Florez-Perez, L; Song, Z; Cortissoz, JC; (2022) Predicting construction productivity with machine learning approaches. In: Proceedings of the International Symposium on Automation and Robotics in Construction. (pp. pp. 107-114). The International Association for Automation and Robotics in Construction: Bogota, Colombia.

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Abstract

Machine learning (ML) is a purpose technology already starting to transform the global economy and has the potential to transform the construction industry with the use of data-driven solutions to improve the way projects are delivered. Unrealistic productivity predictions cause increased delivery cost and time. This study shows the application of supervised ML algorithms on a database including 1,977 productivity measures that were used to train, test, and validate the approach. Deep neural network (DNN), k-nearest neighbours (KNN), support vector machine (SVM), logistic regression, and Bayesian networks are used for predicting productivity by using a subjective measure (compatibility of personality), together with external and site conditions and other workforce characteristics. A case study of a masonry project is discussed to analyse and predict task productivity.

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