Investment estimation of prefabricated concrete buildings based on XGBoost machine learning algorithm |
| |
| Affiliation: | 1. Central South University, Shaoshan South Road, Changsha, Hunan 410075, China;2. Hunan University of Finance and Economics, School of Construction Management, Changsha, Hunan 410205, China;3. Aoyuan Yuekang Corporation Group, President s Office, Guangzhou, Guangdong 511445, China;1. School of Civil Engineering, Tsinghua University, Beijing, China;2. Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong;1. School of Management, Northwestern Polytechnical University, Xi’an, PR China;2. Mechanical Engineering and Design Department, Université de Bourgogne Franche-Comté, Université de technologie de Belfort-Montbéliard, Belfort Cedex, France;3. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, PR China;4. Guangdong Provincial Key Laboratory of Advanced Welding Technology for Ships, CSSC Huangpu Wenchong Shipbuilding Company Limited, Guangzhou, PR China;1. School of Mechanical Engineering, University of Shanghai for Science and Technology, China;2. School of Mechanical Engineering, Jiangsu University, China;3. College of Engineering, Coventry University, Coventry, UK;4. School of Physics, Engineering & Computer Science, University of Hertfordshire, UK;1. School of Civil Engineering, Southeast University, Nanjing 211189, China;2. Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong SAR, China;3. Department of Civil Engineering, University of Hong Kong, Hong Kong SAR, China |
| |
| Abstract: | 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. |
| |
| Keywords: | Prefabricated Concrete Building Investment Estimation XGBoost |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
