Articlegold2022

Prediction Liquidated Damages via Ensemble Machine Learning Model: Towards Sustainable Highway Construction Projects

Odey Alshboul · Ali Shehadeh · Rabia Emhamed Al Mamlook · Ghassan Almasabha · Ali Saeed Almuflih · Saleh Yahya Alghamdi

42 citations0/0 files storedSustainability

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Construction Project Management and Performance BIM and Construction Integration Forecasting Techniques and Applications

Abstract

Highway construction projects are important for financial and social development in the United States. Such types of construction are usually accompanied by construction delay, causing liquidated damages (LDs) as a contractual provision are vital in construction agreements. Accurate quantification of LDs is essential for contract parties to avoid legal disputes and unfair provisions due to the lack of appropriate documentation. This paper effort sought to develop an ensemble machine learning technique (EMLT) that combines algorithms of the Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), k-Nearest Neighbor (kNN), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN), and Decision Tree (DT) for the prediction of LDs in highway construction projects. Key attributes are identified and examined to predict the interrelated correlations among the influential features to develop accurate forecast models to assess the impact of each delay factor. Various machine-learning-based models were developed, where the different modeling outputs were analyzed and compared. Four performance matrices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) were used to assess and evaluate the accuracy of the implemented machine learning (ML) algorithms. The prediction outputs implied that the developed EMLT model has shown better performance compared to other ML-based models, where it has the highest accuracy of 0.997, compared to the DT, kNN, CatBoost, XGBoost, LightGBM, and ANN with an accuracy of 0.989, 0.988, 0.986, 0.975, 0.873, and 0.689, respectively. Thus, the findings of this research designate that the EMLT model can be used as an effective administrative decision adding tool for forecasting the LDs. As a result, this paper emphasizes ML’s potential to aid in the advancement of computerization as a comprehensible subject of investigation within highway building projects.

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 2007Managing public–private megaprojects: Paradoxes, complexity, and project design 2021Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression 2005Programmatic Cost Risk Analysis for Highway Megaprojects 2017Construction Project Complexity: Research Trends and Implications 2022Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction 2017Revisiting Quality Failure Costs in Construction 2010Optimizing Material Procurement and Storage on Construction Sites

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