speed sequence as the independent variable sequence and the real-time center of gravity offset as the dependent variable sequence, the correlation coefficient between the independent variable sequence and the dependent variable sequence is calculated. If the correlation coefficient is greater than or equal to the correlation coefficient threshold, the real-time center of gravity offset and driving speed have a linear relationship. If the correlation coefficient is less than the correlation coefficient threshold, the real-time center of gravity offset and driving speed have a non-linear relationship.

[0022] Preferably, the specific process for constructing the driving speed-center of gravity offset coupled model is as follows:

[0023] If the real-time center of gravity offset is linearly related to the driving speed, then the least squares method is used to fit the linear relationship between the real-time center of gravity offset and the driving speed to construct a driving speed-center of gravity offset coupled model.

[0024] If the real-time center of gravity offset has a non-linear relationship with the driving speed, then a non-linear fitting method is used to fit the non-linear relationship between the real-time center of gravity offset and the driving speed to construct a driving speed-center of gravity offset coupled model.

[0025] Preferably, the specific process of constructing the set of centroid offset coordinate points is as follows:

[0026] Obtain multiple sets of continuous driving speed sequences and corresponding timestamps of different transport vehicles when driving on the same type of historical trestle bridge. Input the continuous driving speed sequences into the driving speed-center of gravity offset coupling model and output multiple sets of continuous real-time center of gravity offsets.

[0027] Based on the continuous driving speed sequence and the corresponding timestamp, the real-time longitudinal driving distance of the transport vehicle is obtained by integrating the speed over time.

[0028] Based on any set of continuous real-time center of gravity offsets, with the real-time longitudinal driving distance as the X-axis and the real-time center of gravity offset as the Y-axis, a center of gravity offset curve is constructed. Multiple sets of continuous center of gravity offset curves are superimposed and enveloped to generate the maximum center of gravity offset envelope.

[0029] Using the longitudinal starting point of the trestle as the origin of the coordinate system, multiple continuous sampling intervals are divided along the longitudinal direction of the bridge with a preset engineering step length.

[0030] For any sampling interval, obtain the maximum centroid offset value of the envelope of the maximum centroid offset within the sampling interval. If the maximum centroid offset value is greater than or equal to the offset safety threshold, extract the geometric midpoint of the sampling interval corresponding to the maximum centroid offset value as the centroid offset coordinate point, obtain all centroid offset coordinate points, and form a set of centroid offset coordinate points.

[0031] Preferably, the process for identifying the stress-weak region is as follows:

[0032] The spatial clustering algorithm DBSCAN is used to cluster the centroid offset coordinate points in the set of centroid offset coordinate points.

[0033] Set the cluster neighborhood radius and the minimum number of samples contained in the core point, and calculate the spatial Euclidean distance between each centroid offset coordinate point;

[0034] The centroid offset coordinates of points whose spatial Euclidean distance is less than the cluster neighborhood radius and whose number of samples is greater than the minimum number of samples contained in the core point are classified into high-frequency overlapping clusters.

[0035] The sampling intervals corresponding to the centroid offset coordinates of each high-frequency overlapping cluster are obtained. The starting boundary coordinates of the sampling interval at the front of the high-frequency overlapping cluster are extracted as the starting coordinates, and the ending boundary coordinates of the sampling interval at the back of the high-frequency overlapping cluster are extracted as the ending coordinates. The starting coordinates and the ending coordinates are spliced ​​together to form a continuous centroid offset interval of high-frequency overlap, which is identified as a stress-weak interval.

[0036] Preferably, the specific process for generating the candidate weak nodes is as follows:

[0037] Obtain the alignment parameters of the target trestle bridge to be designed. The alignment parameters include: the straight length of the trestle bridge, the radius of curvature of the curved section of the trestle bridge, and the slope of the longitudinal slope section of the trestle bridge.

[0038] Obtain the straight length of the historical trestle bridge of the same type, obtain the longitudinal coordinate values ​​corresponding to the starting point coordinates and the ending point coordinates in the stress weak zone, divide them by the straight length respectively, and calculate the relative position ratio range.

[0039] Obtain the straight length of the target trestle to be designed, and multiply the relative position ratio interval by the straight length of the target trestle to obtain the initial target longitudinal interval;

[0040] If the absolute value of the difference between the radius of curvature of the historical similar trestle curve segment and the radius of curvature of the target trestle curve segment is less than the radius of curvature threshold, and the absolute value of the difference between the slope of the historical similar trestle longitudinal slope segment and the slope of the target trestle longitudinal slope segment is less than the slope threshold, then the initial target longitudinal interval is confirmed as the final target longitudinal interval.

[0041] In the initial BIM model of the target trestle bridge to be designed, obtain all standard module connection nodes that fall within the final target longitudinal interval, and mark the standard module connection nodes as candidate weak nodes.

[0042] Preferably, the specific process for determining the actual stress weak point is as follows:

[0043] In the finite element analysis model of the target trestle bridge to be designed, an asymmetric torsional moment load corresponding to the extreme point of continuous centroid offset is applied to the candidate weak nodes.

[0044] Calculate the local torsional stiffness reserve coefficient of the candidate weak node under asymmetric torsional moment load. If the local torsional stiffness reserve coefficient is less than the safety threshold, the candidate weak node is determined to be a real stress weak point.

