Search

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.

Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and R2) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry.

Sustainable construction projects are essential for economic and societal thriving in modern communities. However, infrastructural construction is usually accompanied by delays in project delivery, which impact sustainability. Such delays adversely affect project time, cost, quality, safety objective functions, and associated Liquidated Damages (LDs). LDs are monetary charges to recompense the owner for additional expenses sustained if the project was not delivered on time due to delays caused by the contractor. This paper proposes modified regression modeling using machine learning (ML) techniques to develop solutions to the problem of predicting LDs for construction projects. The novel modeling methodology presented here is based on six years of data collection from many construction projects across the United States. It represents an innovative use of Multiple Linear Regression (MLR) models hybridized with machine learning (ML). The proposed methodology is evaluated using real datasets, where the developed model is designed to outperform the state-of-the-art LD forecast accuracy. Herein, seven modified regression-based models showed high accuracy in predicting the LDs. Nevertheless, those models’ forecasting ability was limited, so another second-order prediction model is proposed to provide better LD estimations. Independent variables were categorized based on their influence on the estimated LDs. The Total Bid Amount variable had the highest impact, while the Funding Indicator variable had a minimal impact. LD prediction was negatively correlated with all change-order-related variables and Total Adjustment Days, which suggests that those variables introduce extreme uncertainties due to their complex nature. The developed prediction models help decision-makers make better LDs predictions, which is essential for construction project sustainability.