Least Square Moment Balanced Machine: A New Approach To Estimating Cost To Completion For Construction Projects

Abstract

In the construction industry, traditional methods of cost estimation are inefficient and cannot reflect real-time changes. Modern techniques are essential to create new tools that outperform current cost estimation. This study introduced the Least Square Moment Balanced Machine (LSMBM), AI-based inference engine, to improve construction cost prediction accuracy. LSMBM considers moments to determine the optimal hyperplane and uses the Backpropagation Neural Network (BPNN) to assign weights to each data point. The effectiveness of LSMBM was tested by predicting the construction costs of residential and reinforced concrete buildings. Correlation analysis, PCA, and LASSO were used for feature selection to identify the most relevant variables, with the combination of LSMBM-PCA giving the best performance. When compared to other machine learning models, the LSMBM model achieved the lowest error values, with an RMSE of 0.016, MAE of 0.010, and MAPE of 4.569%. The overall performance measurement reference index (RI) further confirmed the superiority of LSMBM. Furthermore, LSMBM performed better than the Earned Value Management (EVM) method. LSMBM model has proven to enhanced the precision in predicting cost estimates, facilitating project managers to anticipate potential cost overruns and optimize resource allocation, provide information for strategic and operational decision-making processes in construction projects.