[0045] Among them, the local torsional stiffness reserve coefficient is the asymmetric torsional moment load obtained by converting the ultimate torsional bearing capacity of the node before plastic yielding into the maximum real-time center of gravity offset.

[0046] Preferably, the specific process for generating the final trestle design scheme is as follows:

[0047] In the initial 3D design model of the target trestle bridge, the standard connection modules at the actual stress weak points are deleted and replaced with asymmetric torsional reinforcement modules.

[0048] The second objective of this invention is to provide a modular prefabricated steel structure trestle design system, comprising the following modules:

[0049] The coupled model construction module is used to obtain the driving speed sequence and real-time center of gravity offset of historical vehicles transporting similar trestle bridges, perform linear analysis on the driving speed sequence and real-time center of gravity offset, and construct a driving speed-center of gravity offset coupled model.

[0050] The center of gravity coordinate extraction module is used to calculate the continuous center of gravity offset of different transport vehicles when driving on the same type of trestle in history, based on the driving speed-center of gravity offset coupling model, and extract the bridge surface coordinates corresponding to the extreme points of the continuous center of gravity offset to form a set of center of gravity offset coordinate points.

[0051] The stress-weak section identification module is used to extract high-frequency overlapping center-of-gravity offset sections based on the set of center-of-gravity offset coordinate points and to identify the stress-weak sections of historically similar trestle bridges.

[0052] The weak node mapping module is used to obtain the linear parameters of the target trestle to be designed. It uses a normalized coordinate mapping algorithm to proportionally map the stress weak regions to the corresponding positions of the target trestle to be designed, thereby generating candidate weak nodes.

[0053] The credibility analysis module is used to perform structural safety credibility analysis on candidate weak nodes. If the credibility analysis fails, the candidate weak node is determined to be a real stress weak point.

[0054] The design generation module is used to replace the standard connection modules at the actual stress weak points in the initial design model of the target trestle with asymmetric anti-torsional reinforcement modules, thereby generating the final trestle design scheme.

[0055] The beneficial effects of this invention are as follows:

[0056] This invention, driven by historical engineering data, transfers the real-world operational experience of existing trestle bridges to the design of new trestle bridges, solving the problem of the disconnect between traditional static calculations and actual dynamic working conditions. This significantly improves design accuracy and reliability. Based on a speed-center-of-gravity offset coupling model and cluster analysis, it can accurately identify high-frequency stress weak zones under dynamic loads, avoiding blindly equalizing strength reinforcement and effectively reducing steel consumption and engineering costs. By employing normalized coordinate mapping to achieve proportional transfer of historical experience to the target trestle bridge, it greatly improves the design efficiency of modular trestle bridges and shortens the design cycle. Through structural safety reliability analysis of candidate weak nodes, it accurately locates the actual stress weak points and adopts targeted asymmetric torsional reinforcement modules, significantly improving the local torsional stiffness and overall structural safety of the trestle bridge and reducing the risk of node fatigue damage. Attached Figure Description

[0057] The invention will now be further described with reference to the accompanying drawings.

[0058] Figure 1 This is a flowchart illustrating the steps of a modular prefabricated steel structure trestle bridge design method according to the present invention;

[0059] Figure 2 This is a system module diagram of a modular prefabricated steel structure trestle bridge design system according to the present invention. Detailed Implementation

[0060] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0061] Example 1

[0062] like Figure 1 As shown in the embodiment of the present invention, a modular prefabricated steel structure trestle design method is proposed. This method involves in-depth analysis and algorithmic modeling of vehicle speed sequence data and center of gravity offset data collected by IoT sensors from historical trestle structures of the same type. Combined with spatial clustering analysis, normalized coordinate mapping, and finite element simulation verification, the method automatically reconstructs and outputs a digital design scheme for the trestle, including asymmetric reinforcement features, within the BIM (Building Information Modeling) system. The computer-aided design method specifically includes the following steps:

[0063] Step S10: Obtain the driving speed sequence and real-time center of gravity offset of historical similar trestle transport vehicles, perform linear analysis on the driving speed sequence and real-time center of gravity offset, and construct a driving speed-center of gravity offset coupling model.

[0064] In some embodiments, the specific steps in S10 for obtaining the driving speed sequence and center of gravity offset of historical similar trestle transport vehicles are as follows:

[0065] Specifically, by retrieving the dynamic weighing system (WIM) installed on the historically similar trestle bridge and the vehicle-mounted inertial navigation system (IMU) on the transport vehicle, real-time speed data and car body tilt angle data of the transport vehicle when it is traveling on the historically similar trestle bridge are collected simultaneously.

[0066] Based on the tilt angle data of the carriage, the cargo loading height of the transport vehicle is obtained, and the center of gravity offset of the transport vehicle on the cross section is obtained by trigonometric function calculation.

[0067] The real-time speed data is sorted according to timestamps to generate a driving speed sequence, and the center of gravity offset is time-aligned with the driving speed sequence to obtain the corresponding real-time center of gravity offset.

