CN114239107A - A method of determining the optimal number of pile drivers based on the genetic algorithm of matlab software - Google Patents

A method of determining the optimal number of pile drivers based on the genetic algorithm of matlab software Download PDF

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CN114239107A
CN114239107A CN202111527494.5A CN202111527494A CN114239107A CN 114239107 A CN114239107 A CN 114239107A CN 202111527494 A CN202111527494 A CN 202111527494A CN 114239107 A CN114239107 A CN 114239107A
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pile
genetic algorithm
construction
matlab software
optimal
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叶钦繁
李光宁
郭凌勇
蒙锐
何宇
王峰
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Zhuhai Huafa Habitat Life Research Institute Co Ltd
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Zhuhai Huafa Habitat Life Research Institute Co Ltd
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Abstract

The invention discloses a method for determining the number of optimal pile machines based on matlab software genetic algorithm, which relates to the field of constructional engineering and has the technical key points that: firstly, solving by using nonlinear programming in the theory of operational research, obtaining an optimal solution by using a genetic algorithm (tool kit) based on MATLAB software, constructing a model of pile foundation construction, and solving the number of the optimal pile machines constructed on site according to the model of the foundation construction. The invention solves the problems by using the nonlinear programming in the theory of operational research, obtains the optimal solution by using the genetic algorithm (tool kit) based on MATLAB software, and can calculate the optimal pile driver number in site construction by the constraint conditions determined by the total construction period, the pile foundation number, the field area and the like.

