Stiff skeleton arch bridge encased concrete segmentation calculation method based on influence line superposition method

Abstract

The invention discloses a stiff skeleton arch bridge encased concrete segmentation calculation method based on an influence line superposition method, and relates to the field of arch bridge construction.The stiff skeleton arch bridge encased concrete segmentation calculation method comprises the steps that a deflection influence line of each node of an arch bridge is determined through finite element simulation analysis; according to an influence line superposition formula, the deflection change value of each node before and after construction is calculated; the number of arch bridge construction faces is set, and the initial position of each construction face is determined according to deflection change values before and after construction; and setting constraint conditions of pouring segments of each construction surface, and determining the optimal length of each working surface segment by using an improved genetic optimization algorithm. According to the method, based on the influence line superposition and optimization algorithm, the more reasonable length of each pouring segment can be determined, and the pouring construction quality of the stiff skeleton arch bridge is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of arch bridge construction, and more particularly to a segmented calculation method for outer concrete based on an influence line superposition method. Background Art

[0002] With the rapid development of infrastructure construction, rigid skeleton concrete arch bridges have been widely used due to their excellent mechanical properties and low construction and maintenance costs.

[0003] However, during the construction of rigid-frame concrete arch bridges, especially during the pouring of the outer concrete envelope of the main arch ring, segmented pouring places the main arch ring structure under a constantly changing stress state. Each pouring of a new concrete segment changes the weight distribution of the structure, leading to constant changes in the stress conditions of the rigid frame and the poured concrete. If the length of the pouring segments is unreasonable, the structure may experience excessive stress and deformation during construction, even exceeding the design allowable values and compromising its safety.

[0004] Therefore, how to determine a more reasonable length of each casting segment to improve the casting construction quality of the rigid skeleton arch bridge is a problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a segmented calculation method for the exterior concrete of a rigid skeleton arch bridge based on the influence line superposition method. By finite element simulation and determining the influence lines of each point in the arch ring, the adaptability of the optimization process is determined, and then the length of each segment is optimized, thereby improving the quality of pouring construction.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention discloses a segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method, and the specific steps are as follows:

[0008] Use finite element simulation analysis to determine the deflection influence line of each node of the arch bridge;

[0009] Calculate the deflection change value of each node before and after construction according to the influence line superposition formula;

[0010] Setting the number of construction surfaces of the arch bridge and determining the starting position of each construction surface according to the deflection change value before and after the construction;

[0011] The constraints of the casting segments of each construction surface are set, and the improved genetic optimization algorithm is used to determine the optimal length of each working surface segment.

[0012] Furthermore, determining the deflection influence line of each node of the arch bridge specifically includes:

[0013] Establish a three-dimensional model based on the actual geometric size and shape of the rigid skeleton;

[0014] Discretizing the three-dimensional model into a plurality of units, and setting material properties and boundary conditions for each unit;

[0015] According to the set step size, the unit simulated load is applied from one arch foot to the other arch foot of the rigid skeleton, and the deflection response of each node under the simulated load at different positions is solved and recorded;

[0016] According to the response results, the deflection influence line of each node is determined respectively, which can be expressed as:

[0017] {δ i}=[δ i1 ,δ i2 ,…δ ij …,δ iN ];

[0018] Among them, {δ i} represents the deflection influence line vector of node i; δ ij It indicates the vertical coordinate of the deflection influence line generated when the unit load acts on the node j, and N represents the total number of nodes.

[0019] Furthermore, the influence line superposition formula is expressed as:

[0020]

[0021] Among them, f i It represents the total deflection value of node i under the combined load of n nodes, P j represents the ratio of the actual load acting on node i to the unit load. According to the arch ring structure design of the rigid skeleton arch bridge, the actual concrete pouring weight corresponding to each node is determined, and the pouring weight is used as the actual load to determine P j The deflection change value of each node before and after construction is calculated.

