A method for regulating the length of the outer concrete segments during the construction of a concrete arch bridge in a segmented ring to an arch state
By combining a multi-objective optimization algorithm and a non-dominated sorting genetic algorithm with a finite element model and moving loading tests, the problem of segment length control in the construction of concrete arch bridges was solved, the stress and deformation of the main arch ring were optimized, and the structural stability and economic benefits were improved.
Patent Information
- Application Number
- CN202610808561.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-04
- Publication Date
- 2026-08-25
AI Technical Summary
In the construction of concrete arch bridges, existing technologies often rely on traditional methods for determining segment lengths, which are highly subjective or labor-intensive. These methods make it difficult to optimize the stress and deformation of the main arch ring after the bridge is completed, and cannot take into account multiple conflicting objectives, leading to structural instability and cracks.
A multi-objective optimization algorithm was adopted to construct a multi-objective optimization function with the stress performance and alignment of the main arch ring after bridge completion as the objectives. Iterative optimization was carried out by combining the non-dominated sorting genetic algorithm NSGA-II. The optimal control scheme was screened out through finite element model verification and moving loading test, and the verification was carried out based on the principle of influence line superposition.
This achieved the optimization of stress and deformation of the main arch ring after the bridge was completed, improved the structural stress performance, reduced the risk of cracking, reduced material consumption and construction costs, and shortened the construction period.
Smart Images
Figure CN122634720A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of bridge engineering technology, and in particular to a method for controlling the length of the outer concrete segments during the arching process of a concrete arch bridge in the ring-shaped construction phase. Background Technology
[0002] Concrete arch bridges are a major type of long-span bridge, and their construction typically employs a segmented, ring-by-ring method for pouring the outer concrete. The segment length of the outer concrete (i.e., the working face length) is a key factor affecting the stress state and alignment of the main arch ring after completion. A well-designed working face is crucial for ensuring structural stability and safety during construction, preventing cracking, and optimizing the structural performance of the completed bridge.
[0003] Traditional methods for determining segment lengths mainly rely on engineering experience and the influence line method. The experience-based method is highly subjective and difficult to obtain the optimal solution; although the influence line method can perform mechanical analysis, it is usually labor-intensive and often focuses on a single objective (such as controlling the stress or deformation of a certain section) as the optimization object. It cannot take into account multiple conflicting objectives such as the maximum compressive stress, maximum tensile stress and vertical deformation of the main arch ring after the bridge is completed, and it is difficult to achieve the optimization of the overall structural performance.
[0004] Therefore, proposing a method for controlling the length of the outer concrete segments during the construction of a concrete arch bridge in the ring-shaped arch state is an urgent problem to be solved by those skilled in the art to address the difficulties existing in the prior art. Summary of the Invention
[0005] In view of this, the present invention provides a method for controlling the length of the outer concrete segment during the arching process of a concrete arch bridge. Based on a multi-objective optimization algorithm, it can effectively reduce the stress and deformation of the main arch ring after the bridge is completed, improve the stress distribution of the concrete, and enhance the load-bearing performance of the bridge.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for controlling the length of the outer concrete segment during the arch-forming process of a concrete arch bridge under segmental construction includes: S1. Construct a multi-objective optimization function with the stress performance and alignment of the main arch ring after bridge completion as the objective, and determine the design variables and constraints. S2. Establish and verify the finite element model of the entire construction process of the concrete arch bridge; S3. Based on the finite element model, the non-dominated sorting genetic algorithm NSGA-II is used to iteratively optimize the design variables and solve the Pareto front solution set. S4. Establish evaluation indicators, screen the Pareto front solution set, and determine the optimal control scheme; S5. The optimal control scheme is verified through a moving load test based on the principle of influence line superposition.
[0007] The above method, optionally, involves constructing a multi-objective optimization function in S1, specifically as follows: ; In the formula, For the first time after the bridge was completed i The first working face j The compressive stress of the concrete segment For the first time after the bridge was completed i The first working face j Tensile stress in the concrete section This represents the maximum compressive stress in all the concrete after the bridge is completed. This indicates the maximum tensile stress in all the concrete after the bridge is completed. This indicates the vertical displacement of the arch crown after the bridge is completed.