Description

Optimal pile machine number determination method based on matlab software genetic algorithm
Technical Field
The invention relates to the field of constructional engineering, in particular to a method for determining the number of optimal pile machines based on matlab software genetic algorithm.
Background
The method is limited by factors such as construction site area, cost and the like, the pile machines cannot be infinitely arranged in the pile foundation construction, and the number of the pile machines in the traditional pile foundation construction approach is often determined according to engineering experience 'brain shooting bags'.
At present, how to combine engineering experience and accurate calculation to achieve reasonable pile machines, not only can meet construction requirements, but also can save total cost to the minimum. Therefore, it is necessary to invent an optimal pile driver number determination method based on matlab software genetic algorithm to solve the above problems.
Disclosure of Invention
The invention aims to provide a method for determining the number of optimal pile machines based on matlab software genetic algorithm, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the optimal pile machine quantity determination method based on MATLAB software genetic algorithm comprises the steps of firstly solving by using nonlinear programming in the operational research principle, and obtaining an optimal solution by using genetic algorithm (tool box) based on MATLAB software;
constructing a model of pile foundation construction, and solving the number of the optimal pile machines for site construction according to the model of the foundation construction;
the concrete model for building the pile foundation construction is as follows:
setting the requirement of a construction period as A, the number of tubular piles as B, the number of drilling and pouring pile foundations as C, and the floor area of a cell as D;
meanwhile, the daily cost of each static pile machine (construction pipe pile) is set as a, the daily construction quantity is set as b, and the working surface is set as c;
setting the daily cost of each bored pile driver (construction bored piles) as d, the daily construction quantity as e, and the working surface as f;
obtaining a formula according to a nonlinear programming in the operational research theory:
minZ＝aX1Y1+dX2Y2；
cX1+fX2＜D/2；
bX1eY1≥B；
X2Y2≥C；
X1≤g；X2≤h；
Y1≤A；Y2≤A；
wherein Z is the total cost, minZ is the lowest total cost, and X is the total cost1Number (integer) of static pile press, Y1Static pile machine construction days (integer), X2Number (integer) of bored pile-pouring machines, Y2The construction days (integer) of the drilling and pouring pile machine, and g and h are empirical values taken by constructors;
finally, X is calculated through matlab software genetic algorithm (tool box)1、Y1、X2、Y2And minZ results.
In the method for determining the number of the optimal pile machines based on the matlab software genetic algorithm, X is finally calculated each time through the matlab software genetic algorithm (tool box)1、Y1、X2And Y2The results are different, but the lowest value of the total cost of the objective function minZ is the same.
In the above method for determining the number of optimal pile machines based on matlab software genetic algorithm, the objective function in the matlab software genetic algorithm (tool box) is as follows: z is aX1Y1+dX2Y2。
In the above method for determining the number of optimal pile machines based on matlab software genetic algorithm, the nonlinear constraint conditions in the matlab software genetic algorithm (tool box) are as follows: cX1+fX2＜D/2、bX1eY1Not less than B and X2Y2≥C。
In the above method for determining the number of optimal pile machines based on matlab software genetic algorithm, the linear constraint conditions in the matlab software genetic algorithm (tool box) are as follows: x1≤g，X2≤h，Y1A and Y are not more than2≤A。
The invention has the technical effects and advantages that:
1. the invention solves the problem by applying the nonlinear programming in the operational research theory and obtains the optimal solution by applying the genetic algorithm (tool kit) based on MATLAB software;
2. the invention can calculate the optimal pile machine number in site construction through the constraint conditions determined by the total construction period, the pile foundation number, the field area and the like.
Drawings
FIG. 1 is a schematic diagram of the calculation of the objective function of the present invention.
FIG. 2 is a schematic diagram of the calculation of the nonlinear constraints of the present invention.
FIG. 3 is a schematic diagram of a first linear constraint of the present invention.
FIG. 4 is a schematic diagram of a second linear constraint of the present invention.
FIG. 5 is a schematic diagram of the algorithm calculation of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The invention discloses a method for determining the number of optimal piling machines based on MATLAB software genetic algorithm, which is shown in fig. 1-5, and comprises the steps of firstly solving by using nonlinear programming in the theory of operation and research, and obtaining an optimal solution by using genetic algorithm (tool kit) based on MATLAB software;
constructing a model of pile foundation construction, and solving the number of the optimal pile machines for site construction according to the model of the foundation construction;
the concrete model for building the pile foundation construction is as follows:
setting the requirement of a construction period as A, the number of tubular piles as B, the number of drilling and pouring pile foundations as C, and the floor area of a cell as D;
meanwhile, the daily cost of each static pile machine (construction pipe pile) is set as a, the daily construction quantity is set as b, and the working surface is set as c;
setting the daily cost of each bored pile driver (construction bored piles) as d, the daily construction quantity as e, and the working surface as f;
obtaining a formula according to a nonlinear programming in the operational research theory:
minZ＝aX1Y1+dX2Y2；
cX1+fX2＜D/2；
bX1eY1≥B；
X2Y2≥C；
X1≤g；X2≤h；
Y1≤A；Y2≤A；
wherein Z is the total cost, minZ is the lowest total cost, and X is the total cost1Number (integer) of static pile press, Y1Static pile machine construction days (integer), X2Number (integer) of bored pile-pouring machines, Y2The construction days (integer) of the drilling and pouring pile machine, and g and h are empirical values taken by constructors;
finally, X is calculated through matlab software genetic algorithm (tool box)1、Y1、X2、Y2And minZ result, and finally calculating X through matlab software genetic algorithm (tool box) each time1、Y1、X2And Y2The results are different, but the lowest value of the total cost of the objective function minZ is the same.
Wherein the objective function is: z is aX1Y1+dX2Y2The nonlinear constraint is: cX1+fX2＜D/2、bX1eY1Not less than BAnd X2Y2C is larger than or equal to C, and the linear constraint condition is as follows: x1≤g，X2≤h，Y1A and Y are not more than2≤A。
Example 2
The construction of a pile foundation in a certain community requires 60 days for a construction period, the number of pipe piles is 900, the number of drilling and pouring pile foundations is 600, and the floor area of the community is 40000m2Assuming that the cost of each static pile machine (construction pipe pile) is 1 ten thousand yuan per day, 10 static pile machines are constructed per day, and the working surface is 10 x 10 to 100m2(ii) a The cost of each bored pile machine (construction bored pile) is 0.4 ten thousand yuan per day, and the working surface is 6 × 6 ═ 36m2Construction was performed 1 per day.
Obtaining a formula according to a nonlinear programming in the operational research theory:
minZ＝X1Y1+0.4X2Y2；
100X1+36X2＜40000/2；
10X1Y1≥900；
X2Y2≥600；
X1less than or equal to 5 (empirical value); x2Less than or equal to 20 (empirical value);
Y1less than or equal to 60 (construction period constraint); y is2Less than or equal to 60 (construction period constraint);
wherein Z is the total cost, minZ is the lowest total cost, and X is the total cost1Number (integer) of static pile press, Y1Static pile machine construction days (integer), X2Number (integer) of bored pile-pouring machines, Y2The construction days (integer) of the drilling and pouring pile machine, and g and h are empirical values taken by constructors;
finally, the genetic algorithm (tool box) is calculated by matlab software:
X1＝3X2＝12Y1＝30Y250minZ 330 (ten thousand yuan)
Of course, the result of each genetic algorithm is not unique, and the second calculation yields:
X1＝2X2＝15Y1＝45Y240minZ 330 (ten thousand yuan)
But the lowest value of the total cost of the objective function minZ is the same.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.

Claims (5)

