# Application of Particle Swarm Algorithm in Nanoscale Damage Detection and Identification of Steel Structure

Authors: Ying Zhang
Journal: International Journal of Analytical Chemistry
 (2022)

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## Abstract

In order to identify the damage of a grid structure, the author proposes a damage identification method for grid structures based on particle swarm optimization. First, using the Modal Assurance Criterion (MAC) and combining the respective advantages of frequency and mode shape in damage identification, a fitness function based on frequency and mode shape is constructed. Second, two test functions are used to compare and analyze, which shows that the improved particle swarm algorithm has better optimization performance. Finally, through an example of a grid structure simulation model, the effectiveness of the proposed method is verified. Three working conditions were set up for damage identification, and the results were analyzed.Results. The particle swarm calculation of case 1 converged in the 53rd generation, and case 2 completed the convergence in the 67th generation, which is consistent with the actual situation. Condition 3 completed the convergence at the 125th generation, indicating that the fitness value has been decreasing until then, and it does not change after the convergence, indicating that the convergence is good. The damage degree of No. 92 member was identified as 95.69%, and the damage degree of No. 105 member was 96.28%. Basically, this is in line with the actual situation. Conclusions. The damage identification method based on the particle swarm algorithm can accurately identify the location and degree of damage.


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## Body

## 1. Introduction

With the advancement of modern science and technology, the development of industry and the needs of future human development, modern civil engineering structures are developing in the direction of large-scale, spatial, and complex. These large complex structures, such as bridges, dams, high-rise buildings, offshore structures, and large-span space grid structures, etc., after being put into use in a complex service environment, it is faced with the problem of structural damage accumulation under the influence of design load changes and various sudden external factors [1]. Therefore, if the damage to the structure cannot be found in time and effective measures are taken to deal with it, it will lead to structural damage; it can even lead to catastrophic consequences, causing threats or even losses to the life and property of the country and the people. In order to eliminate the hidden safety hazards of the structure during service to the greatest extent and avoid the occurrence of catastrophic accidents, it is necessary to establish a health monitoring system for large-scale structures. The core problem of the health monitoring system is the damage detection of the structure, it can be seen that the author’s topic has a broad engineering application background.Any structure can be regarded as a mechanical system composed of structural parameters such as stiffness, mass, and damping, once the structure is damaged, the structural parameters will change, resulting in changes in its response characteristics [2]. Therefore, the damage to the structure can be deduced from the change in the structural response characteristics. An ideal damage identification method should be able to distinguish between deviations caused by modeling errors of structures and those caused by structural damage. Although some relatively successful structural damage detection techniques have appeared in recent years, and health monitoring techniques have also been successfully applied, however, the scientific theoretical system of how to judge the current state of the structure from the measured information has not been established, health monitoring and damage detection of complex structures such as bridges, super high-rise buildings, and offshore platforms are still challenges in the field of civil engineering. The identification of the location and degree of structural damage mainly involves the study of damage identification technology and related theoretical methods, in the process, the dynamic response characteristics of the structure are used to analyze. Structural damage identification is of great significance in safety assurance, timely maintenance, and avoidance of safety accidents of the structure. In the process of using the grid structure, it is necessary to identify the damage of key members and nodes, mainly to judge whether the structure is damaged, the damage location, the damage degree, and the damage type. However, due to a large number of rods in the space grid structure, it is difficult to determine the specific damage to the grid structure. Therefore, the research on the damage identification method of grid structure has important engineering significance.In general, at present, the research on health monitoring and damage detection of large-scale structures at home and abroad are on the rise, and a large number of literature and research results appear every year. In recent years, several large international conferences have also made structural damage diagnosis and health monitoring the focus of research and discussion, such as the International Modal Analysis Conference (IMAC), the International Symposium on Structural Health Monitoring, etc., however, there is still a long way to go before it is widely used in practical engineering. Currently, four levels of structural damage detection are considered to include: first, determine whether there is damage to the structure; second, determine the location of structural damage; third, determine the degree of structural damage; and fourth, evaluate the remaining life of the structure. As shown in Figure1:Figure 1
Particle swarm algorithm.


