Optimization method of foundation pit trestle layout based on microhabitat genetic algorithm

Abstract

The present invention discloses an optimization method for the arrangement of foundation pit trestles based on the niche genetic algorithm, which includes the steps of: establishing an adjacent unit database, a unit size database, a unit database at the entrance and exit, a necessary unit database, and a must-not unit database; randomly generating an initial parental population P(t), defining an objective function f and a penalty function g, and establishing a fitness function F for the multi-objective optimization of the trestle; generating a combination N(t) according to the fitness function values F of each arrangement combination individual; using crossover, mutation operations and niche technology to generate a new population P N (t), and forming a new parental population P New (t) enters the next iterative calculation until a preset termination condition is reached, and an optimized trestle plan is obtained. By setting up an objective function and a penalty function, establishing a unified fitness function, using crossover and mutation operations in the genetic algorithm, and obtaining the optimal layout plan through iteration, the present application provides a scientific and reasonable optimization plan for the layout of foundation pit trestles.

Description

Technical Field

[0001] The invention relates to a foundation pit trestle arrangement optimization method based on a microhabitat genetic algorithm, and belongs to the technical field of foundation pit trestle design. Background Art

[0002] Foundation pit engineering is an important branch of engineering construction. In recent years, with the increase in the scale of urban underground space development, foundation pit engineering has tended to develop in the direction of large area and deep depth, and the proportion of foundation pit engineering in the construction cost and construction period has also increased. The construction progress of foundation pit engineering will directly affect the construction period and investment of construction projects to a greater extent. At present, the mainstream construction method of foundation pit engineering is still open excavation. The open excavation foundation pit engineering mainly consists of three parts: earthwork engineering, support construction and demolition engineering, and underground structure engineering. These three parts of the project all need to use the foundation pit trestle as an operating platform for construction vehicles to pass, soil excavation, hoisting, material stacking and processing, concrete pouring and other construction contents. Therefore, the layout of the foundation pit trestle will directly affect whether the foundation pit engineering can be smoothly organized and constructed, and then affect the construction period and cost of the foundation pit engineering, and further affect the implementation of the entire construction project.

[0003] At present, the arrangement of foundation pit trestles is mainly based on the overall technical route planning technology of the project and the understanding and engineering experience of the foundation pit design and construction technicians. Due to the limitations of the experience of the technicians, the arrangement of foundation pit trestles often leads to two results: the trestles are too large, resulting in unnecessary engineering waste, which does not meet the current development requirements of carbon neutrality in buildings; or the trestles are not in line with the construction requirements, resulting in construction traffic congestion, reduced excavation and material lifting efficiency, increased construction period of the foundation pit project and long exposure time, which is not conducive to the safety control of the foundation pit.

[0004] How to adopt a scientific and reasonable solution to optimize the design of the pier is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] In order to solve the problem in the prior art that the arrangement of foundation pit trestles is restricted by the experience of technicians, the present application provides a method for optimizing the arrangement of foundation pit trestles based on a microhabitat genetic algorithm.

[0006] In order to solve the above technical problems, the present invention includes the following technical solutions:

[0007] A method for optimizing the layout of foundation pit trestle bridges based on a microhabitat genetic algorithm comprises the following steps:

[0008] S1. Number the space units enclosed by the foundation pit support beams, and establish an adjacent unit database and a unit size database for each space unit; determine the entrance and exit unit database, the required unit database, and the non-required unit database according to the project site conditions;

[0009] S2. Determine the calculation population size M, randomly generate M kinds of trestle unit arrangement combinations according to each database in step S1, and constitute the initial parent population P(t); define the objective function f corresponding to each objective of the trestle arrangement optimization and the penalty function g of the trestle arrangement optimization constraint condition, and establish the fitness function F of the trestle multi-objective optimization;

[0010] S3. Calculate the fitness function value F of each arrangement combination individual in the initial parent population P(t), and arrange them in descending order; select the first N arrangement combination individuals to form a combination N(t), where N <M;

[0011] S4. Calculate the crossover probability P according to the fitness of each arrangement combination individual c , crossover operation generates P'(t); or \ and, calculate the mutation probability P m , mutation operation generates P”(t); P'(t) or \ and P”(t) and N(t) are combined to form a new population P N (t), calculate the fitness function value F and sort it in descending order, select the first M arrangement combination individuals to form a new parent population P New (t);

[0012] S5. Determine whether the preset termination condition is reached;

[0013] If yes, stop the operation and get the trestle optimization plan;

[0014] If not, return to step S3 and replace the original parent population P(t) with P New (t) and proceed to the next iterative calculation.