[0008] The above method, optionally, includes design variables and constraints in S1, specifically as follows: The pouring length of each working surface of the three rings of concrete in the bottom plate, web plate and top plate is taken as a design variable. The minimum and maximum allowable values of the pouring length for each working face are used as constraints.
[0009] Optionally, in S2, the above method involves establishing and validating a finite element model of the entire construction process of a concrete arch bridge, specifically as follows: Conduct model tests and use finite element software to establish simulation models corresponding to the model tests; The measured data from the model experiment are compared with the simulation results to verify the finite element model.
[0010] Optionally, in S3, based on the finite element model, the non-dominated sorting genetic algorithm NSGA-II is used to iteratively optimize the design variables and solve for the Pareto front solution set, specifically: Embed the validated finite element model into the optimization process and initialize the NSGA-II algorithm parameters; Using design variables as input, the finite element solver is invoked to calculate the multi-objective function. value; A new generation of population is generated through non-dominated sorting, crowding distance calculation, selection, crossover, and mutation operations; Repeat the iteration until the convergence condition is met, and obtain a set of Pareto front solutions.
[0011] Optionally, in S4 of the above method, an evaluation index is established to screen the Pareto front solution set and determine the optimal control scheme, specifically as follows: Establish the stress distribution non-uniformity coefficient as an evaluation index: Multiple key sections were selected along the longitudinal direction of the main arch ring, and the stress values of the bottom plate, web plate and top plate of each section were extracted to form a stress distribution matrix; Using the stress of the base plate as a benchmark, the stress ratios at each point are calculated to form a ratio matrix; The stress distribution non-uniformity coefficient is the average value of all elements in the ratio matrix; Filtering the Pareto front solution set: Calculate the stress distribution non-uniformity coefficient values for all Pareto solutions, and select the Pareto solution corresponding to the maximum stress distribution non-uniformity coefficient as the optimal control scheme.
[0012] Optionally, in S5, the optimal control scheme is verified through a moving load experiment based on the principle of influence line superposition. Specifically: During the construction stages of the concrete arch bridge closure, bottom slab closure, web closure and top slab concrete closure, moving loading tests were carried out to obtain the stress influence lines and displacement influence lines of each key section of the main arch ring. Based on the principle of influence line superposition, the measured influence lines are combined and spliced to simulate and calculate the arch stress and arch crown deformation under the optimal control scheme. The simulation results are compared with the finite element simulation results. If the relative error between the two is less than the preset threshold, the optimal control scheme is accurate.
[0013] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method for controlling the length of the outer concrete segment during the arching process of a concrete arch bridge in the segmented ring construction stage, which has the following beneficial effects: The present invention abandons the limitation of traditional methods that only control the compressive stress or arch crown deformation at a single location, and takes the maximum compressive stress, maximum tensile stress and vertical deformation of the arch crown of all concrete in the main arch ring after the bridge is completed as optimization objectives, constructs a multi-objective optimization function, and solves the Pareto front solution set through the NSGA-II algorithm, which can achieve optimal balance among multiple conflicting objectives; it proposes the "stress distribution non-uniformity coefficient" as a screening index for the Pareto solution set, which can quantitatively evaluate the stress distribution uniformity of the bottom plate, web plate and top plate concrete at each key section of the main arch ring after the bridge is completed, and overcomes the difficulty of selecting the comprehensive optimal solution from multiple Pareto solutions in the prior art; it conducts moving loading tests during the key construction stage to obtain the true stress influence line and displacement influence line of the main arch ring, and based on the influence lines The superposition principle is used to simulate and calculate the arch stress and deformation under different pouring schemes, which can verify the accuracy of the optimization results without actual construction, solving the problem that finite element simulation results are difficult to verify directly. By adjusting various construction parameters such as the number of working faces, the number of segments in each working face, and the concrete pouring direction, the maximum compressive and tensile stresses of the structure can be effectively reduced, and a set of feasible Pareto optimal solutions can be obtained. It has good adaptability and versatility to different construction techniques and can provide scientific guidance for the construction of the outer concrete of various concrete arch bridges. By optimizing the setting of the working face length, the scientific and precise control of the outer concrete construction is realized. Reasonable segment length can effectively avoid cracks and structural damage caused by stress concentration or excessive deformation, reducing the amount of subsequent repair and reinforcement work. By optimizing the structural stress through multi-objective optimization, the material utilization rate can be improved and the material consumption reduced while ensuring safety. The reduction of construction costs and the shortening of the construction period have good economic benefits. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0015] Figure 1 The flowchart illustrates a method for controlling the length of the outer concrete segments during the arching process of a concrete arch bridge under segmented construction, as provided by this invention. Detailed Implementation
[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0017] Reference Figure 1 As shown, this invention discloses a method for controlling the length of the outer concrete segment during the arching process of a concrete arch bridge under segmented construction, comprising: S1. Construct a multi-objective optimization function with the stress performance and alignment of the main arch ring after bridge completion as the objective, and determine the design variables and constraints. S2. Establish and verify the finite element model of the entire construction process of the concrete arch bridge; S3. Based on the finite element model, the non-dominated sorting genetic algorithm NSGA-II is used to iteratively optimize the design variables and solve the Pareto front solution set. S4. Establish evaluation indicators, screen the Pareto front solution set, and determine the optimal control scheme; S5. The optimal control scheme is verified through a moving load test based on the principle of influence line superposition.
