Department of Chemistry

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1995 - 199952000 - 200910

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End date: 2009Subject: search.filters.subject.Civil Engineering

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Now showing 1 - 10 of 15
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    A methodology for building neural networks models from empirical engineering data
    (Elsiever, 2000-12) Barai, Sudhir Kumar
    Neural networks (NN) are general tools for modeling functional relationships in engineering. They are used to model the behavior of products and the properties of processes. Nevertheless, their use is often ad hoc. This paper provides a sound basis for using NN as tools for modeling functional relationships implicit in empirical engineering data. First, a clear definition of a modeling task is given, followed by reviewing the theoretical modeling capabilities of NN and NN model estimation. Subsequently, a procedure for using NN in engineering practice is described and illustrated with an example of modeling marine propeller behavior. Particular attention is devoted to better estimation of model quality, insight into the influence of measurement errors on model quality, and the use of advanced methods such as stacked generalization and ensemble modeling to further improve model quality. Using a new method of ensemble of SG(k-NN), one could improve the quality of models even if they are close to being optimal.
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    Influence of specimen geometry on determination of double-K fracture parameters of concrete: a comparative study
    (Springer, 2008-07-03) Barai, Sudhir Kumar
    Comparative study on analytical method, simplified method and weight function approach for determination the double-K fracture parameters using three-point bend and compact tension tests specimen geometries is presented in the paper. The input data required for numerical calculations are obtained using Fictitious Crack Model. The study reports that the double-K fracture parameters computed depends on factors such as initial-notch length/depth ratios, specimen geometry and size-effect. In addition, it is demonstrated that the use of weight function will further improve the computational efficiency without loss of accuracy.
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    Neural Network Models for Air Quality Prediction: A Comparative Study
    (Springer, 2007) Barai, Sudhir Kumar
    The present paper aims to find neural network based air quality predictors, which can work with limited number of data sets and are robust enough to handle data with noise and errors. A number of available variations of neural network models such as Recurrent Network Model (RNM), Change Point detection Model with RNM (CPDM), Sequential Network Construction Model (SNCM), and Self Organizing Feature Maps (SOFM) are implemented for predicting air quality. Developed models are applied to simulate and forecast based on the long-term (annual) and short-term (daily) data. The models, in general, could predict air quality patterns with modest accuracy. However, SOFM model performed extremely well in comparison to other models for predicting long-term (annual) data as well as short-term (daily) data.
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    Time-delay neural networks in damage detection of railway bridges
    (Elsiever, 1997-01) Barai, Sudhir Kumar
    The 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.
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    Structural Sensitivity as a Measure of Redundancy
    (ASCE, 1997) Barai, Sudhir Kumar
    The 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.
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    Determining double-K fracture parameters of concrete for compact tension and wedge splitting tests using weight function
    (Elsiever, 2009-05) Barai, Sudhir Kumar
    The paper presents use of universal form of weight functions for determining the double-K fracture parameters and on compact test and wedge splitting test specimens. The proposed method enables to obtain a closed form expression of cohesion toughness of concrete specimens. A comparison with existing analytical method shows that the weight function method for determination of double-K fracture parameters yields results without any appreciable error. Significant influence of initial notch to depth (a0/D) ratio on the double-K fracture parameters is not also observed. Finally, a possible definition of brittleness of concrete using double-K fracture parameters is proposed.
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    AN INVESTIGATION INTO THE USE OF ICT IN THE NIGERIAN CONSTRUCTION INDUSTRY (2005)
    (2005) Barai, Sudhir Kumar
    The 2002 global ICT rankings by the International Telecommunications Union (ITU) ranked Nigeria 27th among 51 African countries and 153rd among 178 countries in the world. It was against this background that the paper investigated the state of ICT in the Nigerian construction industry to highlight the level of ICT penetration, its impact in the industry and the constraints to its adoption. The study identified the factors significantly impacting the level of ICT use, grouping them into those internal to the industry and those external to it. A total of 136 respondents to a questionnaire survey, comprising, contractors, consultants and academic researchers, provided empirical data for the analysis. The results showed that some internal factors, i.e., the type of business (whether contracting, consulting or academic), chief executive officers (CEOs)/senior managers’ perception of the benefits of ICT and the years of computer literacy of the CEOs/senior managers were significantly correlated with the level of ICT use in the industry. However, none of the external factors were significantly correlated with the level of ICT use. The main uses of ICT in the industry are word processing, Internet communications, costing and work scheduling. The top five constraints to the use of ICT are insufficient/irregular power supply, high cost of ICT software and hardware, low job order for firms, fear of virus attacks and high rate of obsolescence of ICT software and hardware. A comparison with the results of similar studies in some industrialised and newly industrialised countries indicated that the proportion of firms using the computer is quite high for a developing like Nigeria. It also highlighted the large gap in access to electricity and other communications infrastructure between developed and developing countries
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    Studies on recycled aggregates-based concrete
    (Sage, 2006-06-01) Barai, Sudhir Kumar
    Reduced extraction of raw materials, reduced transportation cost, improved profits, reduced environmental impact and fast-depleting reserves of conventional natural aggregates has necessitated the use of recycling, in order to be able to conserve conventional natural aggregate. In this study various physical and mechanical properties of recycled concrete aggregates were examined. Recycled concrete aggregates are different from natural aggregates and concrete made from them has specific properties. The percentages of recycled concrete aggregates were varied and it was observed that properties such as compressive strength showed a decrease of up to 10% as the percentage of recycled concrete aggregates increased. Water absorption of recycled aggregates was found to be greater than natural aggregates, and this needs to be compensated during mix design.
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    Vibration Signature Analysis Using Artificial Neural Networks
    (ASCE, 1995-10) Barai, Sudhir Kumar
    Damage 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.
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    Evaluating machine learning models for engineering problems
    (Elsiever, 1999-07) Barai, Sudhir Kumar
    The 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.