Construction Site Layout Planning (CSLP) comprises determining, sizing and placing of the temporary facilities within the boundaries of a construction site by considering many factors. Traveling distance between facilities and safety risks are two essential factors that need to be minimized while planning site layout of a construction project. Many studies treated CSLP as a single objective optimization problem. They have mainly focused on either diminishing the travel cost of resources on site without considering the safety aspect or vice versa. While a few of the studies have treated the problem as a multi-objective optimization problem, none of them included a risk assessment approach including crane-related constraints. Hence, a user-friendly CSLP model that includes a risk assessment approach for safety constraints is proposed by using a Multi-Objective Particle Swarm Optimization algorithm based on Pareto dominance approach to minimize both the construction safety risks of crane operated projects and the total traveling distance of the resources between temporary facilities.
Construction crew productivity prediction is one of the most important issues that affect the realistic prediction of construction duration and cost. Use of different search algorithms like Feed Forward Neural Network, Ant Colony, Artificial Bee Colony, Particle Swarm Optimization, Radial Based Neural Networks and Self Organizing Maps for crew productivity prediction problem have been discussed in previous studies. However, the significant effect of the coherence between the nature of the data and the characteristics of the method used in prediction performance has generally been neglected. The aim of the current research thus has been to analyse the prediction performance of two contemporary learning algorithms; K-Nearest Neighbour (K-NN) and Generalized Neural Network (GRNN) when applied to three different crew (formwork, tiling and masonry) productivity related data sets with different distribution characteristics. Performance of both methods varied with the changing coefficient of variation values. K-NN outperformed GRNN for all data sets and both of the methods had their worst performance on the dataset with the highest variance.
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