Multi-stage approach for structural damage identification using particle swarm optimization

Structural damage identification using static test data and changes in frequencies

The identification of nonlinear systems with artificial neural networks models has been successfully used in many applications. Most processes in industry are characterized by nonlinear and time-varying behavior. In this context, the identification of mathematical models for nonlinear systems is vital in many fields of engineering. The Radial Basis Function Neural Network (RBF-NN) is a powerful approach for nonlinear identification and can be improved using Particle Swarm Optimization (PSO) approaches. This paper presents a multivariable nonlinear system identification using RBF-NN combined with standard PSO and Constriction Factor PSO (CFPSO) approaches in order to determine the RBF-NN parameters. RBF-NN is considered to be a good choice for black-box modeling problems due to its rapid learning capacity and, therefore, has been applied successfully to nonlinear time series modeling and nonlinear identification. On the other hand, PSO was inspired by the choreography of bird flocks and fish schools and can be seen as a distributed behavior algorithm that performs multidimensional search. Furthermore, promising simulation results from performance analysis of the proposed RBF-NN with PSO training approaches are presented and discussed in this paper showing promising results.

This paper proposes a novel structural damage identification approach by modifying the original Tree Seeds Algorithm (TSA), termed as M-TSA. To enhance the performance of the standard TSA, a one-step K-means clustering mechanism is conducted before starting the seeds search, which turns out to be beneficial to improve the standard algorithm’s global search capacity. The objective function is formulated by using the frequency do-main data. The first several natural frequencies and the incomplete mode shape are employed to build up the objective function and the proposed M-TSA is adopted to obtain the optimal solution and reflect the structure’s health status. Numerical studies on a 26-bar truss are conducted to investigate the accuracy of using the proposed approach for structural damage identification. Two types of uncertainties including the finite element modelling errors and measurements noise, are considered simultaneously. The final identification results show that with the assistance of the K-means clustering, the proposed M-TSA is able to identify the damages accurately and the performance is better than the standard TSA.

Stiffness-based structural health monitoring methods are widely used for detecting the damage in a structure. These stiffness-based structural health monitoring methods uses change in natural frequencies and modeshapes for damage detection. These methods are based on identifying the change in stiffness of the healthy and damage structure to predict the damage in the structure. These stiffness-based methods are not efficient for detecting a small damage in a structure as there is a negligible change in natural frequencies and modeshapes due to a small damage in a structure, however the damping characteristics of the structure are highly sensitive to the damage in a structure. In this paper, new damping-based damage detection procedure has been proposed. In the proposed procedure, the changes in damping matrix of the structure has been used to detect the damage in the structure. The proposed procedure is able (or can) to detect both the location of the damage and the extend of the damage in the structure. The proposed procedure of damping-based damage detection is a 2-step procedure. In the first step, damping matrices of both the healthy and damage structure are identified and in the second step, the identified damping matrices are used for damage detection. Numerical and experimental case studies are presented to demonstrate the effectiveness of the proposed procedure. The results have shown that the proposed damping-based damage detection procedure can be used for detecting damage in a structure with confidence.

Vibration-based damage monitoring in model plate-girder bridges under uncertain temperature conditions

Optimisation of fixture layout is critical to reduce geometric and form error of the workpiece during the machining process. In this paper the optimal placement of fixture elements (locator and clamp locations) under dynamic conditions is investigated using evolutionary techniques. The application of the newly developed particle swarm optimisation (PSO) algorithm and widely used genetic algorithm (GA) is presented to minimise elastic deformation of the workpiece considering its dynamic response. To improve the performances of GA and PSO, an improved GA (IGA) obtained by basic GA (GA) with sharing and adaptive mutation and an improved PSO (IPSO) obtained by basic PSO (PSO) incorporated into adaptive mutation are developed. ANSYS parametric design language (APDL) of finite element analysis is employed to compute the objective function for a given fixture layout. Three layout optimisation cases derived from the high speed slot milling case are used to test the effectiveness of the GA, IGA, PSO and IPSO based approaches. The comparisons of computational results show that IPSO seems superior to GA, IGA and PSO approaches with respect to the trade-off between global optimisation capability and convergence speed for the presented type problems.