[0018] Furthermore, in S1, the constructed multi-objective optimization function is as follows: ; In the formula, For the first time after the bridge was completed i The first working face j The compressive stress of the concrete segment For the first time after the bridge was completed i The first working face j Tensile stress in the concrete section This represents the maximum compressive stress in all the concrete after the bridge is completed. This indicates the maximum tensile stress in all the concrete after the bridge is completed. This indicates the vertical displacement of the arch crown after the bridge is completed.
[0019] Furthermore, in S1, the design variables and constraints are as follows: The outer concrete is divided into three rings in the transverse direction: bottom slab, web slab and top slab. Each half-span of the main arch ring is longitudinally divided into multiple working faces. The pouring length of each working surface of the three rings of concrete in the bottom plate, web plate and top plate is taken as a design variable. The minimum and maximum allowable values of the pouring length for each working face are used as constraints and determined based on actual construction feasibility.
[0020] Furthermore, in S2, a finite element model of the entire construction process of the concrete arch bridge is established and verified, specifically as follows: Conduct model tests and use finite element software to establish simulation models corresponding to the model tests; The measured data from the model experiment are compared with the simulation results to verify the finite element model.
[0021] Furthermore, in S3, based on the finite element model, the non-dominated sorting genetic algorithm NSGA-II is used to iteratively optimize the design variables and solve for the Pareto front solution set, specifically: Embed the validated finite element model into the optimization process and initialize the NSGA-II algorithm parameters; Using design variables as input, the finite element solver is invoked to calculate the multi-objective function. value; A new generation of population is generated through non-dominated sorting, crowding distance calculation, selection, crossover, and mutation operations; Repeat the iteration until the convergence condition is met, and obtain a set of Pareto front solutions.
[0022] Furthermore, in S4, evaluation indicators are established to screen the Pareto front solution set and determine the optimal control scheme, specifically as follows: Establish the stress distribution non-uniformity coefficient as an evaluation index: Multiple key sections were selected along the longitudinal direction of the main arch ring, and the stress values of the bottom plate, web plate and top plate of each section were extracted to form a stress distribution matrix; Using the stress of the base plate as a benchmark, the stress ratios at each point are calculated to form a ratio matrix; The stress distribution non-uniformity coefficient is the average value of all elements in the ratio matrix; The stress distribution non-uniformity coefficient is used to evaluate the stress distribution uniformity of the bottom plate, web plate and top plate concrete at each key section of the main arch ring after the bridge is completed. The closer its value is to 1, the more uniform the stress distribution and the better the stress performance. Filtering the Pareto front solution set: Calculate the stress distribution non-uniformity coefficient values for all Pareto solutions, and select the Pareto solution corresponding to the maximum stress distribution non-uniformity coefficient as the optimal control scheme.
[0023] Furthermore, in S5, the optimal control scheme is verified through a moving load experiment based on the principle of influence line superposition. Specifically: During the construction stages of the concrete arch bridge closure, bottom slab closure, web closure and top slab concrete closure, moving loading tests were carried out to obtain the stress influence lines and displacement influence lines of each key section of the main arch ring. Based on the principle of influence line superposition, the measured influence lines are combined and spliced to simulate and calculate the arch stress and arch crown deformation under the optimal control scheme. The simulation results are compared with the finite element simulation results. If the relative error between the two is less than the preset threshold (preferably 5%), then the optimal control scheme is accurate.