## 2. Literature Review

Xiao et al. By separating the overall mode shape of the structure, and then, using the corresponding indicators of the separated submode shape for damage identification, the structural damage diagnosis is mainly based on the modal confidence criterion (MAC) of the mode shape [3]. Ebrahim et al. With the background of numerical simulation and model testing, the damage identification of the structure under noise is studied, and a damage detection method based on the modal multiresolution complexity spectrum is proposed [4]. Yu et al. used the mode shape change before and after structural damage to identify the continuous beam and achieved good results [5]. Kortenbruck et al. for the first time, based on the numerical simulation of beam structures damage identification was carried out according to the change of flexibility before and after damage [6]. Han et al. studied the damage identification problem in the background of the numerical simulation of the girder bridge structure, and achieved good results, mainly using the weighted modal flexibility method [7]. Murshid and Singh studiedthe effectiveness of the structural damage localization method based on the difference of compliance curvature matrix was verified by numerical simulation of beam-like structures [8]. Zhang et al. believed that when the structure is damaged, the stiffness decreases and the change of the damaged modal strain energy leads to the redistribution of the internal force, for this reason, it is the first time that the change of the modal strain energy of the structural element is used as the damage index for damage detection [9]. Jamous et al. Combining the concept of data fusion with unit damage variables, a new damage identification index, LMSECp is constructed, which can identify damage through the fusion of low-order modal information [10]. Chafi and Afrakhte established a unit damage variable (D) index based on modal strain energy in the construction field [11]. Arasteh et al. Using the numerical simulation of beam-like structures as the background, the method of structural damage identification was studied, and for the first time, a structural damage identification method based on modal curvature was proposed [12]. Wu and Xiong Taking the numerical simulation damage identification of simply supported beams as the research background, the curvature modal damage identification method is used for structural damage [13]. Yao et al. Using the modal curvature identification index, the damage diagnosis of an overpass is studied by numerical simulation technology. In practical applications, although the modal curvature method is more sensitive to structural damage than the modal displacement method, due to the influence of environmental noise and other factors, this method will generate measurement errors, thereby reducing the accuracy of damage identification [14].The particle swarm optimization algorithm has been widely used in the field of damage identification due to its advantages of strong optimization ability, simple algorithm, and few parameter settings. Based on the fitness function of frequency and mode shape, the author will improve the particle swarm algorithm to study the nanoscale damage identification of grid structure.


## 3. Research Methods

### 3.1. Particle Swarm Optimization

Swarm intelligence algorithms are intelligent optimization algorithms developed after observing the social behavior of biological groups (ants, birds, fish, etc.). As an emerging evolutionary computing method, swarm intelligence has been studied by more and more scholars. Swarm intelligence is based on the biological swarm system, simulating biological sociality, and individuals in biological swarms can exchange information, it cooperates with each other and are distributed in the population, and the individual is not controlled by the center, so it has relatively strong robustness and adaptability. Swarm intelligence refers to the characteristic of unintelligent subjects exhibiting intelligent behavior through cooperation. Due to the above advantages, swarm intelligence has become a research hotspot, and it is also being more and more applied to problems such as combinatorial optimization, system complexity, and multiagent simulation. The particle swarm optimization algorithm is a typical swarm intelligence optimization algorithm. The author’s optimal arrangement of sensors and damage identification of the grid structure are based on the particle swarm optimization algorithm.Particle swarm optimization is known as a typical swarm intelligence optimization algorithm. The idea of particle swarm optimization is derived from the study of flight trajectories. When flocks of birds are looking for food, birds can cooperate with each other so that they can work together to find the location of the final food in the process of searching for food. The algorithm first obtains random particles from the population, and then, the particles are iteratively optimized in the global scope. It is mainly realized through the cooperation and competition between particles, particle swarm optimization is more and more applied in the field of engineering, mainly because the particles have the function of memory in the optimization process, which is a practical swarm intelligence algorithm.