[0068] For example, the specific process of calculating the center of gravity offset of a transport vehicle on a cross-section using trigonometric functions is as follows:

[0069] The onboard inertial navigation system (IMU) collects the lateral tilt angle of the transport vehicle's cargo compartment in real time, denoted as... Obtain the cargo loading height of the transport vehicle, denoted as... When the transport vehicle tilts laterally, the center of gravity of the cargo shifts synchronously with the tilt of the cargo compartment. The cargo loading height is determined based on the cargo compartment's load-bearing surface. Lateral tilt angle of the carriage With center of gravity offset Forming a right triangle, where the cargo loading height The adjacent side of the right triangle, the centroid offset The opposite sides of the right triangle, the lateral tilt angle of the carriage Let be the angle between the adjacent side and the hypotenuse. Based on the above geometric relationships, a formula for calculating the centroid offset is established. ;

[0070] In some embodiments, the specific steps for performing linear analysis on the driving speed sequence and center of gravity offset in S10 are as follows:

[0071] Specifically, based on the real-time center of gravity offset and the driving speed sequence, with the driving speed sequence as the independent variable sequence and the real-time center of gravity offset after time alignment as the dependent variable sequence, the correlation coefficient between the independent variable sequence and the dependent variable sequence is calculated using the Pearson correlation coefficient method.

[0072] Compare the calculated correlation coefficient with a correlation coefficient threshold (e.g., 0.7);

[0073] If the calculated correlation coefficient is greater than or equal to the correlation coefficient threshold, it indicates that the real-time center of gravity offset is linearly related to the driving speed.

[0074] If the calculated correlation coefficient is less than the correlation coefficient threshold, it indicates that the real-time center of gravity offset has a non-linear relationship with the driving speed.

[0075] In some embodiments, the specific steps for constructing the driving speed-center of gravity offset coupling model in S10 are as follows:

[0076] Specifically, a driving speed-center of gravity offset coupling model is constructed based on the linear analysis results of the driving speed sequence and the real-time center of gravity offset.

[0077] If the real-time center of gravity offset is linearly related to the driving speed, then the least squares method is used to fit the linear relationship between the real-time center of gravity offset and the driving speed to construct a driving speed-center of gravity offset coupled model.

[0078] If the real-time center of gravity offset and driving speed have a nonlinear relationship, a nonlinear fitting method is used to fit the nonlinear relationship between the real-time center of gravity offset and driving speed to construct a driving speed-center of gravity offset coupling model. Among them, the nonlinear fitting method includes, but is not limited to, nonlinear least squares method, polynomial fitting method, exponential fitting method, and logarithmic fitting method. By using the above different nonlinear fitting methods to fit the correlation data between center of gravity offset and driving speed, the goodness of fit R² corresponding to each fitting method is calculated, and the nonlinear fitting method with the highest goodness of fit (goodness of fit R²≥0.95) is selected to construct the driving speed-center of gravity offset coupling model.

[0079] It should be noted that the aforementioned historical identical trestle bridges refer to modular prefabricated steel structure trestle bridges that have been built and put into actual operation in previous projects and that use the same basic structural system as the target trestle bridge to be designed (i.e., both use standardized prefabricated steel structure modules and the same node connection method).

[0080] It should be understood that the steps involve linearly analyzing the speed sequence and center of gravity offset of historical vehicles transporting similar trestle bridges to construct a speed-center of gravity offset coupled model. The main purpose of this model is to provide reliable center of gravity offset data and model support for the future.

[0081] Step S20: Based on the driving speed-center of gravity offset coupling model, calculate the continuous center of gravity offset of different transport vehicles when driving on the same type of trestle in history, and extract the bridge surface coordinates corresponding to the extreme points of continuous center of gravity offset to form a set of center of gravity offset coordinate points.

[0082] In some embodiments, the specific steps in S20 for calculating the continuous center of gravity offset of different transport vehicles when traveling on the same type of trestle bridge in history, based on the driving speed-center of gravity offset coupling model, and extracting the bridge surface coordinates corresponding to the extreme points of the continuous center of gravity offset to form a set of center of gravity offset coordinate points are as follows:

[0083] Specifically, the system obtains a series of continuous driving speed sequences and corresponding timestamps of different transport vehicles traveling on the same type of historical trestle bridge; inputs the continuous driving speed sequences into the driving speed-center of gravity offset coupling model, and outputs a series of continuous real-time center of gravity offsets corresponding to different transport vehicles.

[0084] Based on the continuous driving speed sequence and the corresponding timestamps, the real-time longitudinal driving distance of the transport vehicle from the longitudinal starting point of the trestle is obtained by integrating (or discretely accumulating) the speed over time.

[0085] Based on any set of continuous real-time center of gravity offsets, with the real-time longitudinal travel distance as the X-axis and the real-time center of gravity offset as the Y-axis, a center of gravity offset curve in the spatial domain is constructed; multiple sets of continuous center of gravity offset curves are superimposed and enveloped to generate the maximum center of gravity offset envelope of the entire historical trestle of the same type.

[0086] Using the longitudinal starting point of the trestle as the origin of the coordinate system, multiple continuous sampling intervals are divided along the longitudinal direction of the bridge at preset engineering step lengths (e.g., 1 meter or the length of a single standard module).

