Department of Chemistry
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Item Time-delay neural networks in damage detection of railway bridges(Elsiever, 1997-01) Barai, Sudhir KumarThe recent developments in multilayer perceptron using the backpropagation algorithm, has opened up new possibilities in structural identification. Limitation of traditional neural networks (TNN) in dealing with patterns that may vary in time domain has given birth to time-delay neural networks (TDNN). In the present paper the TNN and the TDNN have been implemented in detecting the damage in bridge structure using vibration signature analysis. A comparative study has been carried out for the various cases of complete as well as incomplete measurement data. It has been observed that TDNNs have performed better than TNNs in this application.Item Structural Sensitivity as a Measure of Redundancy(ASCE, 1997) Barai, Sudhir KumarThe conventional definition of redundancy is applicable to skeletal structural systems only, whereas the concept of redundancy has never been discussed in the context of a continuum. Generally, structures in civil engineering constitute a combination of both skeletal and continuum segments. Hence, this paper presents a generalized definition of redundancy that has been defined in terms of structural response sensitivity, which is applicable to both continuum and discrete structures. In contrast to the conventional definition of redundancy, which is assumed to be fixed for a given structure and is believed to be independent of loading and material properties, the new definition would depend on strength and response of the structure at a given stage of its service life. The redundancy measure proposed in this paper is linked to the structural response sensitivities. Thus, the structure can have different degrees of redundancy during its lifetime, depending on the response sensitivity under consideration. It is believed that this new redundancy measure would be more relevant in structural evaluation, damage assessment, and reliability analysis of structures at large.Item Vibration Signature Analysis Using Artificial Neural Networks(ASCE, 1995-10) Barai, Sudhir KumarDamage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.Item Evaluating machine learning models for engineering problems(Elsiever, 1999-07) Barai, Sudhir KumarThe use of machine learning (ML), and in particular, artificial neural networks (ANN), in engineering applications has increased dramatically over the last years. However, by and large, the development of such applications or their report lack proper evaluation. Deficient evaluation practice was observed in the general neural networks community and again in engineering applications through a survey we conducted of articles published in AI in Engineering and elsewhere. This status hinders understanding and prevents progress. This article goal is to remedy this situation. First, several evaluation methods are discussed with their relative qualities. Second, these qualities are illustrated by using the methods to evaluate ANN performance in two engineering problems. Third, a systematic evaluation procedure for ML is discussed. This procedure will lead to better evaluation of studies, and consequently to improved research and practice in the area of ML in engineering applications.Item Multilayer perceptron in damage detection of bridge structures(Elsiever, 1995-02-17) Barai, Sudhir KumarRecent developments in artificial neural networks (ANN) have opened up new possibilities in the domain of structural engineering. For inverse problems like structural identification of large civil engineering structures such as bridges and buildings where the in situ measured data are expected to be imprecise and often incomplete, the ANN holds greater promise. The detection of structural damage and identification of damaged element in a large complex structure is a challenging task indeed. This paper presents an application of multilayer perceptron in the damage detection of steel bridge structures. The issues relating to the design of network and learning paradigm are addressed and network architectures have been developed with reference to trussed bridge structures. The training patterns are generated for multiple damaged zones in a structure and performance of the networks with one and two hidden layers are examined. It has been observed that the performance of the network with two hidden layers was better than that of a single-layer architecture in general. The engineering importance of the whole exercise is demonstrated from the fact that measured input at only a few locations in the structure is needed in the identification process using the ANN.