A novel construction cost prediction model using hybrid natural and light gradient boosting. (October 2020)

• Record Type: Journal Article
• Authors: Chakraborty, Debaditya; Elhegazy, Hosam; Elzarka, Hazem; Gutierrez, Lilianna
• Is Part Of: Advanced engineering informatics. Volume 46(2020)
• Publication Date: 2020-10
• Subjects: Construction cost -- Value engineering -- Construction estimation -- Machine learning -- Gradient boosting -- Hybrid modeling -- Game theory; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101201
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: In this paper, we compared the predictive capabilities of six different machine learning algorithms – linear regression, artificial neural network, random forest, extreme gradient boosting, light gradient boosting, and natural gradient boosting – and demonstrated that a hybrid light gradient boosting and natural gradient boosting model provides the most desirable construction cost estimates in terms of the accuracy metrics, uncertainty estimates, and training speed. We also present a game theory-based model interpretation technique to evaluate the average marginal contribution of each feature value, across all possible combinations of features, on the model predictions. The comparison between the predicted cost and the actual cost confirms good alignment with R 2 ∼ 0.99, R M S E ∼ 0.5, and M B E ∼ -0.009. Besides, the proposed hybrid model can provide uncertainty estimates through probabilistic predictions for real-valued outputs. This probabilistic prediction approach produces a holistic probability distribution over the entire outcome space to quantify the uncertainties related to construction cost predictions.