[0015] Further, the objective functions in step S2 include the objective function f1 of the percentage of the pier area, the objective function f2 of the traffic capacity, the objective function f3 of the available yard area, and the objective function f4 of the number of available operating berths;

[0016] Among them, the objective function f1 of the trestle area percentage is expressed as:

[0017]

[0018] In the formula, A t is the projected plane area of ​​the trestle, A fp is the projected plane area of ​​the foundation pit;

[0019] Among them, the expression of the objective function f2 of the traffic capacity of the trestle arrangement mode is:

[0020]

[0021] In the formula, m is the number of traffic loops between different entrances and exits, B jminis the minimum passable width of the jth traffic loop between different entrances and exits, 2 is the traffic contribution coefficient of the traffic loop between different entrances and exits; n is the number of traffic loops at the same entrance and exit, B kmin is the minimum passable width of the kth traffic loop route with the same entrance and exit;

[0022] Among them, the expression of the available yard area objective function f3 is:

[0023] f3=A s ,

[0024] In the formula, A s The remaining trestle area that can be used as a material stacking site after deducting the necessary traffic area;

[0025] Among them, the objective function f4 of the number of available operating berths is expressed as:

[0026] f4=N op ,

[0027] Where N op The number of non-continuous areas where the remaining trestle board size meets the minimum working space requirements for construction machinery after deducting the necessary traffic width.

[0028] Further, the penalty function in step S2 includes a penalty function g1 of the necessary trestle area condition, penalty functions g2 and g3 of the excavation capacity condition, and a penalty function g4 of the main building priority condition;

[0029] Among them, the penalty function g1 of the necessary trestle area condition is:

[0030]

[0031] Where C1 is the penalty function term imposed on the trestle arrangement when it does not meet the necessary trestle area conditions or the condition of no trestle area, which is the preset value;

[0032] The penalty functions g2 and g3 of the excavation capacity conditions are:

[0033]

[0034]

[0035] Wherein, C2 and C3 are penalty function items imposed on the trestle arrangement when it does not meet the excavation capacity condition, and C2 and C3 are preset values; the excavation capacity condition 1 is that the number of working spaces for excavators and earthmoving vehicles that meet the requirements and do not affect the two-way traffic conditions of the remaining bridge deck vehicles must meet the daily excavation target requirements set by the project, and the excavation capacity condition 2 is that the edge-to-edge distance between the working areas that can meet the requirements and do not affect the two-way traffic conditions of the remaining bridge deck vehicles should be greater than the set value to meet the synchronous operation conditions;

[0036] Among them, the penalty function g4 of the main building priority condition is:

[0037]

[0038] Where C4 is the penalty function term imposed on the trestle layout when it does not meet the main building priority condition.

[0039] Furthermore, the fitness function F of the multi-objective optimization of the trestle is:

[0040]

[0041] Where n is the total number of targets; ω k is the importance weighting coefficient, is the preset value, and satisfies ∑ω k =1, and ω k >0;f k is the objective function of each sub-function; n' is the total number of penalty functions; g j are penalty functions.

[0042] Furthermore, each objective function in the fitness function of the multi-objective optimization of the trestle needs to be regularized before calculating the fitness, where the regularization formula is:

[0043]

[0044] In the formula, f kmin 、f kmax The objective function f k The minimum and maximum values ​​in the entire optimization area; f k 0 is the regularized objective function, and 0≤f k 0 ≤1.

