Bridge static load test vehicle automatic load distribution method and system
By automating the generation of candidate vehicle sets and iterative loading cycles, combined with structural safety verification and cost-effectiveness scoring, the problem of low efficiency and insufficient safety in vehicle loading during bridge static load tests was solved. This achieved full-process automation and multi-objective optimization, ensuring the safety and economy of the bridge structure.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA RAILWAY SOUTHWEST SCI RES INST CO LTD
- Filing Date
- 2025-12-08
- Publication Date
- 2026-04-24
AI Technical Summary
Existing methods for static load testing of bridges using vehicle loading are inefficient, unsafe, and lack multi-objective optimization capabilities, making it difficult to achieve automated and intelligent vehicle loading, resulting in structural safety hazards and resource waste.
An automated load placement method is adopted. By inputting bridge and vehicle parameters, a candidate vehicle set is generated, and an iterative load placement cycle is performed. Combined with structural safety verification and cost-effectiveness scoring, the vehicle layout is optimized to ensure that the efficiency of the control section meets the standard and avoid the risk of exceeding the limit in the non-control section.
It has achieved full automation and intelligence in bridge static load testing, improving efficiency, ensuring structural safety, and achieving a balance between safety and economy, adapting to different bridge types and testing objectives.
Smart Images
Figure CN121920127A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of bridge engineering testing technology, specifically to an automated loading method and system for bridge static load testing vehicles. Background Technology
[0002] Static load testing of bridges is an indispensable core step in evaluating and verifying the load-bearing capacity and performance of bridge structures. It involves applying static loads (usually heavy vehicles) to the bridge deck and measuring the strain, displacement, and other responses of key structural components to assess the bridge's actual working condition and safety. In this test, the design of the vehicle loading scheme directly determines the success or failure of the test. Its core objective is to find the appropriate vehicle tonnage, quantity, and spatial location that ensures the loading efficiency of control sections (such as the maximum bending moment or shear force section) meets the specifications (e.g., 0.95-1.05).
[0003] Currently, this load design process heavily relies on engineers manually performing iterative calculations using general-purpose finite element software (such as Midas Civil and ANSYS). This traditional paradigm has three main limitations: 1. Inefficient and lacking automation: Engineers need to rely on experience to pre-determine a vehicle layout plan, input it into a finite element model to calculate the efficiency of the control section, and then repeatedly adjust the vehicle position and quantity based on the results. This "trial and error" process is cumbersome and time-consuming, and the quality of the plan heavily depends on the engineer's personal experience, making it difficult to quickly obtain the optimal solution. 2. Lagging safety control and blind spots: Traditional methods focus almost entirely on the loading efficiency of the control section, lacking systematic pre-control of the stress or strain levels of non-control sections. Typically, engineers only verify the non-control sections after obtaining a plan that meets the efficiency requirements of the control section. This "meet the standard first, then verify" sequential workflow has inherent flaws: once the verification finds that the loading efficiency of the non-control section exceeds the standard safety limit, the entire plan is rejected, all previous calculations are wasted, and it must be started over. More seriously, under complex stress conditions, the risk of exceeding the limits of the non-control section is easily overlooked, thus creating potential structural safety hazards for static load tests. Existing literature and engineering practice have confirmed that load distribution schemes generated by mature commercial software may still result in non-control section efficiency exceeding the standard by nearly 10%. 3. Lack of multi-objective collaborative optimization capabilities: An excellent load distribution scheme must not only meet technical feasibility (efficiency targets met, safety compliance met), but also consider economic efficiency (e.g., using fewer vehicles and fewer rows to reduce costs and labor hours). Traditional manual trial-and-error methods struggle to systematically and quantitatively coordinate economic indicators while meeting the aforementioned two objectives, thus failing to achieve collaborative optimization of multiple objectives including safety, efficiency, and economy.
[0004] In summary, existing methods for applying loads using vehicles in bridge static load testing have significant shortcomings in terms of efficiency, safety, and economy. Therefore, developing an intelligent system capable of automated load application, incorporating built-in safety constraints, and possessing multi-objective optimization decision-making capabilities is of paramount importance for improving the technical level of bridge load testing, ensuring test safety, and conserving social resources. Summary of the Invention
[0005] The present invention aims to overcome the shortcomings of the prior art and provide an automated loading method and system for bridge static load testing vehicles, so as to realize the automation and intelligence of the loading process, while ensuring that the loading efficiency of the control section meets the standard, strictly avoiding the risk of exceeding the limit of the non-control section, and comprehensively optimizing the economy of the scheme.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An automated load placement method for bridge static load testing vehicles includes the following steps: S1. Input bridge parameters, parametric model of test vehicles, and test constraints; bridge parameters include influence line data and lane layout information of the bridge; test constraints include target range of loading efficiency for control sections, upper limit of safe loading efficiency for non-control sections, minimum longitudinal spacing of vehicles, maximum number of vehicles in the lateral direction, and movement step size; S2. Based on the influence line data, at least one candidate vehicle set is pre-generated. The candidate vehicle set contains multiple candidate positions sorted by the contribution value of a single vehicle to the loading efficiency of the control section. S3. Position the first row of vehicles at the initial position where the load effect on the control section is greatest, and arrange the vehicles laterally based on the lane layout information and the maximum number of lateral vehicles at the initial position to form the first row load. S4. Calculate the loading efficiency of the first row of loads on the control section and at least one non-control section; S5. Construct an iterative deployment loop: S5.1. Perform structural safety verification: If the loading efficiency of any non-control section in the first row exceeds the upper limit of the loading efficiency safety limit, reduce the number of vehicles in the first row until the loading efficiency of all non-control sections does not exceed the upper limit of the loading efficiency safety limit. S5.2. Enter the main loop: If the loading efficiency of the control section reaches the target range of loading efficiency, end the loop and record the layout scheme; otherwise, dynamically load the subsequent rows of vehicles based on the candidate vehicle set. During the loading of subsequent rows of vehicles, if the loading efficiency of any non-control section exceeds the safe upper limit of loading efficiency, a structural safety check is performed: the number of vehicles in the current row is reduced to decrease the loading efficiency of the non-control section. S5.2 is executed repeatedly until the following conditions are met: the loading efficiency of the control section reaches the preset target range, and the loading efficiency of all non-control sections does not exceed the safe upper limit of loading efficiency. S6. For various types of test vehicles, repeat steps S2 to S5 to generate multiple load distribution schemes; S7. Calculate the cost-effectiveness score for multiple load distribution schemes, and output the optimal load distribution scheme based on the score results.