[0024] In one specific embodiment, a concrete arch bridge model test with a span of L=60m is taken as the object. The outer concrete is divided into three rings in the transverse direction: bottom plate, web plate and top plate. In the longitudinal direction, it is planned to be divided into multiple working surfaces for pouring. The objective function is constructed with the optimization goal of minimizing the maximum compressive stress, maximum tensile stress, and vertical deformation of the arch crown in all concrete of the main arch ring after bridge completion: ; The pouring lengths of the concrete working surfaces of the three rings of the base slab, web slab, and top slab are taken as design variables and denoted as follows: , and Divide the work area into 4 working faces with each half span (L / 2). i Taking the sequence =1, 2, 3, 4 as an example, the length of the fourth working face can be obtained by subtracting the lengths of the first three working faces from half the span (L / 2), so the design variable is... ~ , ~ and ; Based on actual construction feasibility, the constraints on the length of each working face are set as follows: This indicates the minimum allowable length of a single working face in actual engineering. m This indicates the total number of working surfaces into which the same ring of concrete is divided.
[0025] Model tests were conducted, and a finite element simulation model corresponding to the model tests was established using ANSYS APDL to simulate the entire process from the installation of the concrete arch bridge to the completion of the segmented pouring of the outer concrete. The measured stress and deformation data in the model tests were compared with the simulation calculation results to verify the accuracy of the finite element model.
[0026] The validated ANSYS APDL finite element model was integrated into the DirectOptimization module of ANSYS Workbench, and the NSGA-II algorithm was selected: Algorithm parameters: initial sample size 78, maximum allowed Pareto percentage 70%, mutation probability 0.01, crossover probability 0.98; The optimization program automatically calls the APDL model to design variables. ~ , ~ and As input, the corresponding objective function value is calculated; through non-dominated sorting and crowding distance comparison, tournament selection, simulated binary crossover and polynomial mutation are performed to generate offspring population. After multiple generations of iteration, a Pareto front solution set consisting of 602 design points is finally obtained, with a Pareto percentage of 1.28%.
[0027] To select the optimal solution from the Pareto solution set, this embodiment proposes and calculates a "stress distribution non-uniformity coefficient." This coefficient is used to evaluate the stress distribution uniformity of the concrete in the bottom slab, web, and top slab after the bridge is completed. During the construction stage after the arch columns and main beam counterweights are completed, five key sections are selected along the longitudinal direction of the arch ring: arch foot, 1 / 8 span, 2 / 8 span, 3 / 8 span, and mid-span. The stress values of the bottom slab, web, and top slab at each section are extracted to form a stress distribution matrix. Using the stress in the bottom slab as a benchmark, the stress ratio at each point is calculated to form a ratio matrix. The stress distribution non-uniformity coefficient is the average value of all 15 elements in the ratio matrix. The closer the value is to 1, the more uniform the stress distribution. The stress distribution non-uniformity coefficient values of all 602 Pareto solutions are calculated, and the solution corresponding to the maximum stress distribution non-uniformity coefficient is selected as the optimal control scheme under this casting scheme.
[0028] To verify the correctness of the optimization results, a moving loading test was conducted during the key construction stages of the model arch (concrete arch bridge closure, bottom slab closure, web closure, and top slab closure). A 500kg counterweight was suspended using a moving gantry, and the load was applied point by point from one arch foot to the other. The stress influence lines and displacement influence lines of each key section were obtained through actual measurement using sensors. Based on the principle of influence line superposition, the wet weight of concrete on each working surface in the optimal control scheme was treated as a distributed load. By integrating and superimposing the measured influence lines of the corresponding stages, the vertical deformation of the arch crown and the stress of the key sections after the bridge was completed were simulated and calculated. The calculation results of the influence line superposition method were compared with the simulation results of ANSYS APDL. The results showed that the relative error of the maximum compressive stress was less than 2.63%, and the relative error of the arch crown displacement was less than 3.47%, both within 5%, thus verifying the accuracy and reliability of the optimization scheme obtained by the control method of this invention.
[0029] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.