[0045] Due to the adoption of the above technical scheme, the present invention has the following advantages and positive effects compared with the prior art: This application proposes a technical method for multi-objective optimization of foundation pit trestles based on microhabitat genetic algorithms. According to the actual situation and needs of different projects, several target requirements can be set as corresponding objective functions, and the constraints can be set as penalty functions, so as to establish a unified fitness function, so as to form a new population by using the crossover and mutation operations in the genetic algorithm, and obtain the optimal layout plan through iteration. This method utilizes the advantages of genetic algorithms such as intelligence, parallelism, directness, global convergence, and no need for auxiliary information. It uses an improved microhabitat genetic algorithm and a linear weighted penalty function method to solve multi-objective optimization problems, breaks through the human factors of design and construction technicians, and more reasonably optimizes the layout of foundation pit trestles. After engineering verification, this method is feasible and operational, and provides a theoretical and practical basis for the optimization of the layout of open-cut foundation pit trestles. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a method for optimizing the layout of foundation pit trestle bridges based on a microhabitat genetic algorithm in the present invention;

[0047] Figure 2 It is a schematic diagram of the numbering of the space units enclosed by the foundation pit support beams in the present invention;

[0048] Figure 3 This is the initial plan layout of the trestle in an implementation project;

[0049] Figure 4 This is a plan view of the trestle bridge layout after optimization using the foundation pit trestle bridge layout optimization method based on the microhabitat genetic algorithm in an implementation project;

[0050] Figure 5 Schematic diagram of the target fitness evolution process in an implementation project. DETAILED DESCRIPTION

[0051] The following is a further detailed description of the foundation pit trestle arrangement optimization method based on the microhabitat genetic algorithm provided by the present invention in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer in conjunction with the following description. It should be noted that the accompanying drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0052] For ease of understanding, we first briefly introduce the knowledge related to microhabitat genetics. In biology, organizations with common characteristics are called species. The resource environment on which species depend for survival is called a niche. Niche technology is to divide each generation of individuals into several categories, select several individuals with larger fitness from each category as excellent representatives of a category to form a population, and then generate a new generation of individuals through hybridization and mutation in the population and between different populations, and use preselection mechanism or crowding mechanism or sharing mechanism to complete the selection operation. Taking the selection strategy based on the preselection mechanism as an example, its basic idea is that only when the fitness of the offspring individuals generated by crossover and mutation exceeds the fitness of their parent individuals, the offspring individuals generated can replace their parent individuals and be inherited to the next generation population, otherwise the parent individuals will remain in the next generation population. It can ensure that the population evolves in the direction of increasing fitness and accelerate the convergence speed.

[0053] Embodiment 1

[0054] like Figure 1 As shown, the present embodiment provides a method for optimizing the layout of foundation pit trestle bridges based on a microhabitat genetic algorithm, comprising the following steps:

[0055] S1. Number the space units enclosed by the foundation pit support beams, and establish an adjacent unit database and a unit size database for each space unit; determine the entrance and exit unit database, the required unit database, and the non-required unit database according to the project site conditions;

[0056] S2. Determine the calculation population size M, randomly generate M kinds of trestle unit arrangement combinations according to each database in step S1, and constitute the initial parent population P(t); define the objective function f corresponding to each objective of the trestle arrangement optimization and the penalty function g of the trestle arrangement optimization constraint condition, and establish the fitness function F of the trestle multi-objective optimization;

[0057] S3. Calculate the fitness function value F of each arrangement combination individual in the initial parent population P(t), and arrange them in descending order; select the first N arrangement combination individuals to form a combination N(t), where N <M;

[0058] S4. Calculate the crossover probability P according to the fitness of each arrangement combination individual c , crossover operation generates P'(t); or \ and, calculate the mutation probability P m , mutation operation generates P”(t); P'(t) or \ and P”(t) and N(t) are combined to form a new population P N (t), calculate the fitness function value F and sort it in descending order, select the first M arrangement combination individuals to form a new parent population P New(t);

[0059] S5. Determine whether the preset termination condition is reached;

[0060] If yes, stop the operation and get the trestle optimization plan;

[0061] If not, return to step S3 and replace the original parent population P(t) with P New (t) and proceed to the next iterative calculation.

