C. Antony Jeyasehar

Multi-stage approach for structural damage identification using modal strain energy and evolutionary optimization techniques:

Deterioration and degradation of aging structures is a major concern worldwide. It is often necessary to evaluate the integrity of such structural systems. Early detection and eventual quantification of damage are important for improved safety, to prevent potential catastrophic events, and to extend the service life by repairing/retrofitting the components of the structure. Different methodologies have been proposed in the literature for the identification and localization of damage based on optimization techniques and modal-based approaches. The main drawback in using the optimization approach based on evolutionary algorithms is that it requires the evaluation of the objective function for the total population in each generation. As this is computationally intensive, in this study, a multi-stage approach has been proposed. In this, at first, localization of the damage was achieved so as to reduce the number of parameters of the objective function in the optimization approach. These identified damaged elements were analyzed further for exact identification and quantification of the damage using genetic algorithm (GA)-based optimization approach. To demonstrate the efficiency of the proposed hybrid approach, numerical studies have been carried out on selected structures. The approach of using modal strain energy change ratio to identify damage at first-stage identification is found to be very useful in reducing the objective function parameters in the optimization method. This multi-stage approach is found to be very efficient in the exact identification and quantification of damage in structures. The proposed approach could be used for identifying damage in large-scale structures.

Nondestructive Evaluation of Prestressed Concrete Beams using an Artificial Neural Network (ANN) Approach

An artificial neural network (ANN) based approach for the assessment of damage in prestressed concrete (PSC) beams using its present stiffness and natural frequency as the test inputs to the ANN has been proposed. The details of the extensive experimental programme designed and executed in this study to induce the known extent of damage in the PSC beams by a method that resembles natural damage processing techniques and to generate the training and test data for the ANN used to model damage levels have been presented. It has been demonstrated that it is possible to assess the damage with reasonable accuracy by the ANN learning by a back propagation algorithm and stiffness and natural frequency as test inputs. The efficiency of this damage assessment algorithm has been studied by testing this ANN with the test data available in the literature. The results indicate that this approach can be used as a cost effective and simple structural health monitoring tool for PSC beams since this procedure needs only limited nondestructive static and dynamic measurements on the structure under study.

COMPUTATIONAL METHODOLOGIES FOR VIBRATION-BASED DAMAGE ASSESSMENT OF STRUCTURES

In the present work, computational methodologies based on artificial neural networks and genetic algorithms (GA) have been developed for identification of structural damage utilizing vibration data. The natural frequencies and mode shapes obtained from the finite element analysis for the first few modes have been considered for this purpose. A multi-stage hybrid methodology combining the modal strain energy criteria with GA has also been proposed, which showed improved damage identification capability as compared to the conventional GA, and proved to be computationally efficient. To demonstrate the efficiency of the proposed hybrid approach, numerical studies have been carried out on the truss structure. The efficacy of mode shape expansion in conjunction with GA is demonstrated for damage identification of reinforced concrete beam based on experimental modal data.

A new procedure for damage assessment of prestressed concrete beams using artificial neural network

A damage assessment procedure has been developed using artificial neural network (ANN) for prestressed concrete beams. The methodology had been formulated using the results obtained from an experimental study conducted in the laboratory. Prestressed concrete (PSC) rectangular beams were cast, and pitting corrosion was introduced in the prestressing wires and was allowed to be snapped using accelerated corrosion process. Both static and dynamic tests were conducted to study the behaviour of perfect and damaged beams. The measured output from both static and dynamic tests was taken as input to train the neural network. Back propagation network was chosen for this purpose, which was written using the programming package MATLAB. The trained network was tested using separate test data obtained from the tests. A damage assessment procedure was developed using the trained network, it was validated using the data available in literature, and the outcome is presented in this paper.

Damage identification based on vibration data

Structural damage identification, localisation and quantification is receiving attention world over. Developments in sensor technology, modal testing brought out immense technical advancements in modal-based damage detection methods. Advances in modal-based damage detection methods over the last few decades produced new techniques for examining vibration data for identification of structural damage. In this paper, studies carried out using different approaches based on traditional model and advanced non-modelbased methods, for damage identification have been presented. The damage identification studies are carried out based on the dynamic properties of the structure such as frequencies, mode shapes and their derivatives and also using advanced techniques such as neural networks and genetic algorithms. The effectiveness of these methods and usefulness of different parameters such as change in frequency, mode shape, modal curvature and strain energy for identification, localisation and quantification of damage are presented in the paper.