#### 3.1.1. Standard Particle Swarm Algorithm

The mathematical explanation of the particle swarm optimization algorithm is as follows: In theD-dimensional space, there are N particles; the position of particle i is represented by the following formula:(1)xi=xi1,xi2,xi3,…,xiD.The velocity of particlei is expressed by the following formula:(2)Vi=Vi1,Vi2,Vi3,…,ViD.The best position experienced by the individual particlei, that is, the individual optimal value p best is expressed as the following formula:(3)pi=pi1′,pi2′,pi3′,…,piD.The global optimal valueg best, that is, the best position in the history of the population, is represented by the following formula:(4)pg=pA,pA,pB,…,piD.The velocity variation range of thed1≤d≤D-th dimension is within −Vmax,d and Vmin,d, and the position variation range is −Xmin,d,Xmax,d.The update formula of thed-dimensional velocity of particle i is the following formula:(5)Vidt+1=Vidt+c1r1tpidt−xidt+c2r2tpgit−xidt.The update formula of thed-dimensional position of particle i is the following formula:(6)Xidt+Xidt+1=Xidt+1,In the formula: i represents the number of particles; d represents the dimension of each particle; r1, r2 represent random functions; c1, c2 represent acceleration constants.


#### 3.1.2. Improved Particle Swarm Algorithm

The improved particle swarm algorithm adopted by the author is the particle swarm algorithm with inertia weight [15]. The improvement method is as follows: After the research on the inertia weight value ω, it is found that the optimization effect is better when [0.4, 0.9] changes, that is, the linear decreasing method is applied, which is also the most commonly used method, as shown in the following formula:(7)ω=ωmax−ttmaxωmax−ωmin,In the formula: Wmax represents the maximum inertia weight; Wmin represents the minimum inertia weight; t represents the number of iterations; tmax represents the maximum number of iterations.By adjustingω, the search area of particles can be limited, so that particles can maintain the inertia of motion while searching in space, maintain the balance of the whole and part, and can search for new areas. When the inertia weight is large, it is beneficial to the search domain [16]. When the inertia weight is small, it is beneficial to local search. Generally speaking, ω can achieve better results in the interval [0.9, 1.2].


#### 3.1.3. Construct the Fitness Function

First, the fitness function in the following formula is constructed with frequency as the index:(8)fωx=∑i=1nωui−ωdiωui2,where: ωui represents the measured i-th order frequency; ωdi represents the calculated i-th order frequency; x represents the row vector 0≤x≤1 of the damage factor.Frequency, as a global variable, cannot identify damage in symmetrically positioned members; however, mode shapes can address this challenge [17]. In order to identify any damage to the structure, the mode shape fitness function is constructed using the modal confidence criterion as follows:(9)fϕx=1−∑i=1jMACi=1−∑i=1jϕuiTϕdi2ϕuiTϕdiϕuiTϕdi,In the formula: ϕui and ϕdi respectively represent the measured structural mode shape and the calculated structural mode shape of the i-th order of the structure.Combining the advantages of the two can effectively identify the damage of the entire structure, so the fitness function of the frequency and mode shape is constructed as follows:(10)F=fωx+fϕx.The fitness function should make the fitness function value as small as possible.


#### 3.1.4. The Main Process of Damage Identification

The principle of damage identification is to modify the nondestructive structure, that is, model modification. Its main process is shown in Figure2.Figure 2
Flow chart of damage identification.


### 3.2. PSO Test Function

In order to ensure that the particle swarm optimization algorithm can be applied to the damage identification of the grid structure, the ability of the particle swarm optimization algorithm and the superiority of the improved particle swarm optimization algorithm are verified by testing the simple function.The following 2 functions were tested with the improved and standard particle swarm algorithm, respectively.(1)
The Rastrigin multimodal function is as follows:(11)fx=∑i=1nxi2−10cos2πxi+10,In the formula:x∈–5.12,5.12; in the parameter setting, the population size is set to 50, and the learning factors c1=2, c2=2, the number of iteration steps is 1000, the maximum inertia weight ωmax=0.9 and the minimum inertia weight ωmin=0.6 of the improved particle swarm algorithm.The results of the above two algorithms tend to be 0, that is, the optimal solution is obtained, which proves the effectiveness of the particle swarm algorithm [18]. The improved particle swarm algorithm has stronger optimization ability.(2)
The Schaffer multimodal function is as follows:(12)fx=sin2x12+x22−0.51+0.001x12+x222a2+b2−0.5,In the formula:xi∈−100,100; The global minimum point of this function is −1. In the parameter setting, the population size is taken as 30, and the others are the same as above.


