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Assessing cost overrun in the saudi arabian building construction projects for value preservation
Ehab A Mlybari1, Abdulaziz I Al-Muaybid2, Moussa S Elbisy1
1Department of Civil Engineering, College of Engineering and Architecture, Umm Al-Qura University, Makkah, Saudi Arabia.
Scientific Reports
|June 22, 2026
Summary
This study identifies key cost overrun factors in construction and develops a predictive model. A General Regression Neural Network (GRNN) model accurately forecasts project expenditures, aiding budget control.
Area of Science:
- Construction Management
- Data Science
- Predictive Analytics
Background:
- Cost overruns are a major challenge in construction projects, impacting budget and schedule adherence.
- Existing predictive tools lack the accuracy and validation needed for proactive cost control.
Purpose of the Study:
- To identify significant cost overrun factors in building construction.
- To develop and validate an accurate machine learning (ML) predictive model for construction project expenditures.
Main Methods:
- Data collection through surveys and interviews with industry experts, assessing 49 pre-identified factors.
- Prioritization of cost overrun causes using the Relative Importance Weight (RIW) method.
- Development and comparison of seven ML architectures, including General Regression Neural Network (GRNN).
Main Results:
- Internal project control deficiencies (scope changes, design issues, poor management) constitute 70% of significant cost overrun factors.
- The GRNN model demonstrated superior predictive performance with a Mean Absolute Percentage Error (MAPE) below 3% and a correlation (R) of 0.95.
- The validated GRNN model provides a reliable decision-support system for expenditure optimization.
Conclusions:
- Internal project management deficiencies are primary drivers of construction cost overruns.
- The GRNN model offers a validated, high-accuracy solution for proactive construction cost forecasting.
- This research enhances expenditure control and project value preservation in the construction industry.
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