[0087] For any sampling interval, since the X-axis of the envelope of the maximum centroid offset has the same dimension as the sampling interval, the maximum centroid offset of the envelope of the maximum centroid offset within the sampling interval is obtained, and the maximum centroid offset is compared with the offset safety threshold.

[0088] If the maximum value of the centroid offset within the sampling interval is greater than or equal to the offset safety threshold, then the geometric midpoint of the sampling interval corresponding to the maximum value of the centroid offset is extracted as the centroid offset coordinate point; if the maximum value of the centroid offset within the sampling interval is less than the offset safety threshold, then the maximum value of the centroid offset is discarded.

[0089] Obtain all the centroid offset coordinate points that have passed the threshold filtering and form a set of centroid offset coordinate points;

[0090] It should be understood that the center of gravity offset coordinate point does not represent an absolutely precise physical coordinate point on a microscopic level, but rather represents the center position of the macroscopic structural section (i.e., a standard module or flange connection node area) that bears the most severe asymmetric off-center load in the modular prefabricated steel structure trestle bridge, which serves as the anchor point for subsequent cluster analysis and identification of stress-weak sections.

[0091] It should be noted that the offset safety threshold refers to the maximum allowable lateral off-center load distance obtained by mechanical calculation based on the initial torsional stiffness and allowable stress of the material of the standard connection module of the trestle. When the offset of the center of gravity of the transport vehicle is greater than or equal to this threshold, it indicates that the transient torsional shear stress generated inside the standard connection module under the off-center load condition has approached or exceeded its elastic bearing limit, which poses an engineering risk of irreversible plastic deformation or joint tearing. Therefore, it must be extracted as a weak point for subsequent targeted reinforcement design.

[0092] Step S30: Based on the set of center of gravity offset coordinate points, a clustering analysis algorithm is used to extract the high-frequency overlapping center of gravity offset intervals and identify the stress weak intervals of historical similar trestle bridges.

[0093] In some embodiments, the specific steps in S30 for identifying the stress-weak areas of historically similar trestle bridges by extracting frequently overlapping centroid offset intervals based on the set of centroid offset coordinate points using a clustering analysis algorithm are as follows:

[0094] Specifically, the spatial clustering algorithm (DBSCAN) is used to cluster the centroid offset coordinate points in the set of centroid offset coordinate points;

[0095] Set the cluster neighborhood radius (Eps) and the minimum number of samples contained in the core point (MinPts), and calculate the spatial Euclidean distance between each centroid offset coordinate point;

[0096] Centroid offset coordinates that have a spatial Euclidean distance less than the cluster neighborhood radius and a sample number greater than the minimum number of samples contained in the core point are divided into the same high-frequency overlapping cluster.

[0097] Obtain the sampling interval corresponding to each centroid offset coordinate point within each high-frequency overlapping cluster. Extract the starting boundary coordinates of the sampling interval at the front of the high-frequency overlapping cluster as the starting coordinates, and the ending boundary coordinates of the sampling interval at the back of the high-frequency overlapping cluster as the ending coordinates. Combine the starting coordinates and the ending coordinates to form a continuous centroid offset interval of high-frequency overlap.

[0098] All the aforementioned center of gravity offset intervals are marked as areas of high incidence of damage in historical trestle bridges when bearing transport vehicles, thereby identifying the stress-weak intervals of historical trestle bridges.

[0099] For example, assuming the sampling interval length is 1 meter, the clustering neighborhood radius (Eps) is set to 3 meters, and the minimum number of samples contained in the core point (MinPts) is 5, if the spatial clustering algorithm finds that there are 8 centroid offset coordinate points (i.e., the midpoints of the 8 sampling intervals, such as 40.5 meters, 41.5 meters...47.5 meters) that meet the clustering conditions and form a high-frequency overlapping cluster, it will not only extract 40.5 meters and 47.5 meters as boundaries, but will trace back the sampling intervals corresponding to these 8 midpoints (i.e., [40,41] to [47,48]), extract the starting boundary (40 meters) of the frontmost sampling interval and the ending boundary (48 meters) of the last sampling interval, thus forming a complete centroid offset interval with a length of 8 meters;

[0100] It should be understood that, in practical engineering physics, this usually means that the 40-48 meter section is located at the sharp bend of the trestle bridge or the crosswind valley entrance, causing multiple different transport vehicles to experience severe center of gravity shifts when passing through this section. Through this sampling interval-based clustering and boundary restoration method, the system can automatically filter out accidental and sporadic offset points (i.e., noise), accurately locate and restore the physical span of the real stress-weak sections that are repeatedly damaged due to bridge alignment or environmental factors.

[0101] Step S40: Obtain the linear parameters of the target trestle to be designed, and use the normalized coordinate mapping algorithm to proportionally map the stress weak zone to the corresponding position of the target trestle to be designed, thereby generating candidate weak nodes.

[0102] In some embodiments, in step S40, the specific steps for obtaining the linear parameters of the target trestle to be designed, and using a normalized coordinate mapping algorithm to proportionally map the stress-weak regions to the corresponding positions of the target trestle to be designed, and generating candidate weak nodes, are as follows:

[0103] Specifically, the alignment parameters of the target trestle bridge to be designed are obtained, including: the straight length of the trestle bridge, the radius of curvature of the curved section of the trestle bridge, and the slope of the longitudinal slope section of the trestle bridge.

