PRODUCTIVITY ESTIMATION MODEL FOR BRACKLAYER IN CONSTRUCTION PROJECTS USING NEURAL NETWORK
AbstractConstruction labour productivity is a major determinant of success of a construction project. Time and cost overruns of construction projects are widely attributed to poor productivity of construction labour force. Though considerable research exists on productivity factors in other countries, very little studies have addressed productivity issues in Iraq. Brainstorming session and site interview survey was conducted in Karbala province in Iraq, to identify the productivity and the factors affecting bricklayer labor productivity. Thirteen influencing factors are utilized for productivity forecasting by artificial neural network (ANN) model, and they include Age, Experience, Gang health, Gang Number, Weather, wages, Site condition, Material availability, Wall length, Wall thickness, Wall height, Mortar type, and Security in site. One ANN prediction model was built for the productivity of bricklayer labors. It was found that the predict productivity approximately the same as the actual productivity with a good degree of accuracy of the coefficient of correlation (R=86.28%), and mean square error (MSE) of (1.32%) after testing the network. The developed ANN model can be used dependably for estimating production rates of bricklayer for any building construction project by incorporating the influence of selected factors.
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