### 3.3. Numerical Simulation of Damage Identification

#### 3.3.1. Numerical Analysis Model

The author takes the simulation model of the cylindrical shell grid structure as the research object, the simulation model has a total of 60 nodes and 174 rods, and the model size is 3 m × 5 m. The section size of the rod is 42 × 2.5, and the material of the rod is Q235 steel pipe, and the elastic modulus is 200 GPa. Except for the fixed connection at the 4 supports, other rods are connected by hinged connection. In the ANSYS software analysis, the element type of the member is beam188.


#### 3.3.2. Working Condition Setting

Three working conditions were set up to verify the feasibility of the method used by the authors, as follows:Condition 1: No. 162 member is damaged, and the damage degree is 50%.Condition 2: No. 162 member is damaged, and the damage degree is 20%.Case 3: No. 92 and No. 105 members are completely damaged. Damage is modeled by element stiffness reduction, which is 100% at full damage.


## 3.1. Particle Swarm Optimization

Swarm intelligence algorithms are intelligent optimization algorithms developed after observing the social behavior of biological groups (ants, birds, fish, etc.). As an emerging evolutionary computing method, swarm intelligence has been studied by more and more scholars. Swarm intelligence is based on the biological swarm system, simulating biological sociality, and individuals in biological swarms can exchange information, it cooperates with each other and are distributed in the population, and the individual is not controlled by the center, so it has relatively strong robustness and adaptability. Swarm intelligence refers to the characteristic of unintelligent subjects exhibiting intelligent behavior through cooperation. Due to the above advantages, swarm intelligence has become a research hotspot, and it is also being more and more applied to problems such as combinatorial optimization, system complexity, and multiagent simulation. The particle swarm optimization algorithm is a typical swarm intelligence optimization algorithm. The author’s optimal arrangement of sensors and damage identification of the grid structure are based on the particle swarm optimization algorithm.Particle swarm optimization is known as a typical swarm intelligence optimization algorithm. The idea of particle swarm optimization is derived from the study of flight trajectories. When flocks of birds are looking for food, birds can cooperate with each other so that they can work together to find the location of the final food in the process of searching for food. The algorithm first obtains random particles from the population, and then, the particles are iteratively optimized in the global scope. It is mainly realized through the cooperation and competition between particles, particle swarm optimization is more and more applied in the field of engineering, mainly because the particles have the function of memory in the optimization process, which is a practical swarm intelligence algorithm.


### 3.1.1. Standard Particle Swarm Algorithm

The mathematical explanation of the particle swarm optimization algorithm is as follows: In theD-dimensional space, there are N particles; the position of particle i is represented by the following formula:(1)xi=xi1,xi2,xi3,…,xiD.The velocity of particlei is expressed by the following formula:(2)Vi=Vi1,Vi2,Vi3,…,ViD.The best position experienced by the individual particlei, that is, the individual optimal value p best is expressed as the following formula:(3)pi=pi1′,pi2′,pi3′,…,piD.The global optimal valueg best, that is, the best position in the history of the population, is represented by the following formula:(4)pg=pA,pA,pB,…,piD.The velocity variation range of thed1≤d≤D-th dimension is within −Vmax,d and Vmin,d, and the position variation range is −Xmin,d,Xmax,d.The update formula of thed-dimensional velocity of particle i is the following formula:(5)Vidt+1=Vidt+c1r1tpidt−xidt+c2r2tpgit−xidt.The update formula of thed-dimensional position of particle i is the following formula:(6)Xidt+Xidt+1=Xidt+1,In the formula: i represents the number of particles; d represents the dimension of each particle; r1, r2 represent random functions; c1, c2 represent acceleration constants.