[0104] Obtain the straight line length of the historical trestle of the same type, obtain the longitudinal coordinate value corresponding to the starting point coordinate and the longitudinal coordinate value corresponding to the ending point coordinate in the stress weak zone, divide them by the straight line length respectively, and calculate the relative position ratio range (i.e., the normalized range, with a value range of 0 to 1) to eliminate the absolute coordinate misalignment caused by the difference in the straight line length of different trestles.

[0105] Obtain the straight length of the target trestle to be designed (i.e. the total length of the new bridge), multiply the relative position ratio interval by the straight length of the target trestle to be designed, and calculate the initial target longitudinal interval mapped on the target trestle to be designed;

[0106] For example, assuming the straight length of the historical trestle bridge is 100 meters, the longitudinal coordinate value corresponding to the starting point coordinate of the stress weak section identified by the preliminary steps is 40 meters and the longitudinal coordinate value corresponding to the ending point coordinate is 48 meters.

[0107] Dividing the longitudinal coordinate values ​​corresponding to the starting point and the ending point by the straight line length of 100 meters respectively yields a relative position ratio range of [0.4, 0.48]. If the design straight line length of the target trestle bridge (i.e., the total length of the new bridge) is 200 meters, multiply this ratio range by 200 meters to calculate the initial target longitudinal range mapped onto the target trestle bridge as [80 meters, 96 meters]. This mapping method, which normalizes the endpoint coordinates separately, not only proportionally scales the physical span of the weak section but also accurately preserves the relative position information of the section in the overall bridge structure, effectively avoiding the problem of losing the position anchor point if only the length difference is calculated.

[0108] Extract the historical linear features of historical identical trestle bridges within the stress-weak interval, and perform feature similarity matching between the historical linear features and the linear parameters of the target trestle bridge to be designed within the initial target longitudinal interval;

[0109] If the absolute value of the difference between the radius of curvature of the curve segment of the historical similar trestle and the radius of curvature of the curve segment of the target trestle is less than the radius of curvature threshold, and the absolute value of the difference between the slope of the longitudinal slope segment of the historical similar trestle and the slope of the longitudinal slope segment of the target trestle is less than the slope threshold, then it indicates that the historical similar trestle and the target trestle match in terms of feature similarity.

[0110] If the feature similarity between the historical identical trestle and the target trestle to be designed matches, then the initial target longitudinal interval is confirmed as the final target longitudinal interval;

[0111] In the initial BIM model of the target trestle bridge to be designed, all standard module connection nodes that fall within the final target longitudinal interval are obtained, and these standard module connection nodes are marked as candidate weak nodes, thereby realizing the migration of historical experience data across the bridge based on relative position and geometric features.

[0112] It should be noted that the curvature radius threshold and the slope threshold are engineering tolerance ranges pre-calibrated based on the dynamic sensitivity of special transport vehicles. Due to the limitations of different engineering terrains, it is difficult to achieve absolute consistency in the local alignment of the new and old trestle bridges. The above thresholds are introduced to determine that the geometric shapes of the two are equivalent to the mechanical excitation effects (such as centrifugal force in curves and gravity components of slope inclination) caused by the shift of the vehicle's center of gravity within the allowable engineering error range (e.g., curvature radius error within ±5% and slope error within ±1%).

[0113] It should be understood that this step, through a mapping mechanism that combines relative position normalization with dual verification of line type features, effectively avoids the mismatch problem caused by relying solely on length ratio mapping (for example, incorrectly mapping weak points at the curves of the old bridge to the straight sections of the new bridge). This ensures that the extracted candidate weak nodes not only correspond in macroscopic position, but also maintain a high degree of consistency in the underlying physical mechanism that induces the shift of the center of gravity, greatly improving the scientificity and accuracy of historical damage experience in bridge migration.

[0114] Step S50: Perform structural safety reliability analysis on the candidate weak nodes. If the reliability analysis fails, the candidate weak nodes are determined to be actual stress weak points.

[0115] In some embodiments, in step S50, the structural safety reliability analysis is performed on the candidate weak nodes. If the reliability analysis fails, the specific steps for determining that the candidate weak node is a real stress weak point are as follows:

[0116] Specifically, in the finite element analysis model of the target trestle bridge to be designed, an asymmetric torsional moment load corresponding to the extreme point of the continuous center of gravity offset is applied to the candidate weak node.

[0117] Run the finite element solver to calculate the local torsional stiffness reserve coefficient of the candidate weak node under the action of the asymmetric torsional moment load, wherein the local torsional stiffness reserve coefficient = the ultimate torsional bearing capacity of the node before plastic yielding ÷ the maximum real-time center of gravity offset converted to the asymmetric torsional moment load.

[0118] It should be noted that the specific process of converting the maximum real-time center of gravity offset into the asymmetric torsional moment load is as follows:

[0119] Obtain the total load of the transport vehicle corresponding to the maximum real-time center of gravity offset (which can be obtained through the aforementioned dynamic weighing system WIM); introduce a dynamic amplification factor (used to characterize the impact effect of vehicle dynamic movement on the bridge, typically ranging from 1.1 to 1.3); using the total load as the vertical concentrated force and the maximum real-time center of gravity offset as the eccentric lever arm, multiply the total load, the maximum real-time center of gravity offset, and the dynamic amplification factor to calculate the asymmetric torsional moment load acting at the node;

[0120] The calculated local torsional stiffness reserve coefficient is used as the verification result and compared with the safety threshold.

