Hybrid pso-ann and pso models based approach for estimation of cost and duration of construction projects

Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Artificial neural network (ANN) and particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approaches to the prediction of construction costs and duration of construction projects, which is based on hybrid PSO-ANN and PSO approaches. The general applicability of these teqchniques in the formulated problem with cost and duration estimation are examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the proposed models can be an alternative approaches to evaluate the cost and-or duration of construction projects. The developed PSO models provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values, as compared with hybrid PSO-ANN models. The results display that utilizing these models to forecast the cost and duration are practical and helpful for estimators in construction sector. Key Words: Construction projects, Artificial neural network, Particle swarm optimization, Cost and Duration. 2020, 155 Pages Science Code: 90