Cost Prediction for Roads Construction using Machine Learning Models (original) (raw)

International journal of electrical and computer engineering systems

Abstract

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 bas...

Key takeaways

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  1. The study introduces a dataset of 3,000 road construction projects in Diyala, Iraq from 2012-2021.
  2. Machine learning models, including Ridge, LASSO, k-NN, and Random Forest, estimate road construction costs.
  3. Ridge Regression achieved the highest accuracy with an RMSE of 1.00 and R² of 0.99.
  4. K-NN and Random Forest showed significant errors in cost prediction due to dataset linearity issues.
  5. The research emphasizes the need for accurate cost estimation to mitigate overruns in civil engineering projects.

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