[0007] Furthermore, the pre-generated candidate vehicle set specifically includes: S21. Obtain the influence line data of the control section; S22. Traverse the single test vehicle along the longitudinal direction of the bridge using a moving step size; S23. For each traversal position, calculate the contribution value of the vehicle to the loading efficiency of the control section at that position based on the influence line data; S24. Sort all locations and their corresponding loading efficiency contribution values from high to low to form a candidate vehicle set.
[0008] Furthermore, in S3, the first row of vehicles is positioned at the initial position where the load effect on the control section is greatest, and lateral vehicle placement is performed at the initial position based on lane layout information and the maximum number of lateral vehicles to form the first row of loads; preferably, the first row of vehicles is positioned at the peak coordinate of the influence line of the control section.
[0009] Furthermore, dynamically loading subsequent vehicles specifically includes: S51. Select the candidate vehicle with the highest loading efficiency contribution value from the candidate vehicle set as the benchmark vehicle; S52. Perform spacing safety verification: Verify whether the longitudinal spacing between the reference vehicle and all deployed vehicles is less than the minimum longitudinal spacing; S53. If not less than, then taking the longitudinal position of the reference vehicle as the reference, at the longitudinal position, a total of [number] [units] are arranged transversely along the bridge deck. m The vehicles form a new row of vehicles, among which m The number of vehicles should not exceed the maximum number of vehicles arranged laterally. N Positive integers.
[0010] Furthermore, in S53, if the value is less than the target value, the current benchmark vehicle is automatically skipped, and the candidate vehicle with the second highest current loading efficiency contribution value is selected from the candidate vehicle set as the new benchmark vehicle, and S52 is re-executed.
[0011] Furthermore, S7 includes: S71. Calculating the score for a single test condition; S72. Summarizing the scores for all test conditions to obtain the summary score for different vehicle types; S73. Normalizing the summary score to a standard interval to obtain the converted score; S74. Calculating the cost-effectiveness score, which is the ratio of the converted score to the vehicle mass; S75. Recommending the vehicle type with the highest cost-effectiveness score as the optimal vehicle type, and outputting the load distribution scheme corresponding to the optimal vehicle type as the optimal load distribution scheme.
[0012] Furthermore, the score for a single test condition is calculated using the following formula: The score for a single test condition = (number of vehicle rows × total number of vehicles) / loading efficiency of the control section.
[0013] An automated load placement system for bridge static load testing vehicles, used to implement the method, includes: The data layer module is used to store and manage bridge parameters, parametric models of test vehicles, and test constraints. The algorithm layer module communicates with the data layer module and is used to perform candidate vehicle set generation, iterative loading loop, structural safety verification, spacing safety verification, and cost-effectiveness score calculation. The application layer module communicates and connects with the algorithm layer module, providing a graphical user interface for receiving user input, displaying the deployment process, and outputting the final deployment scheme.
[0014] Furthermore, the algorithm layer module includes: The test vehicle definition module is used to build and manage a parametric vehicle mathematical model library based on the test vehicle parametric model; The test parameter definition module is used to receive and configure test constraints; An automated deployment algorithm execution module is used to run an iterative deployment process to generate multiple feasible deployment schemes; The cost-effectiveness scoring module, connected to the automated deployment algorithm execution module, is used to receive multiple feasible deployment schemes, perform quantitative evaluation and comparison, and generate the optimal deployment scheme.
[0015] Furthermore, the test vehicle definition module is also used to define a variety of test vehicles, including standard models and general-purpose models; among them, the general-purpose models support user-defined key parameters, including axle load and wheelbase.
[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The entire deployment process has been automated and intelligent, significantly improving efficiency.
[0017] Traditional methods heavily rely on manual calculations and experience-based judgment by engineers, a process that is cumbersome and time-consuming. This invention automates the entire process from parameter input to optimal solution generation through automated deployment, significantly improving solution generation efficiency.