1.基于matlab软件遗传算法的最优桩机数量确定方法，其特征在于，首先运用运筹学原理中的非线性规划进行解答，并运用基于MATLAB软件中的遗传算法(工具箱)得到最优解；1. The method for determining the optimal number of pile drivers based on the genetic algorithm of matlab software, which is characterized in that, firstly, the nonlinear programming in the principle of operations research is used to solve the problem, and the optimal solution is obtained by using the genetic algorithm (toolbox) based on the MATLAB software. ; 构建桩基础施工的模型，并根据基础施工的模型来解答现场施工最优桩机的数量；Build a model of pile foundation construction, and answer the optimal number of pile drivers for on-site construction according to the model of foundation construction; 建立桩基础施工的模型具体为：The model for establishing the pile foundation construction is as follows: 将工期要求设为A，管桩数量设为B，钻孔灌注桩基数量设为C，小区占地面积设为D；Set the construction period requirement as A, the number of pipe piles as B, the number of bored pile foundations as C, and the area of the plot as D; 同时将每台静压桩机(施工管桩)每天的费用设为a，每天施工的数量为b，作业面设为c；At the same time, the daily cost of each static pile driver (construction pipe pile) is set as a, the daily construction quantity is b, and the working surface is set as c; 将每台钻孔灌注桩机(施工钻孔灌注桩)每天的费用设为d，每天施工的数量为e，作业面设为f；Set the daily cost of each bored pile driver (construction of bored piles) as d, the daily construction quantity as e, and the working surface as f; 依据运筹学原理中的非线性规划得到公式：According to the nonlinear programming in the principles of operations research, the formula is obtained: minZ＝aX1Y1+dX2Y2；minZ=aX 1 Y 1 +dX 2 Y 2 ; cX1+fX2＜D/2；cX 1 +fX 2 <D/2; bX1eY1≥B；bX 1 eY 1 ≥B; X2Y2≥C；X 2 Y 2 ≥C; X1≤g；X2≤h；X 1 ≤ g; X 2 ≤ h; Y1≤A；Y2≤A；Y 1 ≤A; Y 2 ≤A; 式中，Z＝总费用，minZ＝最低总费用，X1＝静压桩机数量(整数)，Y1＝静压桩机施工天数(整数)，X2＝钻孔灌注桩机数量(整数)，Y2＝钻孔灌注桩机施工天数(整数)，g和h均为施工人员取的经验值；In the formula, Z = total cost, minZ = minimum total cost, X 1 = number of static pile drivers (integer), Y 1 = construction days of static pile drivers (integer), X 2 = number of bored pile drivers (integer) ), Y 2 = the construction days of the bored pile driver (integer), and g and h are the empirical values taken by the construction personnel; 通过matlab软件遗传算法(工具箱)最后计算出X1、Y1、X2、Y2以及minZ的结果。Finally, the results of X 1 , Y 1 , X 2 , Y 2 and minZ are calculated by the genetic algorithm (toolbox) of matlab software. 2.根据权利要求1所述的基于matlab软件遗传算法的最优桩机数量确定方法，其特征在于，每次通过所述matlab软件遗传算法(工具箱)最后计算出X1、Y1、X2以及Y2的结果不尽相同，但是目标函数minZ总费用的最低值都是一样的。2. the method for determining the number of optimal pile drivers based on matlab software genetic algorithm according to claim 1, is characterized in that, X 1 , Y 1 , X are finally calculated by described matlab software genetic algorithm (toolbox) at every turn The results of 2 and Y 2 are not the same, but the minimum value of the total cost of the objective function minZ is the same. 3.根据权利要求1所述的基于matlab软件遗传算法的最优桩机数量确定方法，其特征在于，所述matlab软件遗传算法(工具箱)中的目标函数为：Z＝aX1Y1+dX2Y2。3. the method for determining the number of optimal pile drivers based on matlab software genetic algorithm according to claim 1, is characterized in that, the objective function in described matlab software genetic algorithm (tool box) is: Z=aX 1 Y 1 + dX 2 Y 2 . 4.根据权利要求1所述的基于matlab软件遗传算法的最优桩机数量确定方法，其特征在于，所述matlab软件遗传算法(工具箱)中的非线性约束条件为：cX1+fX2＜D/2、bX1eY1≥B以及X2Y2≥C。4. the optimal pile driver quantity determination method based on matlab software genetic algorithm according to claim 1, is characterized in that, the nonlinear constraint condition in described matlab software genetic algorithm (tool box) is: cX 1 +fX 2 <D/2, bX 1 eY 1 ≥B, and X 2 Y 2 ≥C. 5.根据权利要求1所述的基于matlab软件遗传算法的最优桩机数量确定方法，其特征在于，所述matlab软件遗传算法(工具箱)中的线性约束条件为：X1≤g，X2≤h，Y1≤A和Y2≤A。5. the optimal pile driver quantity determination method based on matlab software genetic algorithm according to claim 1, is characterized in that, the linear constraint condition in described matlab software genetic algorithm (tool box) is: X 1 ≤ g, X 2 ≤ h, Y 1 ≤ A and Y 2 ≤ A.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115033956A (en) * 2022-05-26 2022-09-09 珠海十字门中央商务区建设控股有限公司 Calculation method of budget engineering quantity of drainage board based on range map of drilling radiant drainage board

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
王勇: "MATLAB中用遗传算法求解约束非线性规划问题", 哈尔滨商业大学学报(自然科学版), 31 August 2006 (2006-08-31) *
齐宝库 等: "多种配套作业机械优化配置方案求解方法研究", 沈阳建筑大学学报(自然科学版), 30 November 2007 (2007-11-30) *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115033956A (en) * 2022-05-26 2022-09-09 珠海十字门中央商务区建设控股有限公司 Calculation method of budget engineering quantity of drainage board based on range map of drilling radiant drainage board

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