[0022] Furthermore, the determining of the starting position of each construction surface specifically includes:

[0023] Determine the positions of the equally divided points of the rigid skeleton arch ring according to the number of the working surfaces;

[0024] Set the sliding window length and use the sliding window to slide from one arch foot of the rigid frame to the other arch foot. During the sliding process, calculate the initial adaptation value of the working surface of the sliding window at each position. The formula is:

[0025]

[0026] Among them, S i represents the initial adaptation value of the working surface when the sliding window is at node i; Fm represents the deflection change value of the mth node in the sliding window before and after construction, M is the total number of nodes in the sliding window; d m Represents the distance between the mth node in the sliding window and the closest bisection point of the rigid skeleton arch ring; w1 and w2 are the sum coefficient and fluctuation coefficient of the sliding window deflection change value respectively, and 0≤a≤1 is the balance coefficient, which is used to balance the influence of deflection change and bisection point distance;

[0027] Sort all the starting fitness values, and select the sliding window position corresponding to the number of working surfaces from small to large as the starting position of each working surface.

[0028] Furthermore, the improved genetic optimization algorithm is used to determine the optimal length of each working surface segment, specifically including:

[0029] Step 1: generating a number of casting segment length sequences according to the constraint conditions and encoding them into gene sequence individuals to obtain a population consisting of multiple individuals;

[0030] Step 2: Use the fitness function to calculate the fitness value of each individual in the population and sort them;

[0031] Step 3: According to the set screening ratio, individuals with high fitness are removed from the population, and several individuals with low fitness values are selected from the remaining population, and genetic crossover is performed to generate new individuals and add them to the population;

[0032] Step 4: Randomly select several individuals from the population, perform mutation operations, generate mutant individuals and add them to the population;

[0033] Step 5: Determine whether the maximum number of iterations is met. If not, return to step 2. If so, calculate the fitness value of each individual in the population and select the optimal individual. Determine the optimal length of each working surface segment based on the segment lengths corresponding to the optimal individual.

[0034] Furthermore, the constraint conditions include: a total length constraint of each working surface, and a maximum length and a minimum length constraint of a single segment length.

[0035] Furthermore, the generation of several casting segment length sequences is specifically as follows:

[0036] Let L max 、L min Respectively represent the maximum and minimum length of a single segment; L p,q L represents the length of the qth segment of the pth working surface, p = 1, 2, 3...P, q = 1, 2, 3...Q, P is the total number of working surfaces, Q is the total number of segments of each working surface; up 、L dowm Respectively represent the upper and lower limits of the random interval; L pIndicates the remaining length of the pth working surface; the initial value of p is 0;

[0037] Step 1.1: Let p = p + 1, q = 0;

[0038] Step 1.2: q = q + 1, determine L min Is it less than L p -(Qq)×L max If so, let L dowm =L p -(Qq)×L max Otherwise L dowm =L min ;

[0039] Step 1.3: Determine L max Is it greater than L p -(Qq)×L min If so, let L up =L p -(Qq)×L min Otherwise L up =L max ;

[0040] Step 1.4: In the interval [L dowm ,L up ] Randomly generate L p,q value and output;

[0041] Step 1.5: Determine whether q is equal to Q-1. If so, then L p,q+1＝ L p , and output, go to step 1.6; otherwise return to step 1.2;

[0042] Step 1.6, determine whether it is the last working surface, if not, return to step 1.1, if so, the length sequence generation is completed.

[0043] Furthermore, the fitness function is specifically:

[0044]

[0045] Among them, Fitness represents the fitness value; f i t represents the deflection value of the i-th node of the arch bridge after the t-th pouring construction, N represents the total number of nodes, and T is the total number of pouring times; f i max 、f i min They respectively represent the maximum and minimum deflections of the i-th node during the entire pouring process; ω1 and ω2 are coefficients.