[0018] 2. The introduction of full-process safety constraint verification fundamentally eliminates potential structural safety hazards.
[0019] Traditional methods focus only on the loading efficiency of control sections, lacking systematic pre-emptive control over the safety risks of non-control sections, resulting in a safety blind spot during post-compliance verification. This invention uses real-time monitoring of the loading efficiency of non-control sections and geometric verification of vehicle spacing as the core constraints for iteration, proactively avoiding the risk of exceeding limits during loading and ensuring structural safety throughout the entire test process.
[0020] 3. It achieves multi-objective collaborative optimization, and the output solution is both safe and economical.
[0021] Traditional methods struggle to balance technical objectives with economic efficiency. This invention utilizes a quantitative cost-effectiveness scoring system to uniformly quantify key indicators such as the number of vehicle rows, total number of vehicles, and loading efficiency. It can automatically recommend the deployment scheme with the lowest overall cost and most efficient resource utilization while meeting all safety and technical specifications, achieving an optimal balance between safety, efficiency, and economy.
[0022] 4. It possesses strong engineering applicability and flexibility.
[0023] This invention, through parameterized vehicle models, configurable test constraints, and automatic comparison of multiple vehicle models, can flexibly adapt to the load requirements of different bridge types and test purposes, ranging from standard beam bridges to complex rigid frame bridges.
[0024] 5. A complete technological closed loop from analysis to decision-making has been formed.
[0025] Compared to finite element software that only provides calculation functions, this invention constructs a complete system that integrates data processing, intelligent algorithms, security verification, and optimization decision-making. Attached Figure Description
[0026] Figure 1 This is a flowchart of the method of the present invention.
[0027] Figure 2 This is a perspective view of the bridge as an example.
[0028] Figure 3 This is a plan view of the bridge as an example.
[0029] Figure 4 The figure shows the loading efficiency results of the non-control section in test condition 3 of the embodiment. Detailed Implementation
[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0031] like Figure 1 As shown, the present invention provides an automated load placement method for bridge static load testing vehicles, comprising the following steps: S1. Input bridge parameters, parametric model of test vehicle, and test constraints; S2. Based on the influence line data, at least one candidate vehicle set is pre-generated. The candidate vehicle set contains multiple candidate positions sorted by the contribution value of a single vehicle to the loading efficiency of the control section. S3. Position the first row of vehicles at the initial position where the load effect on the control section is greatest, and arrange the vehicles laterally based on the lane layout information and the maximum number of lateral vehicles at the initial position to form the first row load. S4. Calculate the loading efficiency of the first row of loads on the control section and at least one non-control section; S5. Construct an iterative deployment loop: S5.1. Perform structural safety verification: If the loading efficiency of any non-control section in the first row exceeds the upper limit of the loading efficiency safety limit, reduce the number of vehicles in the first row until the loading efficiency of all non-control sections does not exceed the upper limit of the loading efficiency safety limit. S5.2. Enter the main loop: If the loading efficiency of the control section reaches the target range of loading efficiency, end the loop and record the layout scheme; otherwise, dynamically load the subsequent rows of vehicles based on the candidate vehicle set. During the loading of subsequent rows of vehicles, if the loading efficiency of any non-control section exceeds the safe upper limit of loading efficiency, a structural safety check is performed: the number of vehicles in the current row is reduced to decrease the loading efficiency of the non-control section. S5.2 is executed repeatedly until the following conditions are met: the loading efficiency of the control section reaches the preset target range, and the loading efficiency of all non-control sections does not exceed the safe upper limit of loading efficiency. S6. For various types of test vehicles, repeat steps S2 to S5 to generate multiple load distribution schemes; S7. Calculate the cost-effectiveness score for multiple load distribution schemes, and output the optimal load distribution scheme based on the score results.
[0032] In this invention, S1 represents the input of engineering parameters, including bridge parameters, a parametric model of the test vehicle, and test constraints. Bridge parameters define the physical and mechanical properties of the test object and are the foundation of all calculations, including influence line data and lane layout information. Influence line data of internal forces and displacements at all control and non-control sections are obtained through unit load analysis using the finite element model, providing a basis for subsequent load placement calculations. The parametric model of the test vehicle, centered on parameters such as axle load, wheelbase, track width, and gross vehicle weight, is used to accurately reconstruct the physical and load properties of the test vehicle in digital space, providing a unified and standardized load input benchmark for all subsequent automated calculations. Test constraints set optimization targets and boundaries for subsequent calculations, including target ranges for loading efficiency at control sections, safe upper limits for loading efficiency at non-control sections, minimum longitudinal spacing between vehicles, maximum number of vehicles in the lateral direction, and movement step size. Depending on the test objectives, target ranges for loading efficiency at control sections are set: 0.85~1.05 for bridges undergoing final acceptance testing; and 0.95~1.05 for bridges in service. The upper limit for the loading efficiency of non-controlled sections is usually set to 1.05.