### 3.1.2. Improved Particle Swarm Algorithm

The improved particle swarm algorithm adopted by the author is the particle swarm algorithm with inertia weight [15]. The improvement method is as follows: After the research on the inertia weight value ω, it is found that the optimization effect is better when [0.4, 0.9] changes, that is, the linear decreasing method is applied, which is also the most commonly used method, as shown in the following formula:(7)ω=ωmax−ttmaxωmax−ωmin,In the formula: Wmax represents the maximum inertia weight; Wmin represents the minimum inertia weight; t represents the number of iterations; tmax represents the maximum number of iterations.By adjustingω, the search area of particles can be limited, so that particles can maintain the inertia of motion while searching in space, maintain the balance of the whole and part, and can search for new areas. When the inertia weight is large, it is beneficial to the search domain [16]. When the inertia weight is small, it is beneficial to local search. Generally speaking, ω can achieve better results in the interval [0.9, 1.2].


### 3.1.3. Construct the Fitness Function

First, the fitness function in the following formula is constructed with frequency as the index:(8)fωx=∑i=1nωui−ωdiωui2,where: ωui represents the measured i-th order frequency; ωdi represents the calculated i-th order frequency; x represents the row vector 0≤x≤1 of the damage factor.Frequency, as a global variable, cannot identify damage in symmetrically positioned members; however, mode shapes can address this challenge [17]. In order to identify any damage to the structure, the mode shape fitness function is constructed using the modal confidence criterion as follows:(9)fϕx=1−∑i=1jMACi=1−∑i=1jϕuiTϕdi2ϕuiTϕdiϕuiTϕdi,In the formula: ϕui and ϕdi respectively represent the measured structural mode shape and the calculated structural mode shape of the i-th order of the structure.Combining the advantages of the two can effectively identify the damage of the entire structure, so the fitness function of the frequency and mode shape is constructed as follows:(10)F=fωx+fϕx.The fitness function should make the fitness function value as small as possible.


### 3.1.4. The Main Process of Damage Identification

The principle of damage identification is to modify the nondestructive structure, that is, model modification. Its main process is shown in Figure2.Figure 2
Flow chart of damage identification.


## 3.1.1. Standard Particle Swarm Algorithm

The mathematical explanation of the particle swarm optimization algorithm is as follows: In theD-dimensional space, there are N particles; the position of particle i is represented by the following formula:(1)xi=xi1,xi2,xi3,…,xiD.The velocity of particlei is expressed by the following formula:(2)Vi=Vi1,Vi2,Vi3,…,ViD.The best position experienced by the individual particlei, that is, the individual optimal value p best is expressed as the following formula:(3)pi=pi1′,pi2′,pi3′,…,piD.The global optimal valueg best, that is, the best position in the history of the population, is represented by the following formula:(4)pg=pA,pA,pB,…,piD.The velocity variation range of thed1≤d≤D-th dimension is within −Vmax,d and Vmin,d, and the position variation range is −Xmin,d,Xmax,d.The update formula of thed-dimensional velocity of particle i is the following formula:(5)Vidt+1=Vidt+c1r1tpidt−xidt+c2r2tpgit−xidt.The update formula of thed-dimensional position of particle i is the following formula:(6)Xidt+Xidt+1=Xidt+1,In the formula: i represents the number of particles; d represents the dimension of each particle; r1, r2 represent random functions; c1, c2 represent acceleration constants.


## 3.1.2. Improved Particle Swarm Algorithm

The improved particle swarm algorithm adopted by the author is the particle swarm algorithm with inertia weight [15]. The improvement method is as follows: After the research on the inertia weight value ω, it is found that the optimization effect is better when [0.4, 0.9] changes, that is, the linear decreasing method is applied, which is also the most commonly used method, as shown in the following formula:(7)ω=ωmax−ttmaxωmax−ωmin,In the formula: Wmax represents the maximum inertia weight; Wmin represents the minimum inertia weight; t represents the number of iterations; tmax represents the maximum number of iterations.By adjustingω, the search area of particles can be limited, so that particles can maintain the inertia of motion while searching in space, maintain the balance of the whole and part, and can search for new areas. When the inertia weight is large, it is beneficial to the search domain [16]. When the inertia weight is small, it is beneficial to local search. Generally speaking, ω can achieve better results in the interval [0.9, 1.2].


