Polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design

Abstract

The application discloses a kind of polyurea-steel fiber concrete high-efficiency blast-resistant structure based on material and structure matching optimization design, and relates to the field of building materials and engineering protection.In the steel fiber concrete wall surface, polyurea is sprayed, and the thickness of the polyurea coating, the volume fraction of the steel fibers and the specific parameters of the concrete layer are precisely adjusted, and a genetic algorithm is used for optimization design.The application realizes a more optimized structure and a more excellent blast-resistant material and structure design scheme.The design not only improves the blast-resistant capacity of the structure when facing extreme impact load, but also considers the construction feasibility, providing an effective means to improve the safety of public and private buildings.The application is suitable for various buildings and facilities that require high blast-resistant performance, and has a wide application prospect in the fields of military, security and special industry.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel fiber reinforced concrete blast-resistant structures, and specifically relates to a polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design. BACKGROUND

[0002] With the acceleration of urbanization and the increase of global terrorism threats, the protection of urban safety and critical infrastructure has become a major challenge for the world. In this context, the development of high-efficiency blast-resistant structures has become an important research direction in protective engineering and civil building design. Traditional blast-resistant structure design mainly relies on materials such as reinforced concrete and reinforced concrete. Although these materials can absorb and disperse the energy generated by explosions to some extent, their performance is still insufficient when facing high-intensity explosions, and they are prone to cracking, peeling, and even structural damage.

[0003] In recent years, polyurea-steel fiber reinforced concrete has been considered as a powerful material for improving the blast-resistant performance of structures due to its excellent impact resistance, high durability, and good crack resistance. However, although polyurea-steel fiber reinforced concrete has shown advantages at the material level, how to effectively match this new material with structure design to realize its potential in the field of blast-resistant structures is still a key technical problem to be solved.

[0004] The present application proposes a polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization, aiming to solve the problems existing in the prior art by genetic algorithm for material and structure matching optimization, to achieve the dual goals of optimizing material performance and structure blast-resistant ability. In this way, the present application not only improves the blast-resistant performance of the structure, but also optimizes the use efficiency of the material, providing a new solution for blast-resistant structure design. SUMMARY

[0005] In order to solve the above problems, the present application adopts the following technical scheme:

[0006] A polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design, characterized by a polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design, which is composed of a polyurea coating, a steel fiber reinforced concrete layer, and a structure optimization design. The polyurea coating is used to improve the surface durability and impact resistance, the steel fiber reinforced concrete layer is used to improve the overall compressive and bending resistance, and the thickness of the polyurea coating, the volume fraction of the steel fiber, and the thickness of the concrete layer are optimized to achieve the best blast-resistant performance.

[0007] The polyurea adopts an anti-blast type polyurea, and the dynamic tensile strength can reach 28.4 Mpa, the elongation at break can reach 482%, and the tear strength can reach 78 N / mm. The shape type of the steel fiber in the steel fiber reinforced concrete adopts an end straight type, and the yield strength is 780 MPa.

[0008] The optimization design adopts a genetic algorithm, sets a target function as maximum anti-blast performance, takes the polyurea coating thickness, the steel fiber volume fraction and the concrete layer thickness as design variables, and obtains optimal design parameters through iterative search.

[0009] Further, the target function is shown in formula (1)

[0010] F(x) = w1·f1(x) + w2·f2(x) - w3·f3(x) - w4·f4(x) + w5·f5(x) - w6·f6(x) (1)

[0011] In formula (1), the mathematical model of the anti-blast structure comprehensive performance evaluation function, f1 is the compressive strength, f2 is the specific energy absorption, f3 is the crack propagation, f4 is the maximum displacement, f5 is the integrity and function retention, and f6 is the cost. w i is the weight of the corresponding performance index, reflecting the importance of different performance indexes in the total optimization target.

[0012] Further, the constraint condition is

[0013] 1mm≤x1≤5mm (2)

[0014] 1%≤x2≤3% (3)

[0015] 100mm≤x3≤500mm (4)

[0016] Wherein x1, x2 and x3 are the thickness of the polyurea coating, the volume fraction of the steel fiber and the thickness of the concrete layer respectively.

[0017] Further, the optimization design adopting the genetic algorithm includes the following steps:

[0018] (a) Encoding conversion is performed on the initial population; the encoding form is to compile the structure model into an encoding string through binary code;

[0019] (b) Decoding and calculating individual fitness, the decoding formula is shown in formula (5), and the fitness formula is consistent with the target function

[0020]

[0021] In formula (5), x i is the actual value of variable i, x i,min is the minimum value of variable i, and xi,max L is the maximum value taken by the variable i, L represents the length of the binary string assigned to each design variable, j is the index of the bit in the binary string, starting from 1 until L, b ij is the jth bit in the binary string.