[0062] Combine the following Figure 2 The above-mentioned step S1 is described. Figure 2 In the example, the spatial units formed between the horizontal support beams of the foundation pit and the maintenance structure of the foundation pit are numbered as 1, 2, ..., 13. For example, for the spatial unit 3, its adjacent unit database is spatial unit 1, 4, 5, 8. The unit size database records the size data of each spatial unit. According to the construction requirements and the traffic conditions around the site, an entrance and exit 1 needs to be set in the triangular place. According to the minimum width of the trestle, etc., the spatial units 1-4 are determined as the entrance and exit unit database. In other words, setting the entrance and exit must necessarily set the trestle in some spatial units, and these spatial units constitute the entrance and exit unit database. Due to certain needs of the project, such as setting the yard, assuming machinery, etc., it is necessary to set the trestle in specific spatial units. These specific spatial units constitute the necessary unit database. Of course, the entrance and exit can also be used as a specific requirement, so that the necessary unit database includes the entrance and exit unit database. For some spatial units, due to certain factors, it is impossible to set the trestle, such as the construction environment, construction arrangement, or force conditions, these spatial units constitute the necessary unit database.

[0063] The step S2 is further described below.

[0064] The population size M can be set as needed, for example, M=40.

[0065] The microhabitat genetic algorithm can optimize multiple objectives to obtain an optimization solution. In the pier arrangement, several objectives can be set as needed, such as the percentage of pier area, traffic capacity, available yard area, available number of berths, etc., so as to set the pier area percentage objective function f1, traffic capacity objective function f2, available yard area objective function f3 and available number of berths objective function f4. The above objective functions can be taken as needed. Of course, the support structure force, deflection, etc. can also be used as objectives. This article only takes the above four objective functions as examples for further description. Other technical solutions that are similarly expanded or replaced on the basis of the concept of the present invention are within the protection scope of the present invention.

[0066] (1) The objective function f1 of the trestle area percentage. The trestle area percentage is the ratio of the trestle plane area to the foundation pit plane area, which directly reflects the economic performance of the foundation pit trestle. When the trestle area percentage increases, the corresponding amount of reinforced concrete, column piles and lattice columns forming the trestle will increase accordingly, and vice versa. The expression of the objective function of the trestle area percentage for the i-th trestle arrangement is expressed as:

[0067]

[0068] In the formula, A t is the projected plane area of ​​the trestle, A fp is the projected plane area of ​​the foundation pit. And the maximum value of f1 that satisfies the constraints is the optimal solution.

[0069] (2) Traffic capacity objective function f2. The traffic capacity of the foundation pit trestle is one of the main evaluation criteria for whether the trestle arrangement is reasonable. The traffic capacity represents the flow and circulation of traffic in the foundation pit trestle arrangement. Suppose a foundation pit project has m entrances and exits for construction vehicles to enter and exit, and the number of its traffic circulation lines n (that is, lines with continuous trestles connecting every two entrances and exits or lines with continuous trestles for detours at the same entrance and exit). Among them, the lines with continuous trestles connecting every two entrances and exits have higher traffic capacity, and the lines with continuous trestles for detours at the same entrance and exit have lower traffic capacity, and the width of the two lines is proportional to the traffic capacity. At the same time, according to the external conditions of different projects, the construction vehicle entrances and exits can be specified to meet the actual traffic conditions of each project. The traffic capacity objective function expression of the i-th trestle arrangement method is obtained as follows:

[0070]

[0071] In the formula, m is the number of traffic loops between different entrances and exits, B jmin is the minimum passable width of the jth traffic loop between different entrances and exits, 2 is the traffic contribution coefficient of the traffic loop between different entrances and exits; n is the number of traffic loops at the same entrance and exit, B kmin is the minimum passable width of the kth traffic loop route with the same entrance and exit. And the maximum value of f2 under the constraints is the optimal solution.

[0072] (3) Available yard area objective function f3. The available yard area of ​​the foundation pit trestle is another important criterion for evaluating whether the layout of the foundation pit trestle is reasonable. The calculation of the available yard area of ​​the trestle should be based on the area remaining as a material storage site after deducting the necessary traffic area (calculated based on the minimum two-way vehicle width of each traffic loop route, generally 6m). The objective function expression is:

[0073] f3=A s , (3)

[0074] In the formula, A s The area of ​​the trestle that can be used as a material stacking site is the remaining area after deducting the necessary traffic area (calculated based on the minimum two-way vehicle width of each traffic loop route, generally 6m).

