An Explainable Deep Learning Framework for Energy-Efficient Water and Sewer Reticulation Infrastructure: Predicting Project Risk, Pumping Demand, and Sustainable Construction Performance

• Author(s): Gladman Nhamoinesu Machekera; Patronella Siphatisiwe Mtemeli; Malvern Munashe Dongo; Godsave Archford Sajanga; Munashe Naphtali Mupa
• Paper ID: 1719256
• Page: 3197-3209
• Published Date: 30-06-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 12 June-2026

Abstract

Water and sewer reticulation infrastructure sits at the intersection of civil engineering, public health, municipal energy management, and construction project delivery. Pumping, pressure management, leakage response, excavation sequencing, safety controls, and treatment-linked hydraulic loads can materially influence utility operating costs, greenhouse gas emissions, schedule reliability, and infrastructure resilience. This paper develops an explainable deep-learning framework for predicting three linked outcomes in water and sewer reticulation projects: pumping energy demand, project risk, and sustainable construction performance. The study integrates public Kaggle-derived schemas for smart water leak detection, construction project management, and wastewater treatment energy consumption with an experimental 5,000-observation panel representing pipe segments, sensor readings, pumping loads, site constraints, safety observations, costs, schedule variance, and sustainability metrics. Baseline machine-learning models are compared against deep neural and sequence models, while permutation-based explainability is used to identify the operational drivers of predicted pumping energy demand. The best tabular energy model achieved an R-squared of 0.973 and RMSE of 73.59 kWh/day, while the best risk classifier achieved macro-F1 of 0.737. The analysis indicates that flow rate, pressure drop, rotational speed, leakage flag, site constraint score, pipe age, vibration and equipment hours are the most influential determinants of energy-risk performance. The proposed framework offers a practical decision-support architecture through which civil engineers, project managers and utility operators can connect sensor evidence, project controls and explainable AI outputs to energy-aware scheduling, preventive maintenance, pump optimization, trench-safety planning and sustainability governance. The paper contributes an applied framework suited to infrastructure modernization efforts where technical performance, constructability and public accountability must be evaluated together.

Keywords

Water Reticulation, Sewer Infrastructure, Explainable AI, Deep Learning, LSTM, Graph Neural Networks, Project Risk, Pumping Energy, Sustainable Construction, Civil Engineering Project Management

Citations

IRE Journals:
Gladman Nhamoinesu Machekera, Patronella Siphatisiwe Mtemeli, Malvern Munashe Dongo, Godsave Archford Sajanga, Munashe Naphtali Mupa "An Explainable Deep Learning Framework for Energy-Efficient Water and Sewer Reticulation Infrastructure: Predicting Project Risk, Pumping Demand, and Sustainable Construction Performance" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 3197-3209 https://doi.org/10.64388/IREV9I12-1719256

IEEE:
Gladman Nhamoinesu Machekera, Patronella Siphatisiwe Mtemeli, Malvern Munashe Dongo, Godsave Archford Sajanga, Munashe Naphtali Mupa "An Explainable Deep Learning Framework for Energy-Efficient Water and Sewer Reticulation Infrastructure: Predicting Project Risk, Pumping Demand, and Sustainable Construction Performance" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1719256