[0022] (c) performing genetic operations of replication, adaptive crossover, adaptive mutation on the individuals and forming a new generation of population.

[0023] (d) judging termination condition: if the termination condition is met, output the optimal individual for decoding, otherwise return to (b) for loop iteration.

[0024] Further, the replication operator used in step (c) is roulette method.

[0025] Further, the adaptive crossover in step (c) is performed in single-point crossover manner.

[0026] Further, the adaptive mutation in step (c) is performed in basic bit mutation manner.

[0027] Further, step (d) uses a fixed number of iterations as the basis for judging the termination condition.

[0028] The present application has the advantages and beneficial effects that:

[0029] Not only does it exhibit the advantages of polyurea-steel fiber concrete in terms of material, but it also effectively matches this new material with structural design to fully exploit its potential in the field of blast resistance.

[0030] By using genetic algorithm for material and structure matching optimization, the dual objectives of optimizing material performance and structure blast resistance are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 Coding diagram for genetic algorithm

[0032] Figure 2 Roulette diagram

[0033] Figure 3 Basic mutation diagram

[0034] Figure 4 Single-point crossover diagram

[0035] Figure 5 Genetic algorithm flowchart. DETAILED DESCRIPTION

[0036] The application will be further described in detail in connection with specific embodiments, but the protection scope of the application is not limited by the specific embodiments, and is subject to the claims. In addition, any modification or change made by those skilled in the art to the application without departing from the technical scheme of the application will fall within the scope of the claims of the application.

[0037] In order to improve the synergistic optimization of the polyurea-steel fiber concrete blast-resistant structure and maximize the blast-resistant performance of the blast-resistant structure, the genetic algorithm is used to optimize the design of the polyurea-steel fiber concrete.

[0038] A polyurea-steel fiber concrete high-efficiency blast-resistant structure based on material and structure matching optimization design comprises the following steps.

[0039] Adjusting the thickness of the polyurea coating, the volume fraction of the steel fiber in the steel fiber concrete, and the thickness of the steel fiber concrete is the main optimization problem, and is also the main variable of the objective function. In order to seek the optimal solution, the application takes the maximum blast-resistant performance as the objective function, and takes the thickness of the polyurea coating, the volume fraction of the steel fiber in the steel fiber concrete, and the thickness of the steel fiber concrete as the constraint conditions to establish the objective function.

[0040] The design variable is shown as formula (1)

[0041] F(x)=w1·f1(x)+w2·f2(x)-w3·f3(x)-w4·f4(x)+w5·f5(x)-w6·f6(x) (1) In formula (1), the mathematical model of the blast-resistant structure comprehensive performance evaluation function, f1 is the compressive strength, f2 is the specific energy absorption, f3 is the crack propagation, f4 is the maximum displacement, f5 is the integrity and function retention, and f6 is the cost. i w is the weight of the corresponding performance index, reflecting the importance of different performance indexes in the overall optimization target.

[0042] The constraint conditions are shown as formula (2)-(4):

[0043] 1mm≤x1≤5mm (2)

[0044] 1%≤x2≤3% (3)

[0045] 100mm≤x3≤500mm (4)

[0046] Wherein x1, x2 and x3 are the thickness of the polyurea coating, the volume fraction of the steel fiber, and the thickness of the concrete layer respectively.

[0047] (a) For optimization problems, an initial population is first created and encoding transformation is performed via a program. Encoding transformation, as the initial stage of the genetic algorithm's problem-solving process, significantly impacts the computational efficiency of the genetic algorithm. This invention employs binary encoding. This encoding method converts various possible combinations of design parameters into binary strings that the genetic algorithm can process. This allows us to use standard genetic algorithm operations to search for the optimal combination of design parameters, thereby achieving material and structure matching optimization.

[0048] (b) Decode and calculate the individual fitness. The decoding formula is shown in Equation (5). The fitness formula is consistent with the objective function. The fitness score is used in the genetic algorithm to evaluate the performance of individuals. Individuals with higher fitness scores indicate that they perform better in the population and are therefore more likely to be selected to produce offspring. Conversely, individuals with lower fitness scores perform worse, are less competitive in the population, and are more likely to be eliminated in subsequent genetic processes. Furthermore, the genetic algorithm requires that the value of the fitness function must be positive. In a high-efficiency explosion-resistant polyurea-steel fiber reinforced concrete structure based on material and structure matching optimization design, the objective function value and constraints of the individual are used as the evaluation criteria for fitness.