[0075] (4) Objective function f4 of the number of available berths. The number of available berths represents the number of areas that the foundation pit pier can provide for excavators and earthmoving vehicles, truck cranes, concrete truck pumps and fixed pumps to park and operate without affecting the traffic on the roads in the corresponding areas. The requirements for the size of the berth can be determined based on the minimum working space requirements of the construction machinery planned to be equipped in the project. Generally, the required berth size is determined based on the width of the truck crane and concrete truck pump legs and the length of the vehicle. Except for special heavy machinery, the size of 15m×10m is generally agreed to meet general operating requirements. After deducting the berth operating size, the pier passage width should meet the one-way traffic requirements of construction vehicles, which is generally taken as 3m. The objective function expression is:

[0076] f4=N op , (4)

[0077] Where N op The number of non-continuous areas where the remaining trestle board size meets the minimum working space requirement of construction machinery (generally 15m×10m) after deducting the necessary traffic width (generally 3m).

[0078] In order to facilitate calculation, the above multi-objective needs to be converted into a single objective. The weighted coefficient method adjusts each objective according to its relative importance and converts the multi-objective optimization problem into a single objective problem in the form of linear combination:

[0079]

[0080] Where n is the total number of targets; ω k is the importance weighting coefficient, is the preset value, and satisfies ∑ω k =1, and ω k >0;f k When 4 objective functions are selected, n=4.

[0081] Regularization is required before weighting to eliminate the differences in units and quantities of each objective function. The expression is:

[0082]

[0083] In the formula, f kmin 、f kmax The objective function fk The minimum and maximum values ​​in the entire optimization area; f k 0 is the regularized objective function, and 0≤f k 0 ≤1.

[0084] Formula (6) converts each objective function into a value between 0 and 1. Then formula (5) should be rewritten as:

[0085]

[0086] In the process of evolution, especially in the initial stage of evolution, the distribution of the population basically covers the entire optimization area, so the maximum and minimum values ​​that have appeared in the current evolution generation can be used to replace f according to the trial calculation preset or kmax 、f kmin Perform regularization.

[0087] It should be noted that, when optimizing the trestle arrangement scheme, some constraints need to be considered, so when solving the optimal solution, these constraints need to be considered. The present invention adopts the penalty function method to handle the constraint problem, and for the solution individual that does not meet the constraint, a penalty function is imposed to reduce the fitness of the individual, so that the probability of the individual being inherited to the next generation is reduced. For example, necessary trestle area conditions (must have, must not have combination), unearthed ability conditions and main building priority conditions can be considered. Of course, other constraints can also be considered as needed, and this paper is only introduced according to the aforementioned three conditions, and the other technical solutions that are similarly expanded or replaced on the basis of the present invention are all within the protection scope of the present invention.

[0088] (1) Penalty function g1 of necessary trestle area conditions. According to the actual situation of different engineering projects, the areas where trestle plates must be set up can be determined based on technical routes, engineering requirements and engineering experience, such as the main entrance and exit areas, the main construction road areas, the side truss areas at the pit edge that do not meet the traffic conditions, etc.; there are also areas where trestle plates cannot be set up, such as the tower crane body area, the underground steel structure area, etc. By setting the necessary trestle area conditions, the optimization algorithm process can also be accelerated, and the trestle arrangement plans that do not meet the conditions will be eliminated. The penalty function g1 of the necessary trestle area conditions is:

[0089]

[0090] Where C1 is the penalty function term imposed on the trestle arrangement when it does not meet the necessary trestle area conditions or the necessary no trestle area conditions, which is a preset value.