[0033] To enable multi-option comparison and optimization, the test constraints also include user-defined ranges and step sizes for the total weight variation of the test vehicles. Based on these ranges and step sizes, a series of candidate vehicle models with different tonnages are automatically generated for calculation, effectively classifying vehicles into multiple types. For example, the user sets a range for the total weight variation of a vehicle (from 30t to 45t) and a step size (3t). The system automatically generates a set of vehicle types for comparison based on these parameters: [30t, 33t, 36t, 39t, 42t, 45t]. The subsequent automated load deployment process (from generating the candidate set to iterative load deployment) will calculate for each vehicle type, generating multiple alternative solutions, ultimately leading to a cost-effectiveness evaluation stage.
[0034] In this invention, S2 involves generating the candidate vehicle set. The core significance of the candidate vehicle set lies in its transformation from blind trial-and-error search to targeted intelligent optimization through pre-computation and global sorting. Specifically, it provides the iterative algorithm with a globally optimal search path, ensuring it always prioritizes exploring the most efficient vehicle positions. When the optimal position is excluded due to safety constraints, the algorithm can immediately retrieve a second-best option from the set, guaranteeing search efficiency. By replacing repeated evaluations in iterations with a one-time computation, computationally intensive tasks are pre-emptively addressed, significantly improving optimization speed. This design is the key differentiator of this invention from traditional manual trial-and-error and ordinary iterative methods.
[0035] In one embodiment, the pre-generated candidate vehicle set specifically includes: S21. By applying a unit load to the finite element model and performing static analysis, the influence line data of the internal forces (such as bending moment and shear force) or displacements of the control section under the current test conditions are calculated. This influence line data is stored in the form of a discrete array, containing the effect values of the unit force at each element node or calculation point of the bridge.
[0036] S22. Initialize an empty list to store candidate locations. Then, using a designated axle of the currently selected test vehicle (usually the center or rear axle that produces the maximum effect) as a positioning reference point, start from the bridge's starting coordinates and move towards the bridge's ending coordinates at preset step intervals (e.g., 0.1 meters, 0.5 meters). Each longitudinal coordinate reached during this movement is a candidate location to be evaluated.
[0037] S23. For each candidate position in S22 i Perform the following calculations: Determine the position of each axle: Based on the vehicle's wheelbase parameters, calculate the position of all axles. i The precise bridge deck coordinates at that time; Interpolation to obtain influence line coordinates: For the coordinates of each axle, interpolation (such as linear interpolation) is performed on the influence line data of the control section to obtain the influence line coordinate value corresponding to the position of that axle.
[0038] Calculate the single-vehicle effect: According to the formula: Total vehicle load effect ( p ) = Σ(the first) k Axle load × the first k (Coordinates of the influence line corresponding to the axis) to calculate the vehicle's position. i The total load effect on the control section.
[0039] Calculate the efficiency contribution value: Divide the total vehicle load effect by the design live load effect value of the control section to obtain the loading efficiency contribution value of the vehicle at this position.
[0040] S24. After completing the traversal and calculation for all locations, a list containing all (location coordinates, loading efficiency contribution value) tuples will be obtained. Then, this list will be sorted in descending order based on the loading efficiency contribution value field. The sorted list is the final set of candidate vehicles.
[0041] S3 of this invention is the formation of the first row of loads, which is the starting step of automated load deployment. Specifically, it includes: S31. From the candidate vehicle set, read the candidate position with the highest loading efficiency contribution value; determine this position as the optimal position that maximizes the load effect of a single vehicle on the control section, and use it as the initial deployment position for the first row of vehicles; S32. Read the bridge's lane layout information and obtain the lateral coordinates of the centerlines of all available lanes; calculate the maximum number of vehicles that can be accommodated in a single row based on the vehicle's wheelbase, width, and safety margin. n , n The number of vehicles should not exceed the maximum number of vehicles arranged laterally. N Positive integers; select the lane that best benefits the overall structural response (usually the lane that is symmetrical or close to the lane with the largest lateral distribution of the influence line peak), which will have the most N The vehicles are evenly or in an optimal layout on different lanes at this longitudinal position to form the first row of loads.
[0042] In one embodiment, the first row of vehicles is positioned at the peak coordinates of the influence line of the control section, thus determining the initial position of the first row of vehicles. This embodiment, by placing vehicles at the peak, can generate the maximum load effect on the control section with the fewest number of vehicles, thereby providing the most efficient starting point for the entire optimization process.
[0043] S4 of this invention is to quantitatively evaluate the technical feasibility and structural safety of the initially formed first-row loading scheme. The core of this step is to simultaneously calculate the response of the first row of vehicles at the control and non-control sections. Specifically, the first-row loading scheme is used as the load case, and a static analysis is performed using a finite element model to obtain the real-time response values of all monitored sections (control section and at least one non-control section). Subsequently, for each monitored section, its response value is divided by its corresponding preset live load effect value to obtain its precise loading efficiency. This step is the first to incorporate the safety verification of non-control sections into the automated process. Unlike traditional methods that only perform final checks, this invention performs global safety monitoring from the first row loading, reflecting a safety-first design philosophy and preventing the generation of loading schemes with potential safety hazards from the outset.