[0049]

[0050] In equation (5), x i x is the actual value of variable i. i,min x is the minimum value that variable i can take. i,max Let be the maximum value that variable i can take, L represent the length of the binary string assigned to each design variable, j be the index of the bit in the binary string, starting from 1 up to L, and b be the maximum value that variable i can take. ij It is the j-th bit in the binary string.

[0051] (c) Genetic operations such as replication, adaptive crossover, and adaptive mutation are performed on individuals to form a new generation population. In this invention, the replication operator used in the genetic algorithm is the roulette wheel method. A schematic diagram of the roulette wheel method is shown below. Figure 2 As shown. The self-...

[0052] The adaptive crossover will be performed using a single-point crossover method, as shown in the diagram below. Figure 3 As shown. The adaptive mutation of this invention will be based on...

[0053] The basic bit mutation method is as follows: (See diagram for basic bit mutation diagram) Figure 4 As shown

[0054] (d) Termination condition determination: If the termination condition is met, the optimal individual is decoded and output; otherwise, return to (b) for iterative iteration. This invention uses a fixed number of iterations as the basis for determining the termination condition.

[0055] (e) Polyurea-steel fiber concrete wall of length 2 m and height 2 m is the case of the genetic algorithm calculation result

[0056]

[0057]

[0058]

[0059]

Claims

1. A polyurea-steel fiber concrete high-efficiency blast-resistant structure based on material and structure matching optimization design, characterized in that, The structure is composed of a polyurea coating and a steel fiber reinforced concrete layer; the polyurea coating is used to improve surface durability and impact resistance, and the steel fiber reinforced concrete layer is used to improve overall compression resistance and bending resistance; the thickness of the polyurea coating, the volume fraction of the steel fiber and the thickness of the concrete layer are optimized to achieve the best blast resistance; The optimization design adopts a genetic algorithm, sets a target function as maximizing the blast resistance, takes the thickness of the polyurea coating, the volume fraction of the steel fiber and the thickness of the concrete layer as design variables, and obtains optimal design parameters through iterative search; The target function is shown as formula (1) （1） Mathematical model of F(x) anti-blast structure comprehensive performance evaluation function in formula (1), is the compressive strength, is the specific energy absorption, is the crack propagation, is the maximum displacement, is the integrity and function retention, is the cost; is the weight of the corresponding performance index, which reflects the importance of different performance indexes in the overall optimization target.

2. The polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design according to claim 1, characterized in that, The polyurea is an anti-blast polyurea, the dynamic tensile strength of which can reach 32.4 Mpa, the elongation at break can reach 482%, and the tear strength can reach 105 N / mm; the steel fiber in the steel fiber reinforced concrete is in a straight-end type, and the yield strength is 780 MPa.

3. The polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design according to claim 1, characterized in that, The constraint condition of the blast-resistant structure is （2） （3） （4） wherein respectively the thickness of the polyurea coating, the volume fraction of steel fibers and the thickness of the concrete layer.

4. The polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design according to claim 1, characterized in that, The genetic algorithm for the optimization design includes the following steps: (a) encoding conversion is performed on an initial population; the encoding form is to compile the structure model into a code string through binary code; (b) decoding and calculating individual fitness, the decoding formula is shown as formula (5), and the fitness formula is consistent with the target function （5） In formula (5) is the actual value of the variable is the minimum value taken by the variable is the maximum value taken by the variable denotes the length of the binary string assigned to each design variable, is the index of the bit in the binary string, starting from 1 up to ， is the bit number in the binary string;​​​ (c) genetic operation is performed on the individuals to form a new generation population, including copying, adaptive crossover and adaptive mutation; (d) termination condition judgment: if the termination condition is met, the optimal individual is decoded and output, otherwise, step (b) is returned for cyclic iteration.

5. The polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design according to claim 4, characterized in that: The copying operator used in step (c) is a roulette method.

6. The polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design according to claim 4, characterized in that: The adaptive crossover in step (c) is performed in a single-point crossover manner.

7. The polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design according to claim 4, characterized in that: The adaptive mutation in step (c) is performed in a basic bit mutation manner.

8. The polyurea-steel fiber reinforced concrete high-efficiency blast-resistant structure based on material and structure matching optimization design according to claim 4, characterized in that: Step (d) uses a fixed number of iterations as the basis for determining the termination condition.

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