## 3.1.3. Construct the Fitness Function

First, the fitness function in the following formula is constructed with frequency as the index:(8)fωx=∑i=1nωui−ωdiωui2,where: ωui represents the measured i-th order frequency; ωdi represents the calculated i-th order frequency; x represents the row vector 0≤x≤1 of the damage factor.Frequency, as a global variable, cannot identify damage in symmetrically positioned members; however, mode shapes can address this challenge [17]. In order to identify any damage to the structure, the mode shape fitness function is constructed using the modal confidence criterion as follows:(9)fϕx=1−∑i=1jMACi=1−∑i=1jϕuiTϕdi2ϕuiTϕdiϕuiTϕdi,In the formula: ϕui and ϕdi respectively represent the measured structural mode shape and the calculated structural mode shape of the i-th order of the structure.Combining the advantages of the two can effectively identify the damage of the entire structure, so the fitness function of the frequency and mode shape is constructed as follows:(10)F=fωx+fϕx.The fitness function should make the fitness function value as small as possible.


## 3.1.4. The Main Process of Damage Identification

The principle of damage identification is to modify the nondestructive structure, that is, model modification. Its main process is shown in Figure2.Figure 2
Flow chart of damage identification.


## 3.2. PSO Test Function

In order to ensure that the particle swarm optimization algorithm can be applied to the damage identification of the grid structure, the ability of the particle swarm optimization algorithm and the superiority of the improved particle swarm optimization algorithm are verified by testing the simple function.The following 2 functions were tested with the improved and standard particle swarm algorithm, respectively.(1)
The Rastrigin multimodal function is as follows:(11)fx=∑i=1nxi2−10cos2πxi+10,In the formula:x∈–5.12,5.12; in the parameter setting, the population size is set to 50, and the learning factors c1=2, c2=2, the number of iteration steps is 1000, the maximum inertia weight ωmax=0.9 and the minimum inertia weight ωmin=0.6 of the improved particle swarm algorithm.The results of the above two algorithms tend to be 0, that is, the optimal solution is obtained, which proves the effectiveness of the particle swarm algorithm [18]. The improved particle swarm algorithm has stronger optimization ability.(2)
The Schaffer multimodal function is as follows:(12)fx=sin2x12+x22−0.51+0.001x12+x222a2+b2−0.5,In the formula:xi∈−100,100; The global minimum point of this function is −1. In the parameter setting, the population size is taken as 30, and the others are the same as above.


## 3.3. Numerical Simulation of Damage Identification

### 3.3.1. Numerical Analysis Model

The author takes the simulation model of the cylindrical shell grid structure as the research object, the simulation model has a total of 60 nodes and 174 rods, and the model size is 3 m × 5 m. The section size of the rod is 42 × 2.5, and the material of the rod is Q235 steel pipe, and the elastic modulus is 200 GPa. Except for the fixed connection at the 4 supports, other rods are connected by hinged connection. In the ANSYS software analysis, the element type of the member is beam188.


### 3.3.2. Working Condition Setting

Three working conditions were set up to verify the feasibility of the method used by the authors, as follows:Condition 1: No. 162 member is damaged, and the damage degree is 50%.Condition 2: No. 162 member is damaged, and the damage degree is 20%.Case 3: No. 92 and No. 105 members are completely damaged. Damage is modeled by element stiffness reduction, which is 100% at full damage.


## 3.3.1. Numerical Analysis Model

The author takes the simulation model of the cylindrical shell grid structure as the research object, the simulation model has a total of 60 nodes and 174 rods, and the model size is 3 m × 5 m. The section size of the rod is 42 × 2.5, and the material of the rod is Q235 steel pipe, and the elastic modulus is 200 GPa. Except for the fixed connection at the 4 supports, other rods are connected by hinged connection. In the ANSYS software analysis, the element type of the member is beam188.


