A Stacking Heterogeneous Ensemble Learning Method for the Prediction of Building Construction Project Costs
Source abstract
Abstract
The accurate cost estimation of a construction project in the early stage plays a very important role in successfully completing the project. In the initial stage of construction, when the information necessary to predict construction cost is insufficient, a machine learning model using past data can be an alternative. We suggest a two-level stacking heterogeneous ensemble algorithm combining RF, SVM and CatBoosting. In the step of training the base learner, the optimal hyperparameter values of the base learners were determined using Bayesian optimization with cross-validation. Cost information data disclosed by the Public Procurement Service in South Korea are used to evaluate ML algorithms and the proposed stacking-based ensemble model. According to the analysis results, the two-level stacking ensemble model showed better performance than the individual ensemble models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: BIM and Construction Integration · Infrastructure Maintenance and Monitoring
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Affiliations
Department of Architectural Engineering, Andong National University, Andong 36729, Korea
Department of Architectural Engineering, GyeongSang National University, Jinju 52828, Korea
Funding
Korea Agency for Infrastructure Technology Advancement (KAIA)
References (31)
Show source-provided references
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