Developing a machine learning model to predict the construction duration of tall building projects

Abstract

The construction industry is witnessing a rapid rise in tall building projects due to an anticipated urban population explosion. However, this building typology has been subject to time overruns and total abandonment due to an underestimation of the project duration. Consequently, this paper presents the development of a model to predict the construction duration of tall building projects. In developing the model, a suite of machine learning algorithms were adopted including Multi-Linear Regression Analysis (MLRA), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble Methods. Thus, twelve models were developed in the process, and the most efficient model was selected. The procedure described in this study presents researchers and practitioners with a strategy to enhance the time performance of tall building projects through the adoption of modern digital technologies such as machine learning. The proposed model was based on an ensemble method using ANN as the combiner, with a Correlation Coefficient (R2) of 0.69, Root Mean Squared Error (RMSE) of 301.72 and Mean Absolute Percentage Error (MAPE) of 18%.