## 3.3.2. Working Condition Setting

Three working conditions were set up to verify the feasibility of the method used by the authors, as follows:Condition 1: No. 162 member is damaged, and the damage degree is 50%.Condition 2: No. 162 member is damaged, and the damage degree is 20%.Case 3: No. 92 and No. 105 members are completely damaged. Damage is modeled by element stiffness reduction, which is 100% at full damage.


## 4. Analysis of Results

### 4.1. Identification Process and Result Analysis

The parameter settings of the particle swarm algorithm are shown in Table1.Table 1
Particle swarm optimization parameter settings.
Population sizeParticle dimensionThe maximum number of iterationsInertia factorLearning factorParticle velocitywminwmaxc1c2vminvmax1002010000.30.811−2121First, the damage identification simulation analysis is carried out for Case 1 and Case 2, Figures3 and 4 show the particle iteration diagrams of the particle swarm algorithm in the damage identification of Case 1 and Case 2, respectively.Figure 3
Particle iteration diagram of working condition 1.Figure 4
Particle iteration diagram for working condition 2.It can be seen from Figures3 and 4 that the particle swarm fitness function value has finally converged; the particle swarm calculation of Case 1 converged in the 53rd generation, and Condition 2 completed the convergence in the 67th generation, and there is no subsequent change; The fitness value has been decreasing until convergence, which is consistent with the actual situation, and the number of iterations is less than 100. It can be seen that the effect of the improved particle swarm algorithm is very obvious [19].Due to space limitations, the partial results of damage identification under working condition 1 (50% damage degree) and working condition 2 (20% damage degree) are shown in Tables2 and 3.Table 2
Damage identification results of condition 1 (part).
Member no.x1550.07341560.05771570.07141580.06581590.05081600.05481610.05281620.48331630.07951640.05241650.07921660.08871670.07991680.07231690.03021700.04841710.06981720.0547Table 3
Damage identification results of working condition 2 (part).
Member no.x1550.05631560.05441570.08421580.06531590.09321600.02371610.06361620.021281630.04251640.05181650.04331660.04021670.05331680.06071690.02721700.06731710.05521720.0432From the above results, it can be seen that, in both case 1 and case 2, there is a relatively large mutation in the No. 162 member, indicating that the No. 162 member has large damage, and the degree of mutation is different, which are 48.31% and 21.28%, respectively, it is closer to the set damage situation, indicating that the recognition situation is good when a single rod is damaged, the accuracy is high, and the recognition effect of the damage degree is good.Then, the damage identification simulation analysis was carried out for working condition 3, and the population size was expanded to 174 when the parameters were set, and other parameters were the same as above. The iterative diagram of the particle swarm optimization algorithm for working condition 3 is shown in Figure5.Figure 5
Particle iteration diagram for working condition 3.It can be seen from Figure5 that condition 3 completed the convergence at the 125th generation, indicating that the fitness value has been decreasing before this, and it will not change after the convergence, and the convergence is good [20]. Afterwards, we give some identification results for working condition 3 and the result graphics for easy observation.Due to space limitations, the analysis results of some members are listed in Table4.Table 4
Damage identification results for condition 3 (part).
Member no.x810.0708820.0752830.1018840.0538850.1054860.0593870.1042880.0689890.0614900.0855910.1033920.9569930.0685940.0918950.0495960.1005970.0762980.0892990.07111000.09141010.04681020.08131030.07021040.04111050.96281060.05581070.08021080.07941090.08351100.0753It can be seen from the abovementioned figure that the damage degree of the identified No. 92 member is 95.69% and the damage degree of the identified No. 105 member is 96.28%. It basically conforms to the actual situation, indicating that the method also has a good identification effect on the double-position damage condition.