[0030] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for controlling the length of the outer concrete segment during the arch-forming process of a concrete arch bridge under segmented construction, characterized in that, include: S1. Construct a multi-objective optimization function with the stress performance and alignment of the main arch ring after bridge completion as the objective, and determine the design variables and constraints. S2. Establish and verify the finite element model of the entire construction process of the concrete arch bridge; S3. Based on the finite element model, the non-dominated sorting genetic algorithm NSGA-II is used to iteratively optimize the design variables and solve the Pareto front solution set. S4. Establish evaluation indicators, screen the Pareto front solution set, and determine the optimal control scheme; S5. The optimal control scheme is verified through a moving load test based on the principle of influence line superposition.
2. The method for controlling the length of the outer concrete segment during the arch formation process of a concrete arch bridge under segmented construction, as described in claim 1, is characterized in that... In S1, the constructed multi-objective optimization function is as follows: ； In the formula, For the first time after the bridge was completed i The first working face j The compressive stress of the concrete segment For the first time after the bridge was completed i The first working face j Tensile stress in the concrete section This represents the maximum compressive stress in all the concrete after the bridge is completed. This indicates the maximum tensile stress in all the concrete after the bridge is completed. This indicates the vertical displacement of the arch crown after the bridge is completed.
3. The method for controlling the length of the outer concrete segment during the arch formation process of a concrete arch bridge under segmented construction, as described in claim 2, is characterized in that... In S1, the design variables and constraints are as follows: The pouring length of each working surface of the three rings of concrete in the bottom plate, web plate and top plate is taken as a design variable. The minimum and maximum allowable values of the pouring length for each working face are used as constraints.
4. The method for controlling the length of the outer concrete segment during the arch formation process of a concrete arch bridge under segmented construction, as described in claim 3, is characterized in that... In S2, a finite element model of the entire construction process of a concrete arch bridge is established and verified, specifically as follows: Conduct model tests and use finite element software to establish simulation models corresponding to the model tests; The measured data from the model experiment are compared with the simulation results to verify the finite element model.
5. The method for controlling the length of the outer concrete segment during the arch formation process of a concrete arch bridge under segmented construction, as described in claim 4, is characterized in that... In S3, based on the finite element model, the non-dominated sorting genetic algorithm NSGA-II is used to iteratively optimize the design variables and solve for the Pareto front solution set, specifically: Embed the validated finite element model into the optimization process and initialize the NSGA-II algorithm parameters; Using design variables as input, the finite element solver is invoked to calculate the multi-objective function. value; A new generation of population is generated through non-dominated sorting, crowding distance calculation, selection, crossover, and mutation operations; Repeat the iteration until the convergence condition is met, and obtain a set of Pareto front solutions.
6. The method for controlling the length of the outer concrete segment during the arch formation process of a concrete arch bridge under segmented construction, as described in claim 5, is characterized in that... In S4, evaluation indicators are established to screen the Pareto front solution set and determine the optimal control scheme, specifically as follows: Establish the stress distribution non-uniformity coefficient as an evaluation index: Multiple key sections were selected along the longitudinal direction of the main arch ring, and the stress values of the bottom plate, web plate and top plate of each section were extracted to form a stress distribution matrix; Using the stress of the base plate as a benchmark, the stress ratios at each point are calculated to form a ratio matrix; The stress distribution non-uniformity coefficient is the average value of all elements in the ratio matrix; Filtering the Pareto front solution set: Calculate the stress distribution non-uniformity coefficient values for all Pareto solutions, and select the Pareto solution corresponding to the maximum stress distribution non-uniformity coefficient as the optimal control scheme.
7. The method for controlling the length of the outer concrete segment during the arch formation process of a concrete arch bridge under segmented construction, as described in claim 6, is characterized in that... In S5, the optimal control scheme was verified through a moving loading experiment based on the principle of influence line superposition. Specifically: During the construction stages of the concrete arch bridge closure, bottom slab closure, web closure and top slab concrete closure, moving loading tests were carried out to obtain the stress influence lines and displacement influence lines of each key section of the main arch ring. Based on the principle of influence line superposition, the measured influence lines are combined and spliced to simulate and calculate the arch stress and arch crown deformation under the optimal control scheme. The simulation results are compared with the finite element simulation results. If the relative error between the two is less than the preset threshold, the optimal control scheme is accurate.