Predicting construction labor productivity using lower upper decomposition radial base function neural network

Construction labor productivity is affected by many factors such as scope changes, weather conditions, managerial policies, and operational variables. Labor productivity is critical in project development. Its modeling, however, can be a very complex task for it requires consideration of the factors stated above. In this article, a novel methodology is proposed for quantifying the impact of multiple factors on productivity. The data used in the present study was prepared using data processing techniques and was subsequently used in the development of a predictive model for labor productivity utilizing radial basis function neural network. The model focuses on labor productivity in a formwork installation using data gathered from two high-rise buildings in the downtown area of Montreal, Canada. The predictive capability of the developed model is then compared with other techniques including adaptive neuro-fuzzy inference system, artificial neural network, radial basis function (RBF), and generalized regression neural network. The results show that LU-RBF predicts productivity more accurately and thus can be utilized members of project teams to validate the estimated productivity based on available data. The advantages and limitations of the proposed model are discussed in this article.