K. Lakshmi

Discrete hybrid PSO algorithm for design of laminate composites with multiple objectives

This article presents a multi-objective discrete hybrid adaptive swarm intelligence algorithm for combinatorial optimization and applied for design optimization of fiber-reinforced composite structures. An approach is presented in this article to integrate a Pareto dominance concept into the adaptive PSO algorithm developed earlier by the first author in order to handle multi-objective optimization problems. In fact, a hybrid version of the adaptive PSO algorithm is now proposed in this article for multi-objective optimization by integrating with discrete as well as continuous neighborhood search algorithms. Further, an external archive technique is also integrated in order to collect the historical Pareto optimal solutions. The design constraints are handled in this article by treating them as additional objectives. Numerical studies have been carried out through the optimization of a hybrid fiber-reinforced composite plate, laminate composite cylindrical shell, and also a pressure vessel problem with varied number of design objectives. Standard performance metrics are employed to evaluate the performance of the proposed multi-objective hybrid PSO algorithm with both discrete as well as continuous neighborhood search algorithms. Studies clearly favor hybrid PSO with variable-depth neighborhood search algorithm. Finally, comparisons have also been made with other popular evolutionary algorithms like NSGA-II, PAES, Micro-GA, and SPEA2 to demonstrate the effectiveness of the proposed hybrid multi-objective PSO algorithm.

Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

Proposed a new damage diagnostic technique for detection of delamination in laminated composite structures.Proposed algorithm is developed combining FRF, PCA and dynamic quantum PSO algorithm.Integrated an optimal sensor placement algorithm to test the proposed algorithm with limited measurements.A new dynamic quantum PSO algorithm is proposed to solve the inverse problem associated with damage assessment. This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon-epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.

Multi-objective Optimal Design of Hybrid Laminate Composite Structures Using Scatter Search

In this article, a new algorithm for solving multi-objective optimization problems is proposed by extending the single objective scatter search template to deal with multiple objectives. The features like Pareto dominance, density estimation, and an external archive to store the nondominated solutions are added. The traditional solution combination of scatter search template is however replaced with two-point crossover operator being used in evolutionary algorithms in order to improve the convergence characteristics of the proposed scatter search algorithm. The resulting hybrid scatter search algorithm is employed to solve stacking sequence optimization of hybrid fiber-reinforced composite plate, cylindrical shell, and pressure vessel. Performance metrics are used to demonstrate the effectiveness of the proposed algorithm over other popular evolutionary algorithms like NSAGA-II, PAES, Micro-GA, and MOPSO.

Optimal design of stiffened laminate composite cylinder using a hybrid SFL algorithm

Shuffled frog-leaping algorithm (SFLA), a memetic meta-heuristic, is proved to be a successful combinatorial optimization algorithm and applied to several engineering problems. However, its potential is not explored in the context of laminate stacking sequence optimization of composite structures. In this article, we propose a hybrid version of SFLA for solving the combinatorial optimization problem associated with lay-up sequence optimization of laminate composite structures. In order to improve the computational performance as well as reliability of the optimal solutions, a customized neighborhood search algorithm and an adaptive search factor are incorporated in to the SFL algorithm to accelerate the convergence characteristics. Apart from this, a crossover operator is suitably incorporated in the proposed hybrid SFLA. Numerical experiments have been carried out by first considering the problem of buckling and failure load optimization of composite plate and later, optimal design of stiffened composite cylindrical shells. Superior convergence characteristics and robustness of the proposed hybrid SFLA is demonstrated by comparing with other popular meta-heuristic algorithms including genetic algorithms.

Sensor fault detection in structural health monitoring using null subspace–based approach

It is essential to have a robust sensor fault detection and isolation algorithm integrated with successful online continuous structural health monitoring scheme to avoid false alarms. In this work, a sensor fault detection and isolation technique based on null subspace method is presented. The robustness of the proposed sensor failure detection and isolation algorithm is demonstrated using the vibration data obtained from an experimental study of a scaled down bridge model. Studies presented in this article clearly indicate that the proposed method is robust in identification of exact time instant of sensor fault and also isolation of faulty sensor. The proposed algorithm is equally good for all possible types of sensor faults (both additive and multiplicative) and also capable of isolating multiple sensor faults in spatial location of the structure.

A robust damage-detection technique with environmental variability combining time-series models with principal components

In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration time-history data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

Optimal design of laminate composite plates using dynamic hybrid adaptive harmony search algorithm

Stacking sequence design is indispensable to make efficient use of the material properties of laminated composite materials. These laminated composites are usually fabricated from unidirectional plies of given thickness with fiber orientations limited to a small set of angles (e.g., 0°, +45°, –45°, and 90°). This leads to a combinatorial optimisation problem, which is rather complex to solve. In this paper, a new multi-population-based harmony search algorithm with dynamic interaction of populations, hybridised with a customised neighborhood algorithm to solve the layup sequence design problem of laminate composite plate is presented. Numerical experiments have been carried out by first considering the buckling and failure load optimisation of a laminate composite plate and later studies have been carried out using hybrid laminate plates for maximisation of buckling load and fundamental frequency with varied levels of hybridisation and for various plate aspect ratios. Numerical studies carried out in this paper clearly indicate that proposed dynamic hybrid adaptive harmony search algorithm is an effective combinatorial optimisation tool for stacking sequence optimisation of laminate composite structures and the computational performance is found to be superior when compared to other popular implementations of harmony search algorithm.

A Sensor Fault Detection Algorithm for Structural Health Monitoring Using Adaptive Differential Evolution

New sensor technologies make it possible to monitor structures with a dense array of sensors. In a dense sensor network with low-cost sensor nodes, it is quite probable that some nodes fail, resulting in unreliable data for inference and decision. In a large sensor network, the computational time in isolating the faulty sensor using the current algorithms is prohibitive for online sensor fault isolation. In this article, we combine the PCA-based algorithm with an adaptive differential evolution algorithm to improve the performance of sensor fault isolation. Numerical investigations are presented to demonstrate the effectiveness of the proposed algorithm.