Machine learning models for estimating preliminary factory construction cost: case study in Southern Vietnam

Abstract

Construction of industrial enterprises has become more necessary in recent years. It is critical for project managers to estimate the entire cost of a building project at this early stage. Existing approaches that use operator experience as a mathematical formula. Initial estimates are inaccurate due to the lack of available data points, which leads to overruns in project costs. This research utilizes different machine learning techniques to predict preliminary factory construction cost. Five popular numeric predictive techniques: support vector machine (SVM), artificial neural network (ANN), generalized linear regression (GENLIN), classification and regression-based techniques (CART), exhaustive chi-squared automatic interaction detection (CHAID) are used for baseline and ensemble models. A deep learning neural network (DLNN) is also utilized in this study. The machine learning model is trained and tested on actual data gathered in the southern part of Vietnam. Deep learning outperforms all other machine learning algorithms in this comparison, while the ensemble model of artificial neural networks and generalised linear regression also fared well. Cost estimators can quickly pick the best model for projecting the cost of constructing a preliminary factory by having access to a variety of estimate methodologies.