[0046] Furthermore, the gene crossover is specifically performed as follows:

[0047] Step 3.1: Randomly select two individuals from the selected individuals to be subjected to gene crossover operation to generate an intermediate gene sequence;

[0048] Step 3.2: Map the corresponding pouring segment length sequence according to the intermediate gene sequence, and calculate the total length of each corresponding working surface;

[0049] Step 3.3: Determine the ratio of the calculated total length of each working surface to the total length constraint of each working surface, and scale the mapped lengths of each pouring segment so that the total length of each working surface after scaling is equal to the total length constraint of each working surface;

[0050] Step 3.4: Determine whether the scaled lengths of each casting segment meet the maximum and minimum length constraints of a single segment. If so, re-encode the crossover gene sequence based on the scaled lengths of each casting segment and output the crossover individual. Otherwise, proceed directly to step 3.5.

[0051] Step 3.5: Determine whether the number of new individuals generated is equal to the number of individuals eliminated. If so, end; otherwise, return to step 3.1.

[0052] Furthermore, the mutation operation is specifically as follows:

[0053] Step 4.1: Randomly select one or more mutation points from the selected individuals to be mutated and perform random mutation to generate intermediate mutant gene sequences;

[0054] Step 4.2: Map the corresponding pouring segment length sequence according to the intermediate variant gene sequence, and calculate the total length of each corresponding working surface;

[0055] Step 4.3: Determine the ratio of the calculated total length of each working surface to the total length constraint of each working surface, and scale the mapped lengths of each pouring segment so that the total length of each working surface after scaling is equal to the total length constraint of each working surface;

[0056] Step 4.4: Determine whether the scaled lengths of each casting segment meet the maximum and minimum length constraints of a single segment. If so, re-encode the variant gene sequence based on the scaled lengths of each casting segment and output the variant individual. Otherwise, return to step 4.1.

[0057] Step 4.5: Determine whether there are any individuals left to be mutated. If so, randomly select an individual to be mutated and return to step 4.1; otherwise, end.

[0058] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention discloses a method for calculating the segments of the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method. The deflection influence line of each node of the arch bridge is determined by finite element simulation analysis, and the influence line superposition formula is used to accurately calculate the deflection change value of each node before and after construction; on this basis, the starting position of the construction surface and the optimal length of each working surface segment are determined. The reasonable planning of the length of each segment makes the weight distribution of the structure more uniform after the concrete is poured, thereby improving the casting construction quality of the rigid skeleton arch bridge, avoiding excessive deformation of the rigid skeleton and the poured concrete during the construction process, and improving the safety of the structure during the construction process. Based on the influence line superposition and optimization algorithm, the present invention can determine more reasonable lengths of each casting segment, thereby improving the casting construction quality of the rigid skeleton arch bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0060] Figure 1 Schematic diagram of the overall process of an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] The embodiment of the present invention discloses a segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method, such as Figure 1 The specific steps are as follows:

[0063] Use finite element simulation analysis to determine the deflection influence line of each node of the arch bridge;

[0064] According to the influence line superposition formula, calculate the deflection change value of each node before and after construction;

[0065] Set the number of construction surfaces for the arch bridge and determine the starting position of each construction surface based on the deflection change before and after construction;

[0066] The constraints of the casting segments of each construction surface are set, and the improved genetic optimization algorithm is used to determine the optimal length of each working surface segment.

[0067] In a specific embodiment, determining the deflection influence line of each node of the arch bridge specifically includes:

[0068] Establish a three-dimensional model based on the actual geometric size and shape of the rigid skeleton;

[0069] Discretize the three-dimensional model into several units and set the material properties and boundary conditions of each unit;

[0070] According to the set step size, the unit simulated load is applied from one arch foot to the other arch foot of the rigid skeleton, and the deflection response of each node under the simulated load at different positions is solved and recorded;

[0071] According to the response results, the deflection influence line of each node is determined respectively, which can be expressed as:

[0072] {δ i}=[δ i1 ,δ i2 ,…δ ij …,δ iN ];

[0073] Among them, {δ i} represents the deflection influence line vector of node i; δ ij It indicates the vertical coordinate of the deflection influence line generated when the unit load acts on the node j, and N represents the total number of nodes.

