The role of the construction industry and its branches in building community resilience - Samer BuHamdan; Zoubeir Lafhaj; Wassim AlBalkhy; Laure Ducoulombier; Hassan Jai; Tarek Zayed (Environment Systems and Decisions, 2026)

Analysis of drainage system defects using co-occurrence patterns - Dramani Arimiyaw; Tarek Zayed; Jingchao Yang; Beenish Bakhtawar; Sherif Abdelkhalek; Mohamed Nashat (Tunnelling and Underground Space Technology, 2026)

From data to impact: Lifecycle-oriented digital twins for urban heat island mitigation and climate resilience - Eslam Ali; Ahmed Mansour; Mahmoud Abdallah; Amr Eldemiry; Abdelrahman E.E. Eltoukhy; Tarek Zayed (Sustainable Cities and Society, 2026)

A heterogeneous ensemble AI framework for predicting dissolved sulfide in urban sewer networks - Mohamed Nashat; Tarek Zayed; Dramani Arimiyaw; Jingchao YANG (Process Safety and Environmental Protection, 2026)

Integrated bridge deck performance index for reinforced concrete bridges - Bilal Manzoor; Tarek Zayed; Sherif Abdelkhalek; Chenqin Xiong; Abdul Hannan Qureshi; Eslam Mohammed Abdelkader (Innovative Infrastructure Solutions, 2026)
Safety, durability, and functionality of reinforced concrete (RC) bridge decks are critical for effective inspection and maintenance. The most popular technique is visual inspection (VI), but it is rather subjective and has not much ability to reveal subsurface defects like delamination and corrosion. To address these limitations, this study integrated non-destructive testing (NDT) techniques, including Infrared Thermography (IRT) and Ground Penetrating Radar (GPR), into a bridge deck performance index (BDPI) for enhanced evaluation. BDPI was developed with the help of fuzzy mathematics and the combination of Compromise Solution (CoCoSo) technique so that it can facilitate the effective multi-criteria decision-making process. The approach was validated by a full-scale RC bridge deck case study. The results demonstrated that the integrated BDPI significantly improves assessment reliability compared to VI alone. Quantitatively the proposed method resulted in about 50% reduction in inspection time, 50% reduction in cost and manpower and increased the accuracy of defect detection by adding subsurface evaluation. The results elaborated that the use of VI alone to measure a bridge deck could result in an inaccurate performance index, but the use of NDT methods will offer a more reliable score. The suggested BDPI provides a viable decision-support framework to help transportation entities prioritize their maintenance strategies, optimize repair, and prolong the life of older bridge infrastructure.

A nexus of barriers and strategies for circular economy implementation in the construction industry: A structural equation modeling approach - Sulemana Fatoama Abdulai; Gabriel Nani; Ridwan Taiwo; Tarek Zayed; Azeez Arisekola Jimoh; Akinlo Olorunju Mogbojuri (Environmental Impact Assessment Review, 2026)

Data-driven interface for shallow rainfall-induced landslide forecasting considering trigger randomness and spatiotemporal dependencies - Kyrillos Ebrahim; Ridwan Taiwo; Tarek Zayed (Landslides, 2026)

A systematic review of climate change impacts on sewer overflow - Zhe Zou; Tarek Zayed; Shihui Ma (Environmental Research, 2026)

Cross-regional leak detection in water distribution networks through domain transfer - Ziyang Xu; Rongsheng Liu; Haixing Liu; Tarek Zayed (Water Research, 2026)

An innovative fuzzy framework for overcoming lean barriers in sustainable infrastructure megaprojects - Abdelazim Ibrahim; Tarek Zayed; Zoubeir Lafhaj; Jingchao Yang (Innovative Infrastructure Solutions, 2026)

Structural failure in urban sewer pipelines: A comprehensive review from analysis to intervention techniques - Jingchao Yang; Tarek Zayed; Dramani Arimiyaw; Mohamed Nashat; Xianyang Liu; Abdelazim Ibrahim (Tunnelling and Underground Space Technology, 2026)