## 4.1. Identification Process and Result Analysis

The parameter settings of the particle swarm algorithm are shown in Table1.Table 1
Particle swarm optimization parameter settings.
Population sizeParticle dimensionThe maximum number of iterationsInertia factorLearning factorParticle velocitywminwmaxc1c2vminvmax1002010000.30.811−2121First, the damage identification simulation analysis is carried out for Case 1 and Case 2, Figures3 and 4 show the particle iteration diagrams of the particle swarm algorithm in the damage identification of Case 1 and Case 2, respectively.Figure 3
Particle iteration diagram of working condition 1.Figure 4
Particle iteration diagram for working condition 2.It can be seen from Figures3 and 4 that the particle swarm fitness function value has finally converged; the particle swarm calculation of Case 1 converged in the 53rd generation, and Condition 2 completed the convergence in the 67th generation, and there is no subsequent change; The fitness value has been decreasing until convergence, which is consistent with the actual situation, and the number of iterations is less than 100. It can be seen that the effect of the improved particle swarm algorithm is very obvious [19].Due to space limitations, the partial results of damage identification under working condition 1 (50% damage degree) and working condition 2 (20% damage degree) are shown in Tables2 and 3.Table 2
Damage identification results of condition 1 (part).
Member no.x1550.07341560.05771570.07141580.06581590.05081600.05481610.05281620.48331630.07951640.05241650.07921660.08871670.07991680.07231690.03021700.04841710.06981720.0547Table 3
Damage identification results of working condition 2 (part).
Member no.x1550.05631560.05441570.08421580.06531590.09321600.02371610.06361620.021281630.04251640.05181650.04331660.04021670.05331680.06071690.02721700.06731710.05521720.0432From the above results, it can be seen that, in both case 1 and case 2, there is a relatively large mutation in the No. 162 member, indicating that the No. 162 member has large damage, and the degree of mutation is different, which are 48.31% and 21.28%, respectively, it is closer to the set damage situation, indicating that the recognition situation is good when a single rod is damaged, the accuracy is high, and the recognition effect of the damage degree is good.Then, the damage identification simulation analysis was carried out for working condition 3, and the population size was expanded to 174 when the parameters were set, and other parameters were the same as above. The iterative diagram of the particle swarm optimization algorithm for working condition 3 is shown in Figure5.Figure 5
Particle iteration diagram for working condition 3.It can be seen from Figure5 that condition 3 completed the convergence at the 125th generation, indicating that the fitness value has been decreasing before this, and it will not change after the convergence, and the convergence is good [20]. Afterwards, we give some identification results for working condition 3 and the result graphics for easy observation.Due to space limitations, the analysis results of some members are listed in Table4.Table 4
Damage identification results for condition 3 (part).
Member no.x810.0708820.0752830.1018840.0538850.1054860.0593870.1042880.0689890.0614900.0855910.1033920.9569930.0685940.0918950.0495960.1005970.0762980.0892990.07111000.09141010.04681020.08131030.07021040.04111050.96281060.05581070.08021080.07941090.08351100.0753It can be seen from the abovementioned figure that the damage degree of the identified No. 92 member is 95.69% and the damage degree of the identified No. 105 member is 96.28%. It basically conforms to the actual situation, indicating that the method also has a good identification effect on the double-position damage condition.


## 5. Conclusions

The author studies the application of particle swarm algorithm in structural damage identification based on the simulation model of grid structure, and the main conclusions are as follows:(1)
The reliability of the standard particle swarm optimization algorithm and the improved particle swarm optimization algorithm were tested by the function, and the two algorithms were compared. Both of them achieved the optimal result, which shows the effectiveness of particle swarm optimization. The number of iterations of the improved particle swarm optimization algorithm is significantly less, indicating that its convergence performance is better.(2)
The single damage and different degrees of damage of the grid structure are identified and studied, and the single damage condition of No. 162 member is set, and different damage degrees are set. The result analysis shows that the particle swarm algorithm can effectively identify the location and degree of damage, and the convergence speed is fast, which verifies the effectiveness of the particle swarm algorithm.(3)
By setting the multidamage condition of the grid structure, the damage identification of the grid structure is analyzed, and the results show that, whether it is a single damage or a multidamage situation, the method achieves a good recognition effect and the recognition accuracy reaches 95%, which meets the author’s optimization goal.



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*Source: 4300840-2022-10-12.xml*