Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects

Purpose: The purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects. Design/methodology/approach: The proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor. Findings: The findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values. Research limitations/implications: The study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage. Originality/value: The study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.