Damodar Maity

Damage assessment of structures using hybrid neuro-genetic algorithm

Techniques for detecting elemental level damage using the traditional methods receive the setback because of the difficulties in formulating the problems mathematically, specially in case of inverse problems. Artificial neural networks (ANN) have been proved to be an effective alternative for solving the inverse problems because of the pattern-matching capability. But there is no specific recommendation on suitable design of network for different structures and generally the parameters are selected by trial and error, which restricts the approach context dependent. A hybrid neuro-genetic algorithm is proposed in order to automate the design of neural network for different type of structures. The neural network is trained considering the frequency and strain as input parameter and the location and amount of damage as output parameter. The performance is demonstrated using two test problems: (i) clamped-free beam and (ii) plane frame. The outcomes of the results are quite encouraging and prove the robustness of the proposed damage assessment algorithm.

Damage assessment of truss structures from changes in natural frequencies using ant colony optimization

Abstract A method is presented to detect and assess structural damages from changes in natural frequencies using ant colony optimization (ACO) algorithm. It is possible to formulate the inverse problem in terms of optimization and then to utilize a solution technique employing ACO to assess the damages of structures using natural frequencies. The study indicates the potentiality of the developed code to solve a wide range of inverse identification problems in a systematic way. The developed code is used to assess damages of truss like structures using first few natural frequencies. The outcomes of the results show that the developed method can detect and estimate the amount of damages with satisfactory precision.

Damage assessment in structure from changes in static parameter using neural networks

Damage to structures may occur as a result of normal operations, accidents, deterioration or severe natural events such as earthquakes and storms. Most often the extent and location of damage may be determined through visual inspection. However, in some cases this may not be feasible. The basic strategy applied in this study is to train a neural network to recognize the behaviour of the undamaged structure as well as of the structure with various possible damaged states. When this trained network is subjected to the measured response, it should be able to detect any existing damage. This idea is applied on a simple cantilever beam. Strain and displacement are used as possible candidates for damage identification by a back-propagation neural network. The superiority of strain over displacement for identification of damage has been observed in this study

Time-domain analysis of infinite reservoir by finite element method using a novel far-boundary condition

Abstract The focus of the present paper is on the time-domain analysis of a dam–reservoir system using a novel far-boundary condition to model an infinite fluid domain to a finite one. The method is based on the finite element discretization of the complete system assuming only pressure to be the nodal unknown parameter and the fluid to be compressible. The truncation boundary condition is derived numerically from the classical wave equation. Studies show the accuracy of the proposed far-boundary condition, using finite element method, while comparing with the existing ones available in the literature.

Structural damage assessment using FRF employing particle swarm optimization

This paper evaluates the use of Frequency Response Function (FRF) with the help of Particle Swarm Optimization (PSO) technique, for structural damage detection and quantification. The robustness and efficiency of the above method has been established after comparing results between the two methods namely Genetic Algorithm (GA), and PSO, considering natural frequencies as response quantities. The performance of these methods has been evaluated for beam and plane frame structures with various damage scenarios. FRF based damage detection technique is employed subsequently along with PSO. It is observed that the use of FRF as response of damaged structure has led to better accuracy, since it contains data related to mode shape in addition to natural frequencies.

A parametric study on fluid–structure interaction problems

Abstract This paper deals with finite element analysis of the fluid–structure systems considering the coupled effect of elastic structure and fluid. The equations of motion of the fluid considered inviscid and compressible are expressed in terms of the pressure variable alone. The elastic structure and the fluid domain are treated as two separate systems and discretized with finite elements. The solution of the coupled system is accomplished by solving the two systems separately with the interaction effects at the fluid–solid interface enforced by a developed iterative scheme. Non-divergent pressure and displacement are obtained simultaneously through a few numbers of iterations. Studies show the accuracy of the proposed algorithm, while comparing with the existing ones available in the literature. The parametric study of the coupled system shows the importance of fluid height and material property of the structure.

A comparative study on crack identification of structures from the changes in natural frequencies using GA and PSO

Purpose – The early detection of cracks, corrosion and structural failure in aging structures is one of the major challenges in the civil, mechanical and aircraft industries. Common inspection techniques are time consuming and hence can have strong economic implications due to downtime. The paper aims to discuss these issues. Design/methodology/approach – As a result, during the past decade a number of methodologies have been proposed for detecting crack in structure based on variations in the structures dynamic characteristics. This work showcases the efficacy of particle swarm optimization (PSO) and genetic algorithm (GA) in damage assessment of structures. Findings – Efficiency of these tools has been tested on structures like beam, plane and space truss. The results show the effectiveness of PSO in crack identification and the possibility of implementing it in a real-time structural health monitoring system for aircraft and civil structures. Originality/value – The methodology presented establishes t...

A novel far-boundary condition for the finite element analysis of infinite reservoir

Abstract The present paper deals with the finite element analysis of the reservoir of infinite extent using a novel far-boundary condition. The equations of motion are expressed in terms of the pressure only assuming water as inviscid and incompressible. The truncation boundary condition is developed numerically from the classical wave equation. Comparative studies show that the proposed far-boundary condition is numerically efficient and accurate over the existing ones, available in the literature. The effect of the geometry of the reservoir bed and the adjacent structure on the development hydrodynamic pressure has been studied. The results show that the geometry of the reservoir bed and as well as the adjacent structure has considerable effect on the development of hydrodynamic pressure at the dam–reservoir interface.

Crack Assessment in Frame Structures Using Modal Data and Unified Particle Swarm Optimization Technique

The present paper deals with the application of unified particle swarm optimization (UPSO) technique for solving crack assessment problems in frame like structures. UPSO is a scheme for improving performance of standard particle swarm optimization (SPSO) by harnessing its exploration and exploitation ability simultaneously. The objective function formulated for crack assessment purpose uses the changes in natural frequencies and mode shapes as the damage indicators. The efficiency of present crack assessment algorithm is demonstrated by conducting several studies. First, a numerical study conducted among several PSO variants, such as global best SPSO, local best SPSO, PSO with constriction factor, PSO with time varying acceleration coefficient and UPSO for accessing their performance in solving inverse problem for crack assessment. Secondly, the performance of UPSO based algorithm is validated with experimental results. Finally, numerical studies are conducted to investigate the performance of UPSO based algorithm in crack assessment using noisy modal data. The results of these studies indicate that the developed method is capable for crack detection and quantification with satisfactory precision.

Vibration-Based Structural Damage Detection Technique using Particle Swarm Optimization with Incremental Swarm Size

A simple and robust methodology is presented to determine the location and amount of crack in beam like structures based on the incremental particle swarm optimization technique. A comparison is made for assessing the performance of standard particle swarm optimization and the incremental particle swarm optimization technique for detecting crack in structural members. The objective function is formulated using the measured natural frequency of the intact structure and the frequency obtained from the finite element simulation. The outcomes of the simulated results demonstrate that the developed method is capable of detecting and estimating the extent of damages with satisfactory precision.