Civil Engineering

In this paper, multispecies solute transport has been studied using numerical modelling. Certain kinds of solute like perchloroethylene (PCE) have the property to disintegrate to other toxic contaminants. Transport phenomenon of such kind of solute along with their transformed contaminants is studied. Implicit finite difference numerical technique has been used to solve the coupled governing equations. Effect of distance-dependent dispersion has been analyzed using constant, linear and exponential dispersivity function. This study has been demonstrated for three species transport problem. Advective and dispersive term are discretized using upwind and central difference scheme respectively. Effect of linear sorption for first species has been seen through. Sensitivity analysis has been conducted to study the effect of different dispersivity-distance ratio. Finally, the relationship between relative concentration and pore volume is studied for reactive solute. It is concluded from the results that solute and its transformed species do not have same transport pattern. So, this kind of study becomes important as it is not necessary that a particular solute may eliminate at a particular location or after a particular time. Its transformed products can still cause pollution. This study also finds application where aerobic and anaerobic degradation can be implemented inspite of traditional pump and treat remediation method.

The rapid growth of the Indian construction sector over the last few decades and recurring failure in on-time delivery highlight the need for a systematic analysis of the factors influencing delay. A theoretical structural equation model representing the influence of four key latent variables on project delays in the Indian construction industry has been developed. Data collected from a questionnaire

Build-operate-transfer (BOT) has been one of the preferred models of public–private partnership (PPP) for attracting private capital in the Indian highway sector. In BOT projects the traffic demand is considered a critical risk. Through a concession agreement called the model concession agreement (MCA) a standardized risk allocation framework has been adopted for BOT highway projects in India. If the actual

Among many optimisation techniques, Simulated Annealing (SA) is an approach closely related to Genetic Algorithms (GA). While SA and GA have been compared, their performance in Building Envelope Optimisation has not been explored. The objective of this research is to evaluate performance of SA and GA approach in building envelope optimisation. The outcome of study indicates that although GA and SA have comparable performance they have distinct advantages. While GA outperforms SA in initial convergence, it may take longer to reach the optimal. Results also indicate that, near already known good solutions, the SA approach performs better in comparison. There is potential of developing a hybrid optimisation technique that utilizes the strengths of both the GA and SA approaches.

Optimal design of building envelope involves multiple decision parameters and computationally expensive methods for thermal load calculation. Although popular in general design optimisation, there are few instances of adoption of Simulated Annealing (SA) approach in optimisation process of building design. The objective of research is to develop computationally inexpensive methodology in building envelope optimisation using SA approach. This paper presents successful utilisation of SA approach in optimising building envelope for multiple decision parameters. The outcome of study indicates that optimisation utilising SA approach is sensitive to starting location, stopping criterion and database organization. Further, coupled with designer's intuition this approach may provide quicker results.

Optimisation of building envelope for thermal performance is a complex problem involving a multitude of variables. While the combinations of variables lead to a vast dataset of discrete points forming the feasibility domain, the optimal solutions are only few and difficult to discern. The objective is to develop a methodology using a hybrid approach involving both Genetic Algorithm (GA) and Simulated Annealing (SA) approach to optimise the building envelope with respect to thermal performance. The GA and SA are complementary algorithms. The methodology uses the advantages of both these methodologies to improve the solution. The outcome indicates that Hybrid (GASA) approach improves the reliability of solution. While there is minor improvement in solution quality Hybrid (GASA) approach achieves the optimal frequently.