An Empirical Evaluation of the Performance of Deep Neural Networks on Delay Risk Prediction in Urban Flexible Pavement Projects in Iraq
الباحث الأول:
Ban Ali Kamil
الباحثين الآخرين:
Ban Ali Kamil
المجلة:
Engineering, Technology & Applied Science Research
تاريخ النشر:
26 أغسطس، 2025
مختصر البحث:
Ongoing time overruns in urban Flexible Pavement Projects (FPP) highlight the inadequacy of traditional
risk forecasting techniques, which often overlook nonlinear and project-specific delay factors. While recent
Artificial Intelligence (AI)-bas…
Ongoing time overruns in urban Flexible Pavement Projects (FPP) highlight the inadequacy of traditional
risk forecasting techniques, which often overlook nonlinear and project-specific delay factors. While recent
Artificial Intelligence (AI)-based approaches have been proposed, most remain at a descriptive level,
demonstrating only a few mathematically expressible and experimentally validated models suitable for
urban road networks. This study addresses these gaps by developing a closed-form Artificial Neural
Network (ANN) model using nine carefully selected predictors drawn from recent engineering practices
and project data in Najaf, Iraq. The model incorporates advanced preprocessing, including robust outlier
detection and min–max scaling, and is trained on a newly compiled dataset covering 35 major projects,
thereby improving on previous studies' shortcomings in terms of both data quality and methodological
transparency. Empirical results demonstrate that the ANN substantially outperforms baseline models,
achieving an R2 of 0.847 and a Mean Absolute Percentage Error (MAPE) of 7.10%, with all improvements
being statistically significant (p < 0.001). Additionally, feature sensitivity analysis identified payment delay
and contractor experience as the most influential risk factors, underscoring the model's practical
relevance. Importantly, the modular mathematical structure of the ANN facilitates transparent
benchmarking and direct transferability to other urban regions, while creating a sound and replicable
paradigm for impact-based, data-driven decision-making and planning infrastructure. Thus, the proposed
model constitutes a benchmark for future research on predictive modelling of time overruns in urban
pavement projects.