[0121] If the local torsional stiffness reserve coefficient is less than the safety threshold, it indicates that the node has a risk of torsional yielding under actual working conditions, and the structural safety reliability analysis fails. In this case, the candidate weak node is determined to be a real stress weak point. If the local torsional stiffness reserve coefficient is greater than or equal to the safety threshold, the structural safety reliability analysis passes, and the node is determined to be a safe node and is removed.

[0122] For example, suppose that in a finite element analysis model, for a candidate weak node (e.g., a standard flange connection node), an asymmetric torsional moment load of 500 kN·m is applied, which is converted from the maximum value of the real-time center of gravity offset. The finite element solver calculates that the ultimate torsional bearing capacity of the node before plastic yielding is 550 kN·m. From this, the local torsional stiffness reserve coefficient of the node is calculated to be 1.1 (i.e., the ultimate bearing capacity of 550 kN·m divided by the actual applied load of 500 kN·m).

[0123] If the safety threshold is 1.15, since the calculated reserve coefficient of 1.1 is less than the safety threshold of 1.15, it means that the safety redundancy of this node is less than 15% when subjected to extreme off-center loads from special vehicles, and there is a great risk of torsional yielding or fatigue tearing.

[0124] Therefore, the structural safety reliability analysis fails, and the node is identified as a real stress weak point for subsequent targeted reinforcement. Conversely, if the calculated ultimate torsional bearing capacity is 600 kN·m and the corresponding reserve coefficient is 1.2, which is greater than the safety threshold of 1.15, it means that the initial torsional stiffness of the node itself is sufficient to resist the eccentric load, and it is identified as a safe node and removed.

[0125] It should be noted that the safety threshold is derived from the lower limit of the torsional safety factor set for a specific steel grade in the national steel structure design code (e.g., set to 1.15 or 1.2). This safety threshold represents the minimum safety redundancy of the trestle node in resisting plastic deformation and fatigue damage when subjected to extreme eccentric loads. When the local torsional stiffness reserve coefficient is less than this safety threshold, it means that although the node is safe under the static load condition of normal center-of-gravity driving, its safety redundancy has been exhausted under the dynamic torsional moment generated by the extreme center-of-gravity shift of special vehicles. It is very easy for flange tearing or high-strength bolt shearing to occur. Therefore, it must be identified as a real stress weak point so that targeted reinforcement can be carried out in subsequent steps.

[0126] Step S60: In the initial design model of the target trestle to be designed, the standard connection module at the actual stress weak point is replaced with an asymmetric anti-torsion reinforcement module to generate the final trestle design scheme.

[0127] In some embodiments, in step S60, the specific steps for replacing the standard connection module at the actual stress weak point with an asymmetric anti-torsional reinforcement module in the initial design model of the target trestle to be designed, and generating the final trestle design scheme, are as follows:

[0128] Specifically, a prefabricated modular component database is established, which includes standard connection modules and various asymmetric torsional stiffness reinforcement modules with gradient distribution (such as box-section reinforcement nodes and flange nodes with cross torsional tie rods).

[0129] Based on the missing torsional stiffness difference at the actual stress weak point, an asymmetric torsional strengthening module that meets the stiffness compensation requirement is automatically matched and called in the prefabricated modular component database;

[0130] In the initial three-dimensional design model of the target trestle bridge, the standard connection module at the actual stress weak point is deleted, and the standard connection module is replaced in situ with the matching asymmetric anti-torsion reinforcement module.

[0131] Perform a global interference check and update the bill of materials (BOM) on the replaced overall model. After confirming that there are no errors, output the final trestle design scheme and construction drawings that include asymmetric reinforcement features.

[0132] Example 2

[0133] like Figure 2 As shown, based on the specific implementation process of Embodiment 1, the present invention also provides a modular prefabricated steel structure trestle design system, including the following modules:

[0134] The coupled model construction module is used to obtain the driving speed sequence and real-time center of gravity offset of historical vehicles transporting similar trestle bridges, perform linear analysis on the driving speed sequence and real-time center of gravity offset, and construct a driving speed-center of gravity offset coupled model.

[0135] The center of gravity coordinate extraction module is used to calculate the continuous center of gravity offset of different transport vehicles when driving on the same type of trestle in history, based on the driving speed-center of gravity offset coupling model, and extract the bridge surface coordinates corresponding to the extreme points of the continuous center of gravity offset to form a set of center of gravity offset coordinate points.

[0136] The stress-weak section identification module is used to extract high-frequency overlapping center-of-gravity offset sections based on the set of center-of-gravity offset coordinate points and to identify the stress-weak sections of historically similar trestle bridges.

[0137] The weak node mapping module is used to obtain the linear parameters of the target trestle to be designed. It uses a normalized coordinate mapping algorithm to proportionally map the stress weak regions to the corresponding positions of the target trestle to be designed, thereby generating candidate weak nodes.