[0044] The iterative loading loop constructed in S5 of this invention is an automated controller that integrates optimization search and safety constraints. It dynamically constructs loading schemes through a closed-loop logic of evaluation-loading-verification-adjustment. The core of the loop lies in two types of verification: first, geometric feasibility verification (spacing safety verification) based on the candidate vehicle set, ensuring the feasibility of the scheme; second, real-time structural safety verification after each loading step, which forcibly resolves the over-limit state of non-control sections by reducing the number of vehicles, ensuring the overall bridge safety of the scheme. This loop mechanism, by embedding safety verification into each iterative decision, fundamentally ensures that the output scheme, while meeting the loading target of the control section, strictly adheres to the safety constraints of the non-control section, thereby achieving a fundamental guarantee of the overall safety of the scheme.
[0045] Specifically, the loading loop iteration process is as follows: S5.1. Perform structural safety verification: If the loading efficiency of any non-control section in the first row exceeds the upper limit of the loading efficiency safety limit, reduce the number of vehicles in the first row until the loading efficiency of all non-control sections does not exceed the upper limit of the loading efficiency safety limit. S5.2. Enter the main loop: If the loading efficiency of the control section reaches the target range, the loop ends and the layout scheme is recorded; otherwise, based on the candidate vehicle set, the subsequent rows of vehicles are dynamically loaded. During the loading of subsequent rows of vehicles, if the loading efficiency of any non-control section exceeds the safe upper limit of loading efficiency, a structural safety check is performed: by reducing the number of vehicles in the current row (e.g., reducing one or more vehicles) until the efficiency of the non-control section is less than the safe upper limit of loading efficiency. S5.2 is executed repeatedly until the following conditions are met: the loading efficiency of the control section reaches the preset target range, and the loading efficiency of all non-control sections does not exceed the safe upper limit of loading efficiency.
[0046] In one embodiment, dynamically loading subsequent rows of vehicles specifically includes: S51. Select the candidate vehicle with the highest loading efficiency contribution value from the candidate vehicle set as the benchmark vehicle; S52. Perform spacing safety verification: Verify whether the longitudinal spacing between the reference vehicle and all deployed vehicles is less than the minimum longitudinal spacing; S53. If not less than, then taking the longitudinal position of the reference vehicle as the reference, at the longitudinal position, a total of [number] [units] are arranged transversely along the bridge deck. m The vehicles form a new row of vehicles, among which m The number of vehicles should not exceed the maximum number of vehicles arranged laterally. N Positive integers.
[0047] In S53, if the value is less than the target vehicle, the current benchmark vehicle is automatically skipped, and the candidate vehicle with the second highest current loading efficiency contribution value is selected from the candidate vehicle set as the new benchmark vehicle, and S52 is re-executed.
[0048] This invention, S6, breaks through the limitation of fixed vehicle types in traditional methods by automatically traversing multiple possible vehicle resources. Specifically, it first generates a list of candidate vehicle models based on preset vehicle weight variation parameters. Then, for each vehicle type in the list, it independently executes a complete automated loading process (S2-S5), including pre-generating a dedicated set of candidate vehicles. Finally, it aggregates feasible solutions for each vehicle type under all test conditions (such as maximum positive bending moment at the side span, maximum negative bending moment at the mid-support, etc.) into a complete loading scheme that includes vehicle specification parameters (such as total vehicle weight (tons), axle load, wheelbase), detailed loading information for each test condition (such as the precise longitudinal and lateral coordinates of each vehicle, the total number of vehicles required, and the number of rows), and the corresponding loading efficiency.
[0049] It should be further clarified that the test condition in this invention refers to an independent test unit established in a static load test to verify a specific worst-case design state. A test condition is defined by the target control section, its preset target loading efficiency range, and the most unfavorable load effect to be pursued (such as the maximum bending moment). A complete static load test of a bridge typically consists of multiple test conditions to ensure a comprehensive evaluation of its load-bearing capacity.
[0050] S7 of this invention is the final decision-making stage of the system. It first performs multi-index quantitative scoring on each feasible deployment scheme and then summarizes the results. Subsequently, data normalization eliminates the influence of dimensions, and finally, a cost-effectiveness score measures a scientific balance between efficiency and economy. This process transforms complex engineering economic decisions into objective quantitative calculations, ensuring that the output results are optimally unified in terms of technical feasibility, safety, and economy.
[0051] In one embodiment, S7 includes: S71. Calculate the score for a single test condition. The score for a single test condition is calculated using the following formula: Score for a single test condition = (Number of vehicle rows × Total number of vehicles) / Loading efficiency of the control section. The product of the number of vehicle rows and the total number of vehicles comprehensively reflects the complexity and resource consumption of the scheme.
[0052] Increasing the number of rows and the total number of vehicles directly leads to increased on-site organization, vehicle rental, and operation time, i.e., increased costs. Therefore, the smaller the numerator, the better the economic efficiency. The loading efficiency of the control section reflects the load utilization efficiency of the vehicle. When the number of rows and the total number of vehicles are similar, the higher the achieved loading efficiency, the higher the technical efficiency of the solution. In summary, the lower the score of a single test condition, the higher the technical efficiency achieved with fewer resources (smaller numerator) (larger denominator), meaning the solution for that test condition is superior.
[0053] S72. Summarize the scores for all test conditions to obtain the summary scores for different vehicle types.
