Neural Network-Based Approaches for Predicting Construction Overruns with Sustainability Considerations

Kristina Galjanić (), Ivan Marović () and Tomaš Hanak
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Kristina Galjanić: Faculty of Civil Engineering, University of Rijeka, Radmile Matejčić 3, 51000 Rijeka, Croatia
Ivan Marović: Faculty of Civil Engineering, University of Rijeka, Radmile Matejčić 3, 51000 Rijeka, Croatia
Tomaš Hanak: Faculty of Civil Engineering, Brno University of Technology, Veveri 95, 602 00 Brno, Czech Republic

Sustainability, 2025, vol. 17, issue 16, 1-17

Abstract: This research focuses on developing neural network-based models for predicting time and cost overruns in construction projects during the construction phase, incorporating sustainability considerations. Previous studies have identified seven key performance areas that affect the final outcome: productivity, quality, time, cost, safety, team satisfaction, and client satisfaction. Although the interconnections among these performance areas are recognized, their exact relationships and impacts are not fully understood. Hence, the utilization of a neural networks proves to be highly beneficial in predicting the outcome of future construction projects, as it can learn from data and identify patterns, without requiring a complete understanding of these mutual influences. The neural network was trained and tested on the data collected on five completed construction projects, each analyzed at three distinct stages of execution. A total of 182 experiments were conducted to explore different neural network architectures. The most effective configurations for predicting time and cost overruns were identified and evaluated, demonstrating the potential of neural network-based approaches to support more sustainable and proactive project management. The time overrun prediction model demonstrated high accuracy, achieving a MAPE of 10.93%, RMSE of 0.128, and correlation of 0.979. While the cost overrun model showed a lower predictive accuracy, its MAPE (166.76%), RMSE (0.4179), and correlation (0.936) values indicate potential for further refinement. These findings highlight the applicability of neural network-based approaches in construction project management and their potential to support more proactive and informed decision-making.

Keywords: neural networks; prediction; construction project; performance area; key project stakeholders (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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