Multi-step time-to-failure predictions in water pipelines using feature engineering and cascading ensembles - Beenish Bakhtawar; Tarek Zayed; Husnain Arshad (Water Research, 2025)

OWHK: Operational volumetric water content forecasting model for shallow rainfall-induced landslides in Hong Kong - Kyrillos Ebrahim; Ridwan Taiwo; Tarek Zayed (Engineering Geology, 2025)

Bridging barriers to lean construction adoption in megaprojects: a data-driven contribution to sustainable development using SEM - Abdelazim Ibrahim; Tarek Zayed; Zoubeir Lafhaj (Environment, Development and Sustainability, 2025)
Megaprojects frequently face cost overruns, delays, and inefficiencies due to their complexity and multi-stakeholder dynamics. As large-scale infrastructure projects with significant economic and social impacts, they demand innovative solutions to enhance performance and sustainability. Lean Construction (LC) offers a promising approach to achieving these goals, yet its adoption remains limited by various barriers. While prior studies have identified Critical Success Factors (CSFs), there is a lack of robust statistical validation on how these factors mitigate LC adoption challenges. To address this gap, this study employs a sequential mixed-methods approach integrating a systematic literature review to identify preliminary factors, followed by semi-structured interviews with industry experts to refine and validate these factors. A structured questionnaire was then administered to 379 construction professionals involved in megaprojects in China to gather quantitative data. Finally, Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine the causal relationships between CSFs and LC Barriers (LCBs). Results show that CSFs significantly reduce LCBs, with Strategic Leadership (β = 0.243), Resource and Knowledge Availability (β = 0.193), and Process Improvement (β = 0.188) being most influential. The model demonstrates acceptable explanatory power (R 2 = 0.263), predictive relevance (Q 2 = 0.252), and effect size (F 2 = 0.356). This study provides the first empirically validated framework linking success factors with LC adoption barriers, offering actionable strategies for more effective implementation in complex project environments.

Interpretable deep learning for acoustic leak detection in water distribution systems - Ziyang Xu; Haixing Liu; Guangtao Fu; Run Zheng; Tarek Zayed; Shuming Liu (Water Research, 2025)

Explainable deep learning models for predicting water pipe failures - Ridwan Taiwo; Tarek Zayed; Beenish Bakhtawar; Bryan T. Adey (Journal of Environmental Management, 2025)

AIoT-Driven Leak Detection in Real Water Networks Using Hydrophones - Beenish Bakhtawar; Ali Fares; Tarek Zayed (Water Resources Management, 2025)
Acoustic sensing technology is a familiar approach to detect leakage in urban water networks. Critical issues like false alarms, difficult leak locations, missed leaks, unknown site conditions, and high repair costs are still prevalent. The situation warrants developing a more sophisticated and efficient leak detection approach in real water networks. Hydrophone based acoustic technology has a strong promise for high precision detection of leaks. However, AIoT approach using hydroacoustic data for real water leak detection are rarely reported. The current study, therefore, proposes an integrated signal analysis and machine learning-based ensemble model for leak detection using a hydrophone-based smart IoT system. The results show that the most significant features are peak frequency and maximum amplitude. Random forest is the most robust classifier for cost effective long-term monitoring, and the proposed voting ensemble classifies leaks and no leaks with high accuracy on both unseen data and new sites. Specifically, proposed models have very few alarms and missed leaks are reported, a significant problem in models developed using accelerometers and noise loggers. The study shows a significant contribution to the domain of leak detection for real urban water networks.

A comprehensive review of influential factors and predictive techniques of time to failure for sewer pipes - Jingchao Yang; Tarek Zayed; Dramani Arimiyaw; Rui Xiao (Tunnelling and Underground Space Technology, 2025)

Cross-industry review of autonomous alignment technologies: Adaptation potential for modular construction - Sulemana Fatoama Abdulai; Tarek Zayed; Ibrahim Yahaya Wuni; Maxwell Fordjour Antwi-Afari; Abdul-Mugis Yussif (Journal of Cleaner Production, 2025)

A statistical perspective on natural gas distribution pipeline incidents in the United States - Rui Xiao; Tarek Zayed; Mohamed A. Meguid; Laxmi Sushama (Gas Science and Engineering, 2025)