The presence of faults is common in engineering composite structures. Precise detection of faults prior to failure has been the focal point for researchers working in this domain. Detection of damage from the modal characteristics is an age-old promising technique besides the artificial intelligence techniques such as neural network (NN), Artificial neural network (ANN), recurrent neural network (RNN), and particle swarm optimization (PSO) methods. However, multiple detection techniques combined can give better results. In this regard, recurrent neural networks (RNN) and modified particle swarm optimization (mPSO) are combined to detect damages in a glass fiber reinforced polymer (GFRP) composite cantilever beam. The proposed hybrid model consists of two stages; indices of relative natural frequencies applied to locate crack parameters (position and severity) by recurrent neural network technique and optimization of the results (position and severity) by mPSO. The results illustrate this method can be implemented to predict multiple transverse cracks and their parameters in a composite beam, manifested in the change of natural frequencies where the error is limited to 0.97% only.

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

A “Pseudo-excitation” approach for structural damage identification: From “Strong” to “Weak” modality

The Cuckoo search (CS) algorithm is a simple and efficient global optimization algorithm and it has been applied to figure out large range of real-world optimization problem. In this paper, a new formula is introduced to the discovering probability process to improve the convergence rate and the Tournament Selection Strategy is adopted to enhance global search ability of the certain algorithm. Then an approach for structural damage identification based on modified Cuckoo search (MCS) is presented. Meanwhile, we take frequency residual error and the modal assurance criterion (MAC) as indexes of damage detection in view of the crack damage, and the MCS algorithm is utilized to identifying the structural damage. A simply supported beam and a 31-bar truss are studied as numerical example to illustrate the correctness and efficiency of the propose method. Besides, a laboratory work is also conducted to further verification. Studies show that, the proposed method can judge the damage location and degree of structures more accurately than its counterpart even under measurement noise, which demonstrates the MCS algorithm has a higher damage diagnosis precision.

This paper presents a two-step approach for structural damage identification in beam structure using the influence line and bird mating optimizer (BMO). Local damage is simulated as the reduction of the elemental Young’s modulus and mass of beam element. The technique for damage localization based on influence line and its derivatives before and after damage for beam structure was outlined. An objective function comprised of dynamic acceleration is utilized for BMO algorithm. The dynamic response data under external force is calculated by Newmark integration method. Numerical examples of a simply supported beam was investigated. Effect of measurement noise is studied. Studies in the paper indicate that the proposed method is efficient and robust for identifying damages in beam structures.

A particle swarm optimization approach on economic and economic-statistical designs of MEWMA control charts

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

Launch vehicle trajectory optimization has gained enormous significance in the recent past. Constraints handling and accuracy of launch vehicle system, are challenging factors, on account of their high degree of non-linearity. This paper brings in the application of thetaparticle swarm optimization (TH-PSO), which is a recently emerged variant of particle swarm optimization (PSO), for launch vehicle trajectory optimization, which can efficiently handle the constraints and drive the system towards optimality. TH–PSO approach is implemented on a multistage liquid propellant rocket, taking angle of attack as the control parameter. Single and dual thrust cases were solved using TH-PSO technique, and a comparative study was made with classical PSO in terms of terminal error, IE consistency of solutions. Based on the statistics, it can be confirmed that in both single and dual thrust cases TH-PSO outperformed, classical PSO. Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved

It is very important to monitor the occurrence of damage of various structures. By early detection of damage and by taking the proper corrective measures the failure of damage could be avoided. When damage occurs in structure it results in change in stiffness properties of the structure which in turn result in change in modal properties like natural frequency, mode shape change, mode shape curvature and strain energy of a structure. These parameters are used as an input to train the artificial neural network(ANN). The efficiency of the ANN to predict the damage at single and multiple damage cases using the change in natural frequency and strain energy was evaluated in this study. The efficiency of individual modes and combination of frequency and strain energy to detect damage is also considered in this study. The modal parameters were obtained by carrying out modal analysis using ANSYS 2021 R1 software and the ANN was developed using MATLAB software. The results indicated that the strain energy accurately predict the damage location and varies linearly with severity of damage. It is better to use combination of frequency and strain energy as indicator to detect damages.