Investment estimation of prefabricated concrete buildings based on XGBoost machine learning algorithm

The prefabricated concrete buildings (PCBs)are the booster in the process of construction industrialization and intelligent upgrading. However, its high cost has become one of the restricting factors of further application and promotion of prefabricated concrete buildings. Moreover, the existing investment estimation methods of prefabricated concrete buildings have limited predicting accuracy as well as the ability of adapting dynamic factors. Therefore, to achieve more reliable and reasonable investment estimation of prefabricated concrete buildings, this paper has proposed an investment estimation model of prefabricated concrete buildings based on XGBoost machine learning algorithm. In the proposed model, the construction project cost-significance theory (CS) and analytic hierarchy process (AHP) were used to extract the construction characteristic indices of prefabricated concrete buildings investment estimation. Then the XGBoost machine learning algorithm was implemented to build an investment estimation model of prefabricated concrete buildings that was able to quantify the uncertainty of the confidence and prediction, and to enhance the interpretability of the model. The research conducted in this paper showed that when compared with traditional machine learning methods such as Support vector machine (SVM), Back Propagation Neural Network (BPNN) and Random Forest (RF), XGBoost had better generalization and interpretable ability. The discussion provided in this paper further demonstrated the reliability and feasibility of the proposed model, and provided reliable basis for the investment decision-making of prefabricated concrete building projects.

Automated summary

This paper proposes an investment estimation model of prefabricated concrete buildings based on the XGBoost machine learning algorithm, which extracts construction characteristic indices and quantifies uncertainty. Compared with traditional machine learning methods, this model has better generalization and interpretability.