Acoustic leak localization for water distribution network through time-delay-based deep learning approach - Rongsheng Liu; Tarek Zayed; Rui Xiao (Water Research, 2025)

Rapid failure risk analysis of corroded gas pipelines using machine learning - Rui Xiao; Tarek Zayed; Mohamed Meguid; Laxmi Sushama (Ocean Engineering, 2024)

A hybrid machine learning-based model for predicting failure of water mains under climatic variations: A Hong Kong case study - Jiduo Xing; Tarek Zayed; Yanqing Dai; Yuyang Shao; Zainab Almheiri (Tunnelling and Underground Space Technology, 2024)

Time varying reliability analysis of corroded gas pipelines using copula and importance sampling - Rui Xiao; Tarek Zayed; MohamedA. Meguid; Laxmi Sushama (Ocean Engineering, 2024)

Advanced acoustic leak detection in water distribution networks using integrated generative model - Rongsheng Liu; Tarek Zayed; Rui Xiao (Water Research, 2024)

Predicting failure pressure of corroded gas pipelines: A data-driven approach using machine learning - Rui Xiao; Tarek Zayed; Mohamed A. Meguid; Laxmi Sushama (Process Safety and Environmental Protection, 2024)

Modelling the relationship between circular economy barriers and drivers for sustainable construction industry - Sulemana Fatoama Abdulai; Gabriel Nani; Ridwan Taiwo; Prince Antwi-Afari; Tarek Zayed; Adebayo Olatunbosun Sojobi (Building and Environment, 2024)

Designing a fuzzy-based framework for implementing performance-based contracts in bridge asset management - Mohammed Alsharqawi; Saleh Abu Dabous; Tarek Zayed (Innovative Infrastructure Solutions, 2024)

Data-Driven Approaches for Vibroacoustic Localization of Leaks in Water Distribution Networks - Rongsheng Liu; Salman Tariq; Ibrahim A. Tijani; Ali Fares; Beenish Bakhtawar; Harris Fan; Rui Zhang; Tarek Zayed (Environmental Processes, 2024)
This study aims to propose Micro-electromechanical System (MEMS) accelerometers for leak localization in the water distribution network and assess the performance of machine learning models in accurately estimating leak locations. Intensive field experimentation was conducted to collect data for model development. Machine learning algorithms were employed to develop leak localization models, specifically artificial neural network (ANN) and support vector machine (SVM). Seventeen time-domain and frequency-domain features were extracted, and feature selection was performed using the backward elimination method. The results indicate that the ANN and SVM models are suitable classifiers for localizing leak distance. Both models achieved leak location predictions with over 80% accuracy, and the mean absolute errors were measured at 0.858 and 0.95 for the ANN and SVM models, respectively. The validation results demonstrated that the models maintained accuracies close to 80% when the distance between sensors and the leak was less than 15 m. However, the performance of the model deteriorates when leaks occur at distances greater than 15 m. This study demonstrates the applicability of MEMS accelerometers for leak localization in water distribution networks. The findings highlight the promising potential of employing MEMS accelerometers-based ANN and SVM models for accurate leak localization in urban networks, even under real-world, uncontrolled conditions. However, the current model exhibits limited performance in long-distance leak localization, requiring further research to address and resolve this issue.

Rainfall-induced landslide prediction models, part ii: deterministic physical and phenomenologically models - Kyrillos M. P. Ebrahim; Sherif M. M. H. Gomaa; Tarek Zayed; Ghasan Alfalah (Bulletin of Engineering Geology and the Environment, 2024)
Landslides are frequent hillslope events that may present significant risks to humans and infrastructure. Researchers have made ongoing efforts to assess the potential danger associated with landslides, intending to ascertain the location, frequency, and magnitude of these events in a given area. This study is meant to supplement the previous study (Part I), which explored empirical and physically based causative thresholds. In this paper (Part II), a systematic review is used to conduct an in-depth study of existing research on prediction models. Deterministic physical approaches were investigated for local-scale landslides. Next, national-scale landslide susceptibility models are discussed, including qualitative and quantitative models