[0091] (2) Penalty functions g2 and g3 of excavation capacity conditions. The excavation capacity condition of the trestle represents the number of areas for synchronous operation of excavators and earthmoving vehicles provided by the trestle arrangement method. It should meet two conditions, namely (1) the number of operating spaces available for excavators and earthmoving vehicles meets the requirements (generally set to 15m×10m) and does not affect the two-way traffic conditions of the remaining bridge deck vehicles (generally 6m) to meet the daily excavation target requirements set by the project (the average daily excavation volume of each excavation operation area is determined according to the mechanical efficiency adopted); (2) The edge-to-edge distance between the operation areas should be greater than 20m to meet the synchronous operation conditions. The penalty functions g2 and g3 of the excavation capacity condition are:

[0092]

[0093]

[0094] Wherein, C2 and C3 are penalty function items imposed on the trestle when the trestle arrangement does not meet the excavation capacity conditions, and C2 and C3 are preset values; the excavation capacity condition 1 is that the number of working spaces available for excavators and earthmoving vehicles that meet the requirements and do not affect the two-way traffic conditions of the remaining bridge deck vehicles must meet the daily excavation target requirements set by the project; the excavation capacity condition 2 is that the edge-to-edge distance between the working areas that can meet the requirements and do not affect the two-way traffic conditions of the remaining bridge deck vehicles should be greater than the set value to meet the synchronous operation conditions.

[0095] (3) Penalty function g4 of the main building priority condition. In the process of underground structure construction, truck pumps are generally preferred for concrete pouring, especially in the main building area, where the base plate is generally thick and the components are densely packed. The use of truck pumps can speed up the concrete pouring speed. In addition, the main building area may have densely packed steel components, and generally requires the truck crane to stop for steel component hoisting. The main building priority condition indicates whether the area around the main building that can meet the parking space requirements of the truck pump and truck crane can completely cover the main building within the coverage range of the robot arm (determined by the selected robot arm length, generally about 40m).

[0096] The penalty function g4 of the main building priority condition is:

[0097]

[0098] Where C4 is the penalty function term imposed on the trestle layout when it does not meet the main building priority condition.

[0099] The genetic algorithm can obtain the next search information by only using the objective function value of the problem being solved, and the use of the objective function value is reflected by evaluating the fitness of the individual. The general process of evaluating the fitness of an individual is: 1) After decoding the individual code string, the individual phenotype is obtained; 2) The objective function value of the corresponding individual can be calculated from the individual phenotype; 3) According to the type of optimization problem, the fitness of the individual is calculated according to a certain conversion rule from the objective function value.

[0100] In the genetic algorithm, the probability of each individual being inherited to the next generation is determined by the fitness of the individual. After considering the objective function and penalty function at the same time, the fitness function calculation formula of the multi-objective optimization of the trestle is:

[0101] F eval =ω1f1 0 +ω2f2 0 +ω3f3 0 +ω4f4 0 +C1+C 2-1 +C 2-2 +C3 (14)

[0102] In the formula, F eval is the fitness of the individual; f1 0 、f2 0 、f3 0 、f4 0 is the individual objective function value after regularization; ω1, ω2, ω3, ω4 are the weighted coefficients of the objective function; C1, C 2-1 , C 2-2 , C3 are the four penalty function terms of the individual, where C1 = -1 or 0, C 2-1 = -1 or 0, C 2-2 =-1 or 0, C3 =-1 or 0.

[0103] The weighting coefficient can be set in different combinations according to the actual needs of the specific project. When the site red line is sufficient outside the foundation pit, the weight of the trestle area percentage ω1 can be increased to achieve the optimization goal of economy; when the foundation pit site environment is not good, the weight of the traffic capacity ω2 and the available yard area ω3 can be increased to achieve the optimization goal of site conditions; when the construction progress of the foundation pit project is tight, the weight of the number of available berths ω4 can be increased to achieve the optimization goal of large-scale synchronous construction.

[0104] Step S4 is further described below.

[0105] Crossover probability P c and mutation probability P m The reasonable determination of is the key to the behavior and performance of genetic algorithms, and directly affects the convergence of the algorithm.c , P m It can change automatically with the fitness. When the fitness of the population tends to be consistent or the local optimal solution, P c , P m Increase, and when the fitness is more dispersed, P c , P m At the same time, for individuals whose fitness value is higher than the average fitness value of the group, a lower P is assigned c , P m , so that they can be protected into the next generation; and individuals with fitness below the average are given a higher P c , P m This individual is eliminated. The implementation method is:

[0106]

[0107]

[0108] Where P c1 is the upper limit of the crossover probability, P c2 is the lower limit of the crossover probability, P m1 is the upper limit of mutation probability, P m2 is the lower limit of mutation probability, f max and f avg are the maximum and average fitness values ​​of the group, respectively, f' is the larger fitness value of the two individuals to be crossed, and f is the fitness value of the individual to be mutated.

