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

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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>D</mi><mi>s</mi></mrow></semantics></math></inline-formula>) as a contractual provision are vital in construction agreements. Accurate quantification of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>D</mi><mi>s</mi></mrow></semantics></math></inline-formula> 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>M</mi><mi>L</mi><mi>T</mi></mrow></semantics></math></inline-formula>) that combines algorithms of the Extreme Gradient Boosting (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>X</mi><mi>G</mi><mi>B</mi><mi>o</mi><mi>o</mi><mi>s</mi><mi>t</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, Categorical Boosting (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>a</mi><mi>t</mi><mi>B</mi><mi>o</mi><mi>o</mi><mi>s</mi><mi>t</mi></mrow></semantics></math></inline-formula>), k-Nearest Neighbor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mi>N</mi><mi>N</mi></mrow></semantics></math></inline-formula>), Light Gradient Boosting Machine (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>i</mi><mi>g</mi><mi>h</mi><mi>t</mi><mi>G</mi><mi>B</mi><mi>M</mi></mrow></semantics></math></inline-formula>), Artificial Neural Network (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>N</mi><mi>N</mi></mrow></semantics></math></inline-formula>), and Decision Tree (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>T</mi></mrow></semantics></math></inline-formula>) for the prediction of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>D</mi><mi>s</mi></mrow></semantics></math></inline-formula> 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>), Mean Absolute Error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>E</mi></mrow></semantics></math></inline-formula>), Mean Absolute Percentage Error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>P</mi><mi>E</mi></mrow></semantics></math></inline-formula>), and Coefficient of Determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>) were used to assess and evaluate the accuracy of the implemented machine learning (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>L</mi></mrow></semantics></math></inline-formula>) 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>L</mi><mi>D</mi><mi>s</mi></mrow></semantics></math></inline-formula>. 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.