[0074] In a specific embodiment, the influence line superposition formula is expressed as:

[0075]

[0076] Among them, f i It represents the total deflection value of node i under the combined load of n nodes, P j It represents the ratio of the actual load acting on node i to the unit load. According to the arch ring structure design of the rigid skeleton arch bridge, the actual concrete pouring weight corresponding to each node is determined, and the pouring weight is used as the actual load to determine P j The deflection change value before and after construction of each node is calculated.

[0077] In a specific embodiment, determining the starting position of each construction surface specifically includes:

[0078] Determine the locations of the equally divided points of the rigid skeleton arch ring according to the number of working surfaces;

[0079] Set the sliding window length and use the sliding window to slide from one arch foot of the rigid frame to the other arch foot. During the sliding process, calculate the initial adaptation value of the working surface of the sliding window at each position. The formula is:

[0080]

[0081] Among them, S i represents the initial adaptation value of the working surface when the sliding window is at node i; F m represents the deflection change value of the mth node in the sliding window before and after construction, M is the total number of nodes in the sliding window; d m It represents the distance between the mth node in the sliding window and the nearest bisection point of the rigid skeleton arch ring; w1 and w2 are the sum coefficient and fluctuation coefficient of the sliding window deflection change value respectively; 0≤a≤1 is the balance coefficient, which is used to balance the influence of deflection change and bisection point distance;

[0082] Sort all the starting fitness values, and select the sliding window position corresponding to the number of working surfaces from small to large as the starting position of each working surface.

[0083] Specifically, during the segmented pouring of concrete for rigid skeleton arch bridges, simultaneous pouring from multiple working surfaces can effectively reduce the excessive stress and deformation of the arch crown caused by pouring only from the two working surfaces at the arch foot toward the arch crown. To facilitate deformation control during the pouring process, the deflection changes caused by stress and deformation at adjacent nodes at the starting position of each working surface should be as consistent as possible. At the same time, to facilitate synchronous construction, the total pouring length of each working surface during the upward pouring process should be kept consistent as much as possible. Therefore, the influence of the distance between the starting position and the equal division point of the arch ring is taken into account during the determination of the starting position. According to actual construction needs, the balance coefficient is adjusted and finally calculated, and the starting position of each working surface is determined in order.

[0084] In a specific embodiment, the optimal length of each working surface segment is determined using an improved genetic optimization algorithm, which specifically includes:

[0085] Step 1: According to the constraints, generate several casting segment length sequences and encode them into gene sequence individuals to obtain a population consisting of multiple individuals;

[0086] Step 2: Use the fitness function to calculate the fitness value of each individual in the population and sort them;

[0087] Step 3: According to the set screening ratio, individuals with high fitness are removed from the population, and several individuals with low fitness values are selected from the remaining population, and genetic crossover is performed to generate new individuals and add them to the population;

[0088] Step 4: Randomly select several individuals from the population, perform mutation operations, generate mutant individuals and add them to the population;

[0089] Step 5: Determine whether the maximum number of iterations is met. If not, return to step 2. If so, calculate the fitness value of each individual in the population and select the optimal individual. Determine the optimal length of each working surface segment based on the segment lengths corresponding to the optimal individual.

[0090] In a specific embodiment, the constraint conditions include: a total length constraint of each working surface, and a maximum length constraint and a minimum length constraint of a single segment length.