[0109] Step S5 is further described below. In step S5, if the preset termination condition is reached, the operation is stopped and the trestle optimization scheme is obtained. The optimization scheme refers to the new parent population P New For the first K methods in (t), the management personnel can select one from these K methods to lay out the trestle according to the needs, for example K = 3 or 4 or 5.

[0110] Embodiment 2

[0111] The feasibility of the algorithm in this paper is verified by taking the optimization of the layout of the rectangular foundation pit trestle with a relatively simple plane as an example. According to the foundation pit support layout diagram provided by the foundation pit retaining design, the unit blocks surrounded by the support beams are numbered, and the adjacent unit number family library is set to detect the continuity of the trestle layout. According to the actual situation of the project, the regional conditions where the trestle plate must be set are set. The above optimization algorithm is used to program and calculate the multi-objective optimization solution of the foundation pit trestle using MATLAB software.

[0112] The original foundation pit support and trestle plan layout provided by the foundation pit support design unit of a certain project is as follows: Figure 3 As shown, there are 3 entrances and exits around the site, and the foundation pit area is 15860m 2Due to the requirements of surrounding environmental protection, the project was divided into 4 areas for construction, and the main building of the project was located in the east side of the pit.

[0113] The optimization algorithm for the layout of foundation pit trestles proposed in this paper is used for calculation. First, the spaces formed between the foundation pit support beams are numbered, and a database of adjacent unit relationships and a database of unit size and area information are established. The unit numbers that must be equipped with trestles are specified by the entrance and exit locations. Since the site conditions around the foundation pit of this project are very tight, the outer circle does not have traffic conditions, and the traffic conditions on the surrounding roads are not good, the site needs to increase the parking capacity. Therefore, a higher value is taken for the traffic capacity ω2. The weighted values ​​of each objective function are: ω1 = 0.1, ω2 = 0.4, ω3 = 0.2, ω4 = 0.3. The optimization results of the trestle layout based on the set weighted values ​​are obtained by using MATLAB software for genetic algorithm programming calculation, as shown in the following figure: Figure 4 shown.

[0114] The comparison of the technical and economic indicators of the pier between the original plan and the optimized plan is shown in Table 1. It can be seen that the number of traffic loop routes and the number of available berths in the optimized plan have increased by 5 (increased by 100%) and 4 (increased by 57%) respectively compared with the original plan, the available yard area has increased by 137.5m2 (increased by 7.5%), and the percentage of pier area has also increased slightly by 3.1%.

[0115] Table 1 Comparison of technical and economic indicators of the trestle between the original scheme and the optimized scheme

[0116]

[0117] Figure 5 The figure shows the iterative evolution process of the optimization target fitness represented by the fitness function proposed above. It can be seen that after 14 generations of iteration, the target average fitness and the maximum fitness tend to have a very small difference space, indicating that the optimization result tends to be stable after 14 generations of calculation. This shows that this algorithm has good efficiency and computational convergence.

[0118] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing the layout of foundation pit trestle bridges based on a microhabitat genetic algorithm, characterized in that: The steps include: S1. Number the space units enclosed by the foundation pit support beams, and establish an adjacent unit database and a unit size database for each space unit; determine the entrance and exit unit database, the required unit database, and the non-required unit database according to the project site conditions; S2. Determine the calculation population size M, randomly generate M kinds of trestle unit arrangement combinations according to each database in step S1, and constitute the initial parent population P(t); define the objective function f corresponding to each objective of the trestle arrangement optimization and the penalty function g of the trestle arrangement optimization constraint condition, and establish the fitness function F of the trestle multi-objective optimization; S3. Calculate the fitness function value F of each arrangement combination individual in the initial parent population P(t), and arrange them in descending order; select the first N arrangement combination individuals to form a combination N(t), where N <M; S4. Calculate the crossover probability P according to the fitness of each arrangement combination individual c , crossover operation generates P'(t); or \ and, calculate the mutation probability P m , mutation operation generates P”(t); P'(t) or \ and P”(t) and N(t) are combined to form a new population P N (t), calculate the fitness function value F and sort it in descending order, select the first M arrangement combination individuals to form a new parent population P New (t); S5. Determine whether the preset termination condition is reached; If yes, stop the operation and get the trestle optimization plan; If not, return to step S3 and replace the original parent population P(t) with P New (t) and proceed to the next iterative calculation.

