A Review and Prospect of the Application of Machine Learning in Construction Project Cost Estimation
Cost estimation plays a critical role in feasibility studies and decision-making processes during the early stages of construction projects.Given the limited availability of data in early cost estimations,machine learning(ML)techniques have emerged as effective tools to address these challenges.This paper systematically reviews the application of five major machine learning methods in construction cost estimation:Multiple Linear Regression(MLR),Support Vector Machines(SVM),Artificial Neural Networks(ANN),Case-Based Reasoning(CBR),and Ensemble Learning Models.Through an analysis of their performance and improvements,the findings reveal that these machine learning techniques significantly enhance both the accuracy and stability of cost predictions.Moreover,the paper offers insights into future research trends,focusing on refining algorithms to handle the inherent uncertainties in early project phases and improving the interpretability of the models.it aims to provide valuable guidance for project stakeholders and decision-makers to optimize their investment strategies and risk management in construction projects.
NETL
NSTL
万方数据