[0091] In a specific embodiment, several casting segment length sequences are generated, specifically:

[0092] Let L max 、L min Respectively represent the maximum and minimum length of a single segment; L p,q L represents the length of the qth segment of the pth working surface, p = 1, 2, 3...P, q = 1, 2, 3...Q, P is the total number of working surfaces, Q is the total number of segments of each working surface; up 、L dowm Respectively represent the upper and lower limits of the random interval; L p Indicates the remaining length of the pth working surface; the initial value of p is 0;

[0093] Step 1.1: Let p = p + 1, q = 0;

[0094] Step 1.2: q = q + 1, determine L min Is it less than L p -(Qq)×L max If so, let L dowm =L p -(Qq)×L max Otherwise L dowm =L min ;

[0095] Step 1.3: Determine L max Is it greater than L p -(Qq)×L min If so, let L up =L p -(Qq)×L min Otherwise L up =L max ;

[0096] Step 1.4: In the interval [L dowm ,L up ] Randomly generate L p,q value and output;

[0097] Step 1.5: Determine whether q is equal to Q-1. If so, then L p,q+1 =L p , and output, go to step 1.6; otherwise return to step 1.2;

[0098] Step 1.6, determine whether it is the last working surface, if not, return to step 1.1, if so, the length sequence generation is completed.

[0099] Specifically, during the synchronous pouring process of each working surface, in order to meet the construction efficiency requirements, a lower limit of the length of a single pouring is set. At the same time, in order to meet the limitations of actual construction conditions (for example, the size of working surface auxiliary facilities, the size of pouring support templates, support condition limitations, etc.), an upper limit of the length of a single pouring is set. By dynamically adjusting the upper and lower limits of the length of each randomly generated segment of each working surface, it can be ensured that the generated segments meet the actual construction needs.

[0100] In a specific embodiment, the fitness function is specifically:

[0101]

[0102] Among them, Fitness represents the fitness value; f i t represents the deflection value of the i-th node of the arch bridge after the t-th pouring construction, N represents the total number of nodes, and T is the total number of pouring times; f i max 、f i min where ω1 and ω2 represent the maximum and minimum deflections of the i-th node during the entire pouring process, respectively; ω1 and ω2 are coefficients. The fitness function is determined based on the overall changes in each node during construction, as well as the weighted maximum and minimum changes. A smaller fitness value indicates smaller deformation of the arch ring during the overall pouring process, and smaller changes in the internal stress between the arch ring's rigid skeleton and the concrete after pouring. Casting the arch ring according to the length of each pouring segment corresponding to the individual with the smallest fitness value can reduce internal stress changes during construction and improve the pouring quality of the arch ring.

[0103] In a specific embodiment, gene crossover is performed, specifically:

[0104] Step 3.1: Randomly select two individuals from the selected individuals to be subjected to gene crossover operation to generate an intermediate gene sequence;

[0105] Step 3.2: Map the corresponding pouring segment length sequence according to the intermediate gene sequence and calculate the total length of each corresponding working surface;

[0106] Step 3.3: Determine the ratio of the calculated total length of each working surface to the total length constraint of each working surface, and scale the mapped lengths of each pouring segment so that the total length of each working surface after scaling is equal to the total length constraint of each working surface;

[0107] Step 3.4: Determine whether the scaled lengths of each casting segment meet the maximum and minimum length constraints of a single segment. If so, re-encode the crossover gene sequence based on the scaled lengths of each casting segment and output the crossover individual. Otherwise, proceed directly to step 3.5.

[0108] Step 3.5: Determine whether the number of new individuals generated is equal to the number of individuals eliminated. If so, end; otherwise, return to step 3.1.

[0109] Perform mutation operations, specifically:

[0110] Step 4.1: Randomly select one or more mutation points from the selected individuals to be mutated and perform random mutation to generate intermediate mutant gene sequences;

[0111] Step 4.2: Map the corresponding pouring segment length sequence according to the intermediate variant gene sequence, and calculate the total length of each corresponding working surface;

[0112] Step 4.3: Determine the ratio of the calculated total length of each working surface to the total length constraint of each working surface, and scale the mapped lengths of each pouring segment so that the total length of each working surface after scaling is equal to the total length constraint of each working surface;

[0113] Step 4.4: Determine whether the scaled lengths of each casting segment meet the maximum and minimum length constraints of a single segment. If so, re-encode the variant gene sequence based on the scaled lengths of each casting segment and output the variant individual. Otherwise, return to step 4.1.

