Department of Chemistry
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Item A methodology for building neural networks models from empirical engineering data(Elsiever, 2000-12) Barai, Sudhir KumarNeural 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.Item Influence of specimen geometry on determination of double-K fracture parameters of concrete: a comparative study(Springer, 2008-07-03) Barai, Sudhir KumarComparative 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.Item Neural Network Models for Air Quality Prediction: A Comparative Study(Springer, 2007) Barai, Sudhir KumarThe 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.Item Recycled aggregate concrete: Particle Packing Method (PPM) of mix design approach(Elsiever, 2017-10) Barai, Sudhir KumarA sustainable and eco-friendly approach is essential for the construction industry, as it is one of the major sectors responsible for the depletion of the natural resources and the generation of greenhouse gases. In this context, the recycled aggregate (RA) is an effective alternative to natural aggregate. But, the use of RA has not gained popularity yet, because of the inferior quality of RA and yielded recycled aggregate concrete (RAC) using RA. The proposed Particle Packing Method (PPM) of design mix is executed along with the established Two Stage Mixing Approach (TSMA) to produce RAC by completely replacing the natural coarse aggregate. The synergistic effect of PPM design mix and TSMA on fresh and hardened stage performance of RAC were studied. In this context, a comparative analysis showed encouraging results for the PPM design mix as compared to the IS: 10262 (2009) method of mix design approach.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 Data mining applications in transportation engineering(Taylor & Francis, 2011-12-19) Barai, Sudhir KumarData mining is the extraction of implicit, previously unknown and potentially useful information from data. In recent time, data mining studies have been carried out in many engineering disciplines. In this paper the background of data mining and tools is introduced. Further applications of data mining to transportation engineering problems are reviewed. The application of data mining for typical example of ‘Vehicle Crash Study’ is demonstrated using commercially available data mining tool. The paper highlights the potential of data mining tool application in transportation engineering sector.Item Determining the double-K fracture parameters for three-point bending notched concrete beams using weight function(Wiley, 2010-06-16) Barai, Sudhir KumarParameters of universal form of weight functions having four terms and five terms are derived for edge cracks in finite width of plate. The standard Tada Green's function is taken as the basis for the derivation. The shape of universal form of weight functions considered enables closed form expressions for cohesive toughness of three-point bending test geometry of notched concrete beams due to linear cohesive stress distribution in the fictitious fracture zone. This solution provides a viable method to determine the double-K fracture parameters: the initiation toughness, inline image and the unstable toughness inline image for mode I fracture of concrete beam. A comparison with existing analytical method shows that the weight function method for determination of the double-K fracture parameters yields results without any appreciable error. The use of weight function will not only simplify the calculation to obtain the double-K fracture parameters, inline image and inline image but also it will avoid the need of skilled numerical integration technique due to singularity problem at the integral boundary.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 Determining double-K fracture parameters of concrete for compact tension and wedge splitting tests using weight function(Elsiever, 2009-05) Barai, Sudhir KumarThe 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.Item Influence of incorporation of nano-silica and recycled aggregates on compressive strength and microstructure of concrete(Elsiever, 2014-11-30) Barai, Sudhir KumarThe present investigation deals with the study of compressive strength and characteristics of the Interfacial Transition Zone (ITZ) of concrete containing recycled aggregates and nano-silica. For this purpose, compressive strength at 7, 28, 90 and 365 days are determined for fully natural and recycled aggregate concrete mixes made with or without nano-silica. In addition to above, Vickers microhardness test and backscattered-mode scanning electron microscopic analysis is carried to characterize ITZ of concrete mixes. The results of study depict that full replacement of natural coarse aggregates with recycled ones have significant effect on compressive strength and ITZ characteristics of concrete. However, compressive strength and microstructure of concrete mixes improves with the incorporation of nano-silica.