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Risk-indexed artificial neural network for predicting duration and cost of irrigation canal-lining projects using survey-based calibration and python validation

Article scientifique 2025 Anglais

Résumé

The purpose of this study is to develop and validate a risk-driven predictive model for estimating project duration and cost in irrigation canal lining projects, where uncertainties often lead to delays and budget overruns. Ninety-three factors were first reduced to twenty using AHP-RII (Cronbach's [Formula: see text]). A multi-layer perceptron (128-64-32, ReLU, Adam, early stopping) was trained on 5000 simulated scenarios and validated on eight projects with leave-one-project-out cross-validation. The model had [Formula: see text] (training), 0.82 (testing), and made errors within the limits of 0.87 months (time) and EGP 102,500 (cost) on average.The developed model was deployed as a Python-based desktop application, enabling engineers and planners to generate accurate time and cost forecasts during early project stages. This research introduces an integrated ANN-based framework that combines expert-driven risk assessment with machine learning, providing a practical decision-support tool for infrastructure projects.

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Taha, B., Ibrahim, A., Soliman, A. (2025). Risk-indexed artificial neural network for predicting duration and cost of irrigation canal-lining projects using survey-based calibration and python validation. https://doi.org/10.1038/s41598-025-24125-1

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Auteur(s)

Boshra Taha Ahmed H. Ibrahim Asmaa A. Soliman

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