[0138] The credibility analysis module is used to perform structural safety credibility analysis on candidate weak nodes. If the credibility analysis fails, the candidate weak node is determined to be a real stress weak point.

[0139] The design generation module is used to replace the standard connection modules at the actual stress weak points in the initial design model of the target trestle with asymmetric anti-torsional reinforcement modules, thereby generating the final trestle design scheme.

[0140] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A modular prefabricated steel structure trestle design method, characterized in that: include: Step S10: Obtain the driving speed sequence and real-time center of gravity offset of historical similar trestle transport vehicles, perform linear analysis on the driving speed sequence and real-time center of gravity offset, and construct a driving speed-center of gravity offset coupled model. Step S20: Based on the driving speed-center of gravity offset coupling model, calculate the continuous center of gravity offset of different transport vehicles when driving on the same type of trestle in history, and extract the bridge surface coordinates corresponding to the extreme points of continuous center of gravity offset to form a set of center of gravity offset coordinate points. Step S30: Based on the set of center of gravity offset coordinate points, a clustering analysis algorithm is used to extract the high-frequency overlapping center of gravity offset intervals and identify the stress weak intervals of historical similar trestle bridges. Step S40: Obtain the linear parameters of the target trestle to be designed, and use the normalized coordinate mapping algorithm to proportionally map the stress weak zone to the corresponding position of the target trestle to be designed, thereby generating candidate weak nodes. Step S50: Perform structural safety reliability analysis on the candidate weak nodes. If the reliability analysis fails, the candidate weak nodes are determined to be actual stress weak points. The specific process for determining the actual stress weak point is as follows: In the finite element analysis model of the target trestle bridge to be designed, an asymmetric torsional moment load corresponding to the extreme point of continuous centroid offset is applied to the candidate weak nodes. Calculate the local torsional stiffness reserve coefficient of the candidate weak node under asymmetric torsional moment load. If the local torsional stiffness reserve coefficient is less than the safety threshold, the candidate weak node is determined to be a real stress weak point. Among them, the local torsional stiffness reserve coefficient = the ultimate torsional bearing capacity of the node before plastic yielding ÷ the maximum real-time center of gravity offset, which is converted to the asymmetric torsional moment load. Step S60: In the initial design model of the target trestle to be designed, the standard connection module at the actual stress weak point is replaced with an asymmetric anti-torsion reinforcement module to generate the final trestle design scheme.

2. The modular prefabricated steel structure trestle design method according to claim 1, characterized in that, The specific process for obtaining the driving speed sequence and real-time center of gravity offset is as follows: Real-time speed data and cargo box tilt angle data of transport vehicles traveling on historically similar trestle bridges are collected through the dynamic weighing system WIM and the vehicle-mounted inertial navigation system IMU. Based on the tilt angle data of the carriage and the cargo loading height of the transport vehicle, the center of gravity offset is calculated using trigonometric functions. The real-time speed data is sorted according to timestamps to generate a driving speed sequence. The center of gravity offset is then time-aligned with the driving speed sequence to obtain the real-time center of gravity offset.

3. The modular prefabricated steel structure trestle design method according to claim 1, characterized in that, The specific process of performing linear analysis on the driving speed sequence and real-time center of gravity offset is as follows: Using the driving speed sequence as the independent variable sequence and the real-time center of gravity offset as the dependent variable sequence, the correlation coefficient between the independent variable sequence and the dependent variable sequence is calculated. If the correlation coefficient is greater than or equal to the correlation coefficient threshold, the real-time center of gravity offset and driving speed have a linear relationship. If the correlation coefficient is less than the correlation coefficient threshold, the real-time center of gravity offset and driving speed have a non-linear relationship.

4. The modular prefabricated steel structure trestle design method according to claim 1, characterized in that, The specific process of constructing the driving speed-center of gravity offset coupled model is as follows: If the real-time center of gravity offset is linearly related to the driving speed, then the least squares method is used to fit the linear relationship between the real-time center of gravity offset and the driving speed to construct a driving speed-center of gravity offset coupled model. If the real-time center of gravity offset has a non-linear relationship with the driving speed, then a non-linear fitting method is used to fit the non-linear relationship between the real-time center of gravity offset and the driving speed to construct a driving speed-center of gravity offset coupled model.

5. The modular prefabricated steel structure trestle design method according to claim 1, characterized in that, The specific process of constructing the set of centroid offset coordinate points is as follows: Obtain multiple sets of continuous driving speed sequences and corresponding timestamps of different transport vehicles when driving on the same type of historical trestle bridge. Input the continuous driving speed sequences into the driving speed-center of gravity offset coupling model and output multiple sets of continuous real-time center of gravity offsets. Based on the continuous driving speed sequence and the corresponding timestamp, the real-time longitudinal driving distance of the transport vehicle is obtained by integrating the speed over time. Based on any set of continuous real-time center of gravity offsets, with the real-time longitudinal driving distance as the X-axis and the real-time center of gravity offset as the Y-axis, a center of gravity offset curve is constructed. Multiple sets of continuous center of gravity offset curves are superimposed and enveloped to generate the maximum center of gravity offset envelope. Using the longitudinal starting point of the trestle as the origin of the coordinate system, multiple continuous sampling intervals are divided along the longitudinal direction of the bridge with a preset engineering step length. For any sampling interval, obtain the maximum centroid offset value of the envelope of the maximum centroid offset within the sampling interval. If the maximum centroid offset value is greater than or equal to the offset safety threshold, extract the geometric midpoint of the sampling interval corresponding to the maximum centroid offset value as the centroid offset coordinate point, obtain all centroid offset coordinate points, and form a set of centroid offset coordinate points.