[0054] To facilitate comparison among multiple vehicle types, the vehicle type that meets the load requirements of all operating conditions is selected first, ensuring the uniformity and convenience of test operations. Then, for the same vehicle type, the scores calculated for each individual test condition under all test conditions are summed to obtain the overall score for that vehicle type. This overall score represents the overall resource consumption and efficiency level of using that vehicle type to complete all test tasks. The lower the overall score, the better the overall performance of that vehicle type.
[0055] S73. Normalize the total score to a standard interval to obtain the converted score.
[0056] To eliminate the influence of absolute values and ensure fair comparison, the aggregate scores of different vehicle types are linearly mapped to a preset standard interval. For example, if the preset standard interval is [60, 100], the converted score is obtained through the following normalization: Converted score = 60 + [(Current vehicle type aggregate score - Lowest aggregate score) / (Highest aggregate score - Lowest aggregate score)] * 40. In this process, the vehicle type with the lowest aggregate score (optimal) is mapped to 100 points, the vehicle type with the highest aggregate score (worst) is mapped to 60 points, and the remaining vehicle types are interpolated proportionally. Thus, the low-scoring, high-performing scheme in the original system is transformed into a high-scoring, high-performing scheme in the new system.
[0057] S74. Calculate the cost-effectiveness score, which is the ratio of the converted score to the vehicle's weight. Although vehicles with larger axle loads may have a lower initial score (summary score) and higher priority due to fewer required quantities, calculating the cost-effectiveness score balances efficiency and economy, thus recommending vehicle types that are not necessarily the heaviest in axle load but offer the best overall benefits. A higher cost-effectiveness score indicates a higher rate of economic return for that vehicle type.
[0058] S75. Recommend the vehicle type with the highest cost-effectiveness score as the optimal vehicle type, and output the load distribution scheme corresponding to the optimal vehicle type as the optimal load distribution scheme. First, sort all vehicle types according to their cost-effectiveness score from high to low. Select the vehicle type with the highest cost-effectiveness score as the optimal vehicle type. Then, extract the complete load distribution scheme corresponding to the optimal vehicle type from the scheme set formed by multiple load distribution schemes as the final output optimal load distribution scheme.
[0059] The present invention also provides an automated load placement system for bridge static load testing vehicles, used to implement the method, comprising: The data layer module is used to store and manage bridge parameters, parametric models of test vehicles, and test constraints. The algorithm layer module communicates with the data layer module and is used to perform candidate vehicle set generation, iterative loading loop, structural safety verification, spacing safety verification, and cost-effectiveness score calculation. The application layer module communicates and connects with the algorithm layer module, providing a graphical user interface for receiving user input, displaying the deployment process, and outputting the final deployment scheme.
[0060] In one embodiment, the algorithm layer module includes: The test vehicle definition module is used to build and manage a parametric vehicle mathematical model library based on the test vehicle parametric model; The test parameter definition module is used to receive and configure test constraints; An automated deployment algorithm execution module is used to run an iterative deployment process to generate multiple feasible deployment schemes; The cost-effectiveness scoring module, connected to the automated deployment algorithm execution module, is used to receive multiple feasible deployment schemes, perform quantitative evaluation and comparison, and generate the optimal deployment scheme.
[0061] In one embodiment, the test vehicle definition module is also used to define a variety of test vehicles, including standard models and general-purpose models; wherein, the general-purpose models support user-defined key parameters including axle load and wheelbase.
[0062] In this embodiment, to verify the effectiveness, safety, and economy of the present invention, an application analysis is conducted using a (60+2×100+60)m prestressed concrete continuous rigid frame bridge as the engineering background. The bridge deck is a separated double-span bridge, with each span having a width of 16.5m (0.5m crash barrier + 12.0m driving lane + 4.0m sidewalk), and a spacing of 6m between the two spans. The bridge's design load level is Highway-I, the superstructure uses prestressed concrete continuous box girder, and the substructure uses pile-column piers. The basic seismic intensity of the bridge site area is VII. The bridge's plan and elevation layout are shown below. Figure 2 and Figure 3 .
[0063] 1. Parameter settings in this embodiment.
[0064] Control sections: According to the requirements of the "Specifications for Load Testing of Highway Bridges", the design of static load test conditions must follow the principles of most unfavorable stress and representativeness. Considering the structural characteristics of continuous rigid frame bridges, three test conditions were established, as shown in Table 1, corresponding to three key control sections, to ensure a comprehensive evaluation of the performance of the vehicle safety load distribution system under different stress conditions (positive bending moment and negative bending moment).
[0065] Table 1
[0066] Test vehicle: A standard three-axle test vehicle was used. To compare multiple options, the total vehicle weight was set from 30t to 45t, with a variation step of 3t, resulting in 6 candidate vehicle models.
[0067] Test constraints: The target range for loading efficiency of the control section is set to [0.95, 1.05], the safe upper limit for loading efficiency of the non-control section is set to 1.05, and the minimum longitudinal spacing between vehicles is set to 1.0m.
[0068] 2. Implementation process and results.
[0069] The present invention (hereinafter referred to as APBLV) and the commercial finite element model (Midas Civil) were used for load calculation.
[0070] 2.1 The parameters for Midas Civil are as follows: Material definition: The main beam is made of C50 concrete, and the piers are made of C40 concrete. The material constitutive relationship is strictly defined according to the design specifications.