2. The method for optimizing the layout of foundation pit trestle bridges based on a microhabitat genetic algorithm according to claim 1, characterized in that: The objective functions in step S2 include the pier area percentage objective function f1, the traffic capacity objective function f2, the available yard area objective function f3 and the available berth number objective function f4; Among them, the objective function f1 of the trestle area percentage is expressed as: In the formula, A t is the projected plane area of ​​the trestle, A fp is the projected plane area of ​​the foundation pit; Among them, the expression of the objective function f2 of the traffic capacity of the trestle arrangement mode is: In the formula, m is the number of traffic loops between different entrances and exits, B jmin is the minimum passable width of the jth traffic loop between different entrances and exits, 2 is the traffic contribution coefficient of the traffic loop between different entrances and exits; n is the number of traffic loops at the same entrance and exit, B kmin is the minimum passable width of the kth traffic loop route with the same entrance and exit; Among them, the expression of the available yard area objective function f3 is: <h2 style=";text-align:left;direction:ltr">f3=A<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> , In the formula, A s The remaining trestle area that can be used as a material stacking site after deducting the necessary traffic area; Among them, the objective function f4 of the number of available operating berths is expressed as: f4=N op , Where N op The number of non-continuous areas where the remaining trestle board size meets the minimum working space requirements for construction machinery after deducting the necessary traffic width.

3. The method for optimizing the layout of foundation pit trestle bridges based on a microhabitat genetic algorithm according to claim 2, characterized in that: The penalty function in step S2 includes the penalty function g1 of the necessary trestle area condition, the penalty functions g2 and g3 of the excavation capacity condition, and the penalty function g4 of the main building priority condition; Among them, the penalty function g1 of the necessary trestle area condition is: Where C1 is the penalty function term imposed on the trestle arrangement when it does not meet the necessary trestle area conditions or the condition of no trestle area, which is a preset value; The penalty functions g2 and g3 of the excavation capacity conditions are: Wherein, C2 and C3 are penalty function items imposed on the trestle arrangement when it does not meet the excavation capacity condition, and C2 and C3 are preset values; the excavation capacity condition 1 is that the number of working spaces for excavators and earthmoving vehicles that meet the requirements and do not affect the two-way traffic conditions of the remaining bridge deck vehicles must meet the daily excavation target requirements set by the project, and the excavation capacity condition 2 is that the edge-to-edge distance between the working areas that can meet the requirements and do not affect the two-way traffic conditions of the remaining bridge deck vehicles should be greater than the set value to meet the synchronous operation conditions; Among them, the penalty function g4 of the main building priority condition is: Where C4 is the penalty function term imposed on the trestle layout when it does not meet the main building priority condition.

4. The method for optimizing the layout of foundation pit trestle bridges based on a microhabitat genetic algorithm according to claim 1, characterized in that: The fitness function F of the multi-objective optimization of the trestle is: Where n is the total number of targets; ω k is the importance weighting coefficient, is the preset value, and satisfies ∑ω k =1, and ω k >0;f k is the objective function of each sub-function; n' is the total number of penalty functions; g j are penalty functions.

5. The method for optimizing the layout of foundation pit trestle bridges based on a microhabitat genetic algorithm according to claim 4, characterized in that: Each objective function in the fitness function of the multi-objective optimization of the trestle needs to be regularized before calculating the fitness. The regularization formula is: In the formula, f kmin 、f kmax The objective function f k The minimum and maximum values ​​in the entire optimization area; f k 0 is the regularized objective function, and 0≤f k 0 ≤1.

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