Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.13.10.8
Cost Prediction for Roads Construction using Machine Learning Models
Yasamin Ghadbhan Abed
; University of Diyala, College of Science, Department of Computer Science, Diyala, Iraq
Taha Mohammed Hasan
; University of Diyala, College of Science, Department of Computer Science Diyala, Iraq
Raquim Nihad Zehawi
; University of Diyala, College of Engineering, Department of Highway and Airport Engineering Diyala, Iraq
Sažetak
Predicting conceptual costs is among the essential criteria in project decision-making at the early stages of civil engineering disciplines. The cost estimation model availability that may help in the early stages of a project could be incredibly advantageous in respect of cost alternatives and more extraordinary cost-effective solutions periodically. There is a lack of case datasets. Most of the proposed dataset was inefficient. This study offers a new data set that includes the elements of road construction and economic advantages in the year of project construction. Real project data for rural roads in the State of Iraq / Diyala Governorate for the years 2012 to 2021 have use to train a predictive model with a high rate of accuracy based on machine learning (ML) methods. Ridge and Least Absolute Shrinkage and Selection Operator (LASSO) Regressions, K Nearest Neighbors (k-NN), and Random Forest (RF) algorithms have employ to create models for estimating road construction costs based on real-world data. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2) coefficient of determination are utilize to assess the models' performance. The analysis indicated that the RR is the best model for road construction costs, with results R2 = 1.0, MAPE =0.00, and RMSE=0.00. The results showed that the cost estimates were accurate and aligned with the project bids.
Ključne riječi
Construction; Roads; Cost estimation; Machine learning; Ridge regression;
Hrčak ID:
290490
URI
Datum izdavanja:
21.12.2022.
Posjeta: 1.723 *