[0071] Element division: The entire bridge is discretized into 224 beam elements to ensure the calculation accuracy at key control sections.
[0072] Boundary conditions: The pier base is fixed, and the pier and beam are rigidly connected to accurately simulate the structural system of a continuous rigid frame bridge.
[0073] Coordinate system: The X-axis is defined as the longitudinal direction of the bridge, the Y-axis as the transverse direction of the bridge, and the Z-axis as the vertical direction, providing a clear geometric reference for load arrangement and result analysis.
[0074] 2.2 Comparison of control section loading efficiency.
[0075] Taking a 45t vehicle as an example, the detailed load distribution results of the two methods are shown in Table 2.
[0076] Table 2
[0077] As shown in Table 2, both methods achieved the target loading efficiency of the control section under all test conditions, and the results were highly consistent, verifying the accuracy of the calculations in this invention.
[0078] 2.3 Comparison of safety of non-controlled sections.
[0079] Taking the most complex test condition 3 (maximum negative bending moment at the support) as an example, the non-control sections include the main beam (elements 28# and 30#) and the pier (elements 129# and 160#), and are evaluated in conjunction with the control section (element 33#). The comparison results of the loading efficiency of each non-control section are as follows: Figure 4 As shown.
[0080] according to Figure 4 It can be seen that the loading efficiency values of all non-controlled sections calculated using this invention are 0.86, 0.99, 1.01, and 1.03, respectively, all within the safe upper limit of loading efficiency (1.05). In the results calculated using Midas Civil, the loading efficiency of three non-controlled sections exceeded the limit, with values of 1.08, 1.14, and 1.15, exceeding the safe upper limit of loading efficiency by 2.86% to 9.52%. The results show that this invention, through its built-in real-time structural safety verification mechanism, successfully avoided the risk of exceeding the limit for non-controlled sections, while traditional software has potential safety hazards under this condition.
[0081] 2.4 Comparison of economic optimization of multiple load distribution schemes.
[0082] This invention performed the complete S2-S6 process on six vehicle models (30t-45t), generating multiple load configurations, and then proceeded to S7 for cost-effectiveness evaluation. Table 3 shows the scoring results of test vehicles of different masses in test condition 3, i.e., the score of a single test condition (test condition 3). Table 4 shows the comparison results of the total score, converted score, and cost-effectiveness score of test vehicles of different masses in test condition 3.
[0083] Table 3
[0084] As shown in Table 3, under test condition 3, as the vehicle mass increases (from 30.0 tons to 45.0 tons), the number of vehicles required decreases accordingly, but the loading efficiency of the control section remains at around 1.00, indicating that the loading design is generally reasonable. At the same time, the number of vehicle rows decreases from 3 rows to 2 rows when the mass reaches 45.0 tons, and the score shows a significant downward trend, reflecting that under this evaluation system, using heavier and fewer vehicles may be detrimental to the overall score.
[0085] Table 4
[0086] As shown in Table 4, under test condition 3, the overall score decreased with increasing vehicle weight, while the converted score gradually increased and reached full marks at 45.0 tons. The cost-effectiveness score was highest at 33.0 tons (2.39), and then fluctuated downwards, indicating that medium-weight vehicles achieved a good balance between overall performance and cost-effectiveness. Therefore, a 33-ton test vehicle is recommended as the optimal vehicle type, and its corresponding complete load configuration is provided.
[0087] The systematic verification in this embodiment shows that APBLV achieves comparable accuracy to traditional commercial software in calculating the loading efficiency of control sections; APBLV effectively avoids the risk of exceeding limits at non-control sections, and its safety is significantly better than traditional methods. APBLV possesses multi-objective optimization decision-making capabilities and can automatically recommend the most cost-effective solution, while traditional software relies entirely on human experience and lacks this function.
[0088] This embodiment fully demonstrates that the present invention combines reliability, safety, and economy in bridge static load test vehicle loading, exhibiting extremely high engineering practical value.
[0089] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention used to illustrate the technical solutions of the present invention, and are not intended to limit the invention, nor are they intended to limit the patent scope of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. That is to say, any changes or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but whose technical problems are still consistent with the present invention, should be included within the protection scope of the present invention. In addition, the direct or indirect application of the technical solutions of the present invention to other related technical fields are similarly included within the patent protection scope of the present invention.
Claims
1. An automated load placement method for bridge static load testing vehicles, characterized in that, Includes the following steps: S1. Input bridge parameters, parametric model of test vehicles, and test constraints; bridge parameters include influence line data of the bridge and lane layout information; test constraints include target range of loading efficiency for control sections, upper limit of safe loading efficiency for non-control sections, minimum longitudinal spacing of vehicles, maximum number of vehicles in the lateral direction, and movement step size. S2. Based on the influence line data, at least one candidate vehicle set is pre-generated. The candidate vehicle set contains multiple candidate positions sorted by the contribution value of a single vehicle to the loading efficiency of the control section. S3. Based on the influence line of the control section, form the first row of load distribution; S4. Calculate the loading efficiency of the first row of loads on the control section and at least one non-control section; S5. Construct an iterative deployment loop: S5.
