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A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis
The precise prediction of construction costs during the initial phase of a construction project is crucial for ensuring the project’s success. Identifying the parameters that influence project cost contributes to achieving accurate results and improves the overall accuracy of cost estimation. This study applied three machine learning methods such as artificial neural network (ANN), natural gradient boosting (NGBoost), and linear regression (LR) models, to predict the total cost of construction. The NGBoost model was employed for construction cost estimation and was compared with two machine learning algorithms: artificial neural network and linear regression. Evaluation metrics, including Mean Absolute Error (MAE), Coefficient of efficiency (CE), Mean Absolute Percentage Error (MAPE), index of agreement (d), and coefficient of determination (R2), are employed to assess and compare the accuracy of the developed algorithms. Statistical indicators revealed that the NGBoost algorithm outperformed others, displaying the highest coefficient of determination (R2 = 0.992 for training and R2 = 0.985 for testing) and the lowest root mean square error (RMSE = 0.5136 for training and RMSE = 0.3702). Moreover, sensitivity analysis revealed that the input parameter with the highest contribution was formwork, accounting for nearly 41%. On the other hand, the superimposed load had the lowest contribution, totaling 5%. The Shapley Additive Explanation (SHAP) method was employed to elucidate the importance and contribution of input variables influencing construction costs. The findings of this study offer valuable insights for project stakeholders, enabling them to minimize errors in estimated costs and make informed decisions early in the construction process.
A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis
The precise prediction of construction costs during the initial phase of a construction project is crucial for ensuring the project’s success. Identifying the parameters that influence project cost contributes to achieving accurate results and improves the overall accuracy of cost estimation. This study applied three machine learning methods such as artificial neural network (ANN), natural gradient boosting (NGBoost), and linear regression (LR) models, to predict the total cost of construction. The NGBoost model was employed for construction cost estimation and was compared with two machine learning algorithms: artificial neural network and linear regression. Evaluation metrics, including Mean Absolute Error (MAE), Coefficient of efficiency (CE), Mean Absolute Percentage Error (MAPE), index of agreement (d), and coefficient of determination (R2), are employed to assess and compare the accuracy of the developed algorithms. Statistical indicators revealed that the NGBoost algorithm outperformed others, displaying the highest coefficient of determination (R2 = 0.992 for training and R2 = 0.985 for testing) and the lowest root mean square error (RMSE = 0.5136 for training and RMSE = 0.3702). Moreover, sensitivity analysis revealed that the input parameter with the highest contribution was formwork, accounting for nearly 41%. On the other hand, the superimposed load had the lowest contribution, totaling 5%. The Shapley Additive Explanation (SHAP) method was employed to elucidate the importance and contribution of input variables influencing construction costs. The findings of this study offer valuable insights for project stakeholders, enabling them to minimize errors in estimated costs and make informed decisions early in the construction process.
A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis
Asian J Civ Eng
Das, Pobithra (author) / Kashem, Abul (author) / Hasan, Imrul (author) / Islam, Mominul (author)
Asian Journal of Civil Engineering ; 25 ; 3301-3316
2024-06-01
16 pages
Article (Journal)
Electronic Resource
English
| Taylor & Francis Verlag | 2024
| Elsevier | 2024