[0114] Step 4.5: Determine whether there are any individuals left to be mutated. If so, randomly select an individual to be mutated and return to step 4.1; otherwise, end.

[0115] Specifically, during the process of individual gene crossover and mutation, changes in the gene sequence can cause the total length of each corresponding working surface to be disproportionate to the actual total length. Therefore, after crossover and mutation, the length of each segment is adjusted based on the ratio of the two, and the constraint conditions are determined to ensure that the individuals after gene crossover and mutation meet the requirements of the actual pouring construction process.

[0116] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0117] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A segmented calculation method for the concrete wrapping of a rigid skeleton arch bridge based on the influence line superposition method, characterized in that: The specific steps are as follows: Use finite element simulation analysis to determine the deflection influence line of each node of the arch bridge; Calculate the deflection change value of each node before and after construction according to the influence line superposition formula; Setting the number of construction surfaces of the arch bridge and determining the starting position of each construction surface according to the deflection change value before and after the construction; The constraints of the casting segments of each construction surface are set, and the improved genetic optimization algorithm is used to determine the optimal length of each working surface segment.

2. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 1 is characterized in that: Determining the deflection influence line of each node of the arch bridge specifically includes: Establish a three-dimensional model based on the actual geometric size and shape of the rigid skeleton; Discretizing the three-dimensional model into a plurality of units, and setting material properties and boundary conditions for each unit; According to the set step size, the unit simulated load is applied from one arch foot to the other arch foot of the rigid skeleton, and the deflection response of each node under the simulated load at different positions is solved and recorded; According to the response results, the deflection influence line of each node is determined respectively, which can be expressed as: {d} i }=[δ i1 ,d i2 ,…d ij …,d iN ]; Among them, {δ i } represents the deflection influence line vector of node i; δ ij It indicates the vertical coordinate of the deflection influence line generated when the unit load acts on the node j, and N represents the total number of nodes.

3. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 2 is characterized in that: The influence line superposition formula is expressed as: Among them, f i It represents the total deflection value of node i under the combined load of n nodes, P j represents the ratio of the actual load acting on node i to the unit load. According to the arch ring structure design of the rigid skeleton arch bridge, the actual concrete pouring weight corresponding to each node is determined, and the pouring weight is used as the actual load to determine P j The deflection change value of each node before and after construction is calculated.

4. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 1 is characterized in that: Determining the starting position of each construction surface specifically includes: Determine the positions of the equally divided points of the rigid skeleton arch ring according to the number of the working surfaces; Set the sliding window length and use the sliding window to slide from one arch foot of the rigid frame to the other arch foot. During the sliding process, calculate the initial adaptation value of the working surface of the sliding window at each position. The formula is: Among them, S i represents the initial adaptation value of the working surface when the sliding window is at node i; F m represents the deflection change value of the mth node in the sliding window before and after construction, M is the total number of nodes in the sliding window; d m Represents the distance between the mth node in the sliding window and the closest bisection point of the rigid skeleton arch ring; w1 and w2 are the sum coefficient and fluctuation coefficient of the sliding window deflection change value respectively, and 0≤a≤1 is the balance coefficient, which is used to balance the influence of deflection change and bisection point distance; Sort all the starting fitness values, and select the sliding window position corresponding to the number of working surfaces from small to large as the starting position of each working surface.

5. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 1 is characterized in that: The method of using the improved genetic optimization algorithm to determine the optimal length of each working surface segment specifically includes: Step 1: generating a number of casting segment length sequences according to the constraint conditions and encoding them into gene sequence individuals to obtain a population consisting of multiple individuals; Step 2: Use the fitness function to calculate the fitness value of each individual in the population and sort them; Step 3: According to the set screening ratio, individuals with high fitness are removed from the population, and several individuals with low fitness values are selected from the remaining population, and genetic crossover is performed to generate new individuals and add them to the population; Step 4: Randomly select several individuals from the population, perform mutation operations, generate mutant individuals and add them to the population; Step 5: Determine whether the maximum number of iterations is met. If not, return to step 2. If so, calculate the fitness value of each individual in the population and select the optimal individual. Determine the optimal length of each working surface segment based on the segment lengths corresponding to the optimal individual.

6. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 5 is characterized in that: The constraints include: a total length constraint of each working surface, and a maximum length constraint and a minimum length constraint of a single segment length.

7. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 5 is characterized in that: The generation of several pouring segment length sequences is specifically as follows: Let L max 、L min Respectively represent the maximum and minimum length of a single segment; L p,q L represents the length of the qth segment of the pth working surface, p = 1, 2, 3...P, q = 1, 2, 3...Q, P is the total number of working surfaces, Q is the total number of segments of each working surface; up 、L dowm Respectively represent the upper and lower limits of the random interval; L p Indicates the remaining length of the pth working surface; the initial value of p is 0; Step 1.1: Let p = p + 1, q = 0; Step 1.2: q = q + 1, determine L min Is it less than L p-(Q-q) ×L max If so, let L dowm =L p-(Q-q) ×L max Otherwise L dowm =L min ; Step 1.3: Determine L max Is it greater than L p-(Q-q) ×L min If so, let L up =L p-(Q-q) ×L min Otherwise L up =L max ; Step 1.4: In the interval [L dowm ,L up ] Randomly generate L p,q value and output; Step 1.5: Determine whether q is equal to Q-1. If so, then L p,q+1 =L p , and output, go to step 1.6; otherwise return to step 1.2; Step 1.6, determine whether it is the last working surface, if not, return to step 1.1, if so, the length sequence generation is completed.

8. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 5 is characterized in that: The fitness function is specifically: Among them, Fitness represents the fitness value; f i t represents the deflection value of the i-th node of the arch bridge after the t-th pouring construction, N represents the total number of nodes, and T is the total number of pouring times; f i max 、f i min They respectively represent the maximum and minimum deflections of the i-th node during the entire pouring process; ω1 and ω2 are coefficients.

9. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 5 is characterized in that: The gene crossover is specifically performed as follows: Step 3.1: Randomly select two individuals from the selected individuals to be subjected to gene crossover operation to generate an intermediate gene sequence; Step 3.2: Map the corresponding pouring segment length sequence according to the intermediate gene sequence, and calculate the total length of each corresponding working surface; Step 3.3: Determine the ratio of the calculated total length of each working surface to the total length constraint of each working surface, and scale the mapped lengths of each pouring segment so that the total length of each working surface after scaling is equal to the total length constraint of each working surface; Step 3.4: Determine whether the scaled lengths of each casting segment meet the maximum and minimum length constraints of a single segment. If so, re-encode the crossover gene sequence based on the scaled lengths of each casting segment and output the crossover individual. Otherwise, go directly to step 3.5; Step 3.5: Determine whether the number of new individuals generated is equal to the number of individuals eliminated. If so, end; otherwise, return to step 3.

1.

10. The segmented calculation method for the outer concrete of a rigid skeleton arch bridge based on the influence line superposition method according to claim 5 is characterized in that: The mutation operation is specifically as follows: Step 4.1: Randomly select one or more mutation points from the selected individuals to be mutated and perform random mutation to generate intermediate mutant gene sequences; Step 4.2: Map the corresponding pouring segment length sequence according to the intermediate variant gene sequence, and calculate the total length of each corresponding working surface; Step 4.3: Determine the ratio of the calculated total length of each working surface to the total length constraint of each working surface, and scale the mapped lengths of each pouring segment so that the total length of each working surface after scaling is equal to the total length constraint of each working surface; Step 4.4: Determine whether the scaled lengths of each casting segment meet the maximum and minimum length constraints of a single segment. If so, re-encode the variant gene sequence based on the scaled lengths of each casting segment and output the variant individual. Otherwise, return to step 4.1; Step 4.5: Determine whether there are any individuals left to be mutated. If so, randomly select an individual to be mutated and return to step 4.1; otherwise, end.

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