Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Use of municipal solid waste ash in concrete.

Abstract: Because of exponential growing in urbanization and industrialization, the amount of municipal solid wastes (MSWs) has increased very rapidly. The disposal of municipal solid waste (MSW) is becoming an increasing concern for many urban municipalities because of the increasing volume of solid waste generated, the spiraling costs of operating landfills, and the scarcity of landfill sites. With increased environmental awareness and its potential hazardous effects, utilization of these materials has become an attractive alternative to disposal. Ash form MSW could possibly be used in concrete manufacturing. This paper details about the physical, chemical, and mineralogical composition, and elemental analysis of MSW ash. It also covers the effect of MSW ash on the compressive strength, chloride resistance, and shrinkage of concrete. It also deals with the leachate analysis of MSW ash.

Use of cement kiln dust in cement concrete and its leachate characteristics

Abstract: Due to continuous increase in industrial globalization and generation of waste, solid waste management has become one of the major global environmental issue. Cement kiln dust (CKD) is one of such industrial waste or by product which is progressively significant environmental concern related to its emission and disposal. CKD is fine grained, solid, highly alkaline particulate material chiefly composed of oxidized, anhydrous, micron-sized particles collected from electrostatic precipitators during the production of cement clinker. Cement kiln dust so generated is partly reused in cement plants and landfilled. Due to lack of landfilling space and ever increasing disposal cost, utilization of CKD in highway uses, waste treatment, soil stabilization, cement mortar/concrete, CLSM, etc. has become an attractive alternative to its disposal. The leachate obtained from cement kiln dust may contain hazardous compounds and its caustic nature poses harmful effects to the environment. So, it is essential to know the characteristics of leachate obtained from CKD for beneficial utilization towards solid waste management. Several studies have shown that CKD could be used in making cement paste/mortar/concrete. This paper presents the overview of some of the research published on the beneficial use of CKD in construction materials, reducing carbon dioxide emissions and CKD leachate characteristics. Effect of CKD on the cement paste/mortar/concrete properties like compressive strength, tensile strength, durability, hydration, electrical conductivity, etc. and leachate test methods and leachate characteristics of cement kiln dust is also discussed in this paper.

Vehicular traffic noise modeling using artificial neural network approach

Abstract: In India, the transportation sector is growing rapidly and the number of vehicles on Indian roads is increasing at a very fast rate leading to overcrowded roads and noise pollution. The traffic scenario is typically different from other countries due to predominance of a variety of two-wheelers which has doubled in the last decade and forms a major chunk of heterogeneous volume of vehicles. Also tendency of not following the traffic norms and poor maintenance adds to the noise generation. In the present study, Multilayer feed forward back propagation (BP) neural network has been trained by Levenberg–Marquardt (L–M) algorithm to develop an Artificial Neural Network (ANN) model for predicting highway traffic noise. The developed ANN model is used to predict 10 Percentile exceeded sound level (L10) and Equivalent continuous sound level (Leq) in dB (A). The model input parameters are total vehicle volume/hour, percentage of heavy vehicles and average vehicle speed. The predicted highway noise descriptors, Leq and L10 from ANN approach and regression analysis have also been compared with the field measurement. The results show that the percentage difference is much less using ANN approach as compared to regression analysis. Further goodness-of-fit of the models against field data has been checked by statistical t-test at 5% significance level and proved the Artificial Neural Network (ANN) approach as a powerful technique for traffic noise modeling.