A. Rama Mohan Rao

Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique

Setting up a health monitoring system for large-scale civil engineering structures requires a large number of sensors and the placement of these sensors is of great significance for such spatially separated large structures. In this paper, we present an optimal sensor placement (OSP) algorithm by treating OSP as a combinatorial optimization problem which is solved using a swarm intelligence technique called particle swarm optimization (PSO). We propose a new hybrid PSO algorithm by combining a self-configurable PSO with the Nelder–Mead algorithm to solve this rather difficult combinatorial problem of OSP. The proposed algorithm aims precisely to achieve the best identification of modal frequencies and mode shapes. Numerical experiments have been carried out by considering civil engineering structures to evaluate the performance of the proposed swarm-intelligence-based OSP algorithm. Numerical studies indicate that the proposed hybrid PSO algorithm generates sensor configurations superior to the conventional iterative information-based approaches which have been popularly used for large structures. Further, the proposed hybrid PSO algorithm exhibits superior convergence characteristics when compared to other PSO counterparts.

A Meta-Heuristic Algorithm for Multi-Objective Optimal Design of Hybrid Laminate Composite Structures

: In this article, we propose a meta-heuristic algorithm for solving multi-objective combinatorial optimization problems. The proposed multi-objective combinatorial optimization algorithm is developed by combining the good features of popular guided local search algorithms like simulated annealing (SA) and tabu search (TS). It has been organized as a multiple start algorithm to maintain a good balance between intensification and diversification. The proposed meta-heuristic algorithm is evaluated by solving the stacking sequence optimization of hybrid fiber-reinforced composite plate, cylindrical shell, and pressure vessel problems. The standard performance metrics for evaluating multi-objective optimization algorithms are used to demonstrate the effectiveness of the proposed algorithm over other popular evolutionary algorithms like Nondominated Sorting Genetic Algorithms (NSGA-II), Pareto Archived Evolutionary Strategy (PAES), micro-GA, and Multi-Objective Particle Swarm Optimization (MOPSO).

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.

A new parallel overlapped domain decomposition method for nonlinear dynamic finite element analysis

Abstract In this paper a new parallel algorithm for nonlinear transient dynamic analysis of large structures has been presented. An unconditionally stable Newmark-β method (constant average acceleration technique) has been employed for time integration. The proposed parallel algorithm has been devised within the broad framework of domain decomposition techniques. However, unlike most of the existing parallel algorithms (devised for structural dynamic applications) which are basically derived using nonoverlapped domains, the proposed algorithm uses overlapped domains. The parallel overlapped domain decomposition algorithm proposed in this paper has been formulated by splitting the mass, damping and stiffness matrices arises out of finite element discretisation of a given structure. A predictor–corrector scheme has been formulated for iteratively improving the solution in each step. A computer program based on the proposed algorithm has been developed and implemented with message passing interface as software development environment. PARAM-10000 MIMD parallel computer has been used to evaluate the performances. Numerical experiments have been conducted to validate as well as to evaluate the performance of the proposed parallel algorithm. Comparisons have been made with the conventional nonoverlapped domain decomposition algorithms. Numerical studies indicate that the proposed algorithm is superior in performance to the conventional domain decomposition algorithms.

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.

MPI-based parallel finite element approaches for implicit nonlinear dynamic analysis employing sparse PCG solvers

This paper presents three formulations combining domain decomposition based finite element method with linear preconditioned conjugate gradient (LPCG) technique for solving large-scale problems in structural mechanics on parallel processing machines. In the first formulation called the Global Interface Formulation (GIF), the PCG algorithm is applied on the assembled interface stiffness coefficient matrices of all submeshes. The second formulation called Local Submesh Formulation (LSF) operates on the local unassembled submesh matrices and the preconditioner is constructed using the local submesh information. In the third formulation called Local Interface Formulation (LIF), the sparse PCG algorithm is formulated using the unassembled local schur complement matrices of submeshes. Both diagonal and incomplete Cholesky preconditioners have been employed. These domain decomposition based PCG algorithms have been implemented within a finite element code for nonlinear implicit transient dynamic analysis. Time integration is performed using Newmark-β constant average acceleration method. The parallel finite element code uses an MPI-based message passing approach to provide portable parallel execution on shared, distributed and distributed shared memory computers. Numerical experiments have been conducted on PARAM-10000, an Indian parallel supercomputer to evaluate the performance of the implicit parallel nonlinear finite element code employing the three proposed PCG formulations. Numerical studies indicate that the proposed parallel PCG formulations are highly adaptive for parallel computing and superior in performance when compared to the conventional domain decomposition algorithm with parallel direct solver. The LSF formulation, which is amenable for efficient implementation of communications by way of overlapping with computations found to be superior in performance compared to other two PCG formulations.

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.

Parallel mesh-partitioning algorithms for generating shape optimised partitions using evolutionary computing

In this paper, parallel mesh-partitioning algorithms are proposed for generating submeshes with optimal shape using evolutionary computing techniques. It is preferred to employ a formulation for mesh partitioning, which maintains constant number of design variables irrespective of the size of the mesh. Two distinct parallel computing models have been employed. The first model of parallel evolutionary algorithm uses the master-slave concept (single population model) and a new synchronous model is proposed to optimise the performance even on heterogeneous parallel hardware. Alternatively, a multiple population model is also developed which simulates its sequential counter part. The advantage of the second model is that it can fit in large size problems with large population even on moderate capacity parallel computing nodes. The performance of the evolutionary computing based mesh-partitioning algorithm is demonstrated first by solving several practical engineering problems and also several benchmark test problems available in the literature and comparing the results with the multilevel algorithms. Later the speedup of the parallel evolutionary algorithms on parallel hardware is evaluated by solving large scale practical engineering problems.