Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction

Odey Alshboul, Ali Shehadeh, Ghassan Almasabha and Ali Saeed Almuflih
Additional contact information
Odey Alshboul: Department of Civil Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Ali Shehadeh: Department of Civil Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Shafiq Irshidatst, Irbid 21163, Jordan
Ghassan Almasabha: Department of Civil Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Ali Saeed Almuflih: Department of Industrial Engineering, King Khalid University, King Fahad St., Guraiger, Abha 62529, Saudi Arabia

Sustainability, 2022, vol. 14, issue 11, 1-20

Abstract: Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and R 2 ) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry.

Keywords: green buildings; cost prediction; machine learning; extreme gradient boosting (XGBOOST); deep neural network (DNN); random forest (RF) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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