1. Perform structural safety verification: If the loading efficiency of any non-control section in the first row exceeds the upper limit of the loading efficiency safety limit, reduce the number of vehicles in the first row until the loading efficiency of all non-control sections does not exceed the upper limit of the loading efficiency safety limit. S5.
2. Enter the main loop: If the loading efficiency of the control section reaches the target range of loading efficiency, end the loop and record the layout scheme; otherwise, dynamically load the subsequent rows of vehicles based on the candidate vehicle set. During the loading of subsequent rows of vehicles, if the loading efficiency of any non-control section exceeds the safe upper limit of loading efficiency, a structural safety check is performed: the number of vehicles in the current row is reduced to decrease the loading efficiency of the non-control section. S5.2 is executed repeatedly until the following conditions are met: the loading efficiency of the control section reaches the preset target range, and the loading efficiency of all non-control sections does not exceed the safe upper limit of loading efficiency. S6. For various types of test vehicles, repeat steps S2 to S5 to generate multiple load distribution schemes; S7. Calculate the cost-effectiveness score for multiple load distribution schemes, and output the optimal load distribution scheme based on the score results.
2. The automated load placement method for bridge static load testing vehicles according to claim 1, characterized in that, The pre-generated candidate vehicle set specifically includes: S21. Obtain the influence line data of the control section; S22. Traverse the single test vehicle along the longitudinal direction of the bridge using a moving step size; S23. For each traversal position, calculate the contribution value of the vehicle to the loading efficiency of the control section at that position based on the influence line data; S24. Sort all locations and their corresponding loading efficiency contribution values from high to low to form a candidate vehicle set.
3. The automated load placement method for bridge static load testing vehicles according to claim 1, characterized in that, In S3, the first row of vehicles is positioned at the initial position where the load effect on the control section is greatest, and the vehicles are arranged laterally based on the lane layout information and the maximum number of lateral vehicles at the initial position to form the first row load; preferably, the first row of vehicles is positioned at the peak coordinate of the influence line of the control section.
4. The automated load placement method for bridge static load testing vehicles according to claim 1, characterized in that, Dynamically loading subsequent vehicles specifically includes: S51. Select the candidate vehicle with the highest loading efficiency contribution value from the candidate vehicle set as the benchmark vehicle; S52. Perform spacing safety verification: Verify whether the longitudinal spacing between the reference vehicle and all deployed vehicles is less than the minimum longitudinal spacing; S53. If not less than, then taking the longitudinal position of the reference vehicle as the reference, at the longitudinal position, a total of [number] [units] are arranged transversely along the bridge deck. m The vehicles form a new row of vehicles, among which m The number of vehicles should not exceed the maximum number of vehicles arranged laterally. N Positive integers.
5. The automated load placement method for bridge static load testing vehicles according to claim 4, characterized in that, In S53, if the value is less than the target vehicle, the current benchmark vehicle is automatically skipped, and the candidate vehicle with the second highest current loading efficiency contribution value is selected from the candidate vehicle set as the new benchmark vehicle, and S52 is re-executed.
6. The automated load placement method for bridge static load testing vehicles according to claim 1, characterized in that, S7 includes: S71. Calculating the score for a single test condition; S72. Summarizing the scores for all test conditions to obtain the summary score for different vehicle types; S73. Normalizing the summary score to a standard range to obtain the converted score; S74. Calculating the cost-effectiveness score, which is the ratio of the converted score to the vehicle mass; S75. Recommending the vehicle type with the highest cost-effectiveness score as the optimal vehicle type and outputting the load distribution scheme corresponding to the optimal vehicle type as the optimal load distribution scheme.
7. The automated load placement method for bridge static load testing vehicles according to claim 6, characterized in that, The score for a single test condition is calculated using the following formula: The score for a single test condition = (number of vehicle rows × total number of vehicles) / loading efficiency of the control section.
8. An automated load placement system for bridge static load testing vehicles, used to implement the method described in any one of claims 1-8, characterized in that, include: The data layer module is used to store and manage bridge parameters, parametric models of test vehicles, and test constraints. The algorithm layer module communicates with the data layer module and is used to perform candidate vehicle set generation, iterative loading loop, structural safety verification, spacing safety verification, and cost-effectiveness score calculation. The application layer module communicates and connects with the algorithm layer module, providing a graphical user interface for receiving user input, displaying the deployment process, and outputting the final deployment scheme.
9. The automated load distribution system for bridge static load testing vehicles according to claim 8, characterized in that, The algorithm layer module includes: The test vehicle definition module is used to build and manage a parametric vehicle mathematical model library based on the test vehicle parametric model; The test parameter definition module is used to receive and configure test constraints; An automated deployment algorithm execution module is used to run an iterative deployment process to generate multiple feasible deployment schemes; The cost-effectiveness scoring module, connected to the automated deployment algorithm execution module, is used to receive multiple feasible deployment schemes, perform quantitative evaluation and comparison, and generate the optimal deployment scheme.
10. An automated load distribution system for bridge static load testing vehicles according to claim 9, characterized in that, The test vehicle definition module is also used to define a variety of test vehicles, including standard models and general-purpose models; among them, general-purpose models support user-defined key parameters, including axle load and wheelbase.