Dynamic feature selection for accurately predicting construction productivity using symbiotic organisms search-optimized least square support vector machine

Productivity is one of the crucial elements for managing construction operations effectively which directly impacts on general cost and time of a project. The accurate prediction of productivity is thus of paramount importance to help construction manager give decision-making timely for avoiding cost overrun and project falling behind schedule. This study introduces an artificial intelligence (AI)-based inference model to accurately forecast productivity of a construction project. The model is a hybridization of least square support vector machine (LSSVM), symbiotic organisms search (SOS), and a feature selection (FS) method in which SOS proceeds the optimization process to achieve the greatest performance of LSSVM by simultaneously determining hyperparameter of LSSVM model and set of highly relevant attributes of construction productivity. The performance of the proposed model, SOS-LSSVM-FS, is validated based on productivity dataset of two real projects located in Montreal, Canada constructed from September 2001 to June 2004. The statistical results of a 10-fold cross validation method indicate that SOS-LSSVM-FS achieves the highest accuracy of productivity prediction with 3.67% mean absolute percentage error (MAPE) which is at least 19.6% better than that of other comparative AI models. In addition, with the support of SOS, the model can run without human intervention of trial-and-error to tune control parameters. Therefore, this study contributes to core body of knowledge a novel model to deal with construction productivity-related problem. The SOS-LSSVM-FS model is strongly recommended as a promising tool for helping construction manager manage/control site productivity.