6. The modular prefabricated steel structure trestle design method according to claim 1, characterized in that, The process for identifying the stress-weak regions is as follows: The spatial clustering algorithm DBSCAN is used to cluster the centroid offset coordinate points in the set of centroid offset coordinate points. Set the cluster neighborhood radius and the minimum number of samples contained in the core point, and calculate the spatial Euclidean distance between each centroid offset coordinate point; The centroid offset coordinates that have a spatial Euclidean distance less than the cluster neighborhood radius and a sample number greater than the minimum number of samples contained in the core point are classified into high-frequency overlapping clusters. The sampling intervals corresponding to the centroid offset coordinates of each high-frequency overlapping cluster are obtained. The starting boundary coordinates of the sampling interval at the front of the high-frequency overlapping cluster are extracted as the starting coordinates, and the ending boundary coordinates of the sampling interval at the back of the high-frequency overlapping cluster are extracted as the ending coordinates. The starting coordinates and the ending coordinates are spliced ​​together to form a continuous centroid offset interval of high-frequency overlap, which is identified as a stress-weak interval.

7. The modular prefabricated steel structure trestle design method according to claim 1, characterized in that, The specific process for generating the candidate weak nodes is as follows: Obtain the alignment parameters of the target trestle bridge to be designed. The alignment parameters include: the straight length of the trestle bridge, the radius of curvature of the curved section of the trestle bridge, and the slope of the longitudinal slope section of the trestle bridge. Obtain the straight length of the historical trestle bridge of the same type, obtain the longitudinal coordinate values ​​corresponding to the starting point coordinates and the ending point coordinates in the stress weak zone, divide them by the straight length respectively, and calculate the relative position ratio range. Obtain the straight length of the target trestle to be designed, and multiply the relative position ratio interval by the straight length of the target trestle to obtain the initial target longitudinal interval; If the absolute value of the difference between the radius of curvature of the historical similar trestle curve segment and the radius of curvature of the target trestle curve segment is less than the radius of curvature threshold, and the absolute value of the difference between the slope of the historical similar trestle longitudinal slope segment and the slope of the target trestle longitudinal slope segment is less than the slope threshold, then the initial target longitudinal interval is confirmed as the final target longitudinal interval. In the initial BIM model of the target trestle bridge to be designed, obtain all standard module connection nodes that fall within the final target longitudinal interval, and mark the standard module connection nodes as candidate weak nodes.

8. The modular prefabricated steel structure trestle design method according to claim 1, characterized in that, The specific process for generating the final trestle design is as follows: In the initial 3D design model of the target trestle bridge, the standard connection modules at the actual stress weak points are deleted and replaced with asymmetric torsional reinforcement modules.

9. A modular prefabricated steel structure trestle design system, corresponding to the method described in any one of claims 1-8, characterized in that: include: The coupled model construction module is used to obtain the driving speed sequence and real-time center of gravity offset of historical vehicles transporting the same type of trestle, perform linear analysis on the driving speed sequence and real-time center of gravity offset, and construct a driving speed-center of gravity offset coupled model. The center of gravity coordinate extraction module is used to calculate the continuous center of gravity offset of different transport vehicles when driving on the same type of trestle in history, based on the driving speed-center of gravity offset coupling model, and extract the bridge surface coordinates corresponding to the extreme points of the continuous center of gravity offset to form a set of center of gravity offset coordinate points. The stress-weak section identification module is used to extract high-frequency overlapping center-of-gravity offset sections based on the set of center-of-gravity offset coordinate points and to identify the stress-weak sections of historically similar trestle bridges. The weak node mapping module is used to obtain the linear parameters of the target trestle to be designed. It uses a normalized coordinate mapping algorithm to proportionally map the stress weak regions to the corresponding positions of the target trestle to be designed, thereby generating candidate weak nodes. The credibility analysis module is used to perform structural safety credibility analysis on candidate weak nodes. If the credibility analysis fails, the candidate weak node is determined to be a real stress weak point. The specific process for determining the actual stress weak point is as follows: In the finite element analysis model of the target trestle bridge to be designed, an asymmetric torsional moment load corresponding to the extreme point of continuous centroid offset is applied to the candidate weak nodes. Calculate the local torsional stiffness reserve coefficient of the candidate weak node under asymmetric torsional moment load. If the local torsional stiffness reserve coefficient is less than the safety threshold, the candidate weak node is determined to be a real stress weak point. Among them, the local torsional stiffness reserve coefficient = the ultimate torsional bearing capacity of the node before plastic yielding ÷ the maximum real-time center of gravity offset, which is converted to the asymmetric torsional moment load. The design generation module is used to replace the standard connection modules at the actual stress weak points in the initial design model of the target trestle with asymmetric anti-torsional reinforcement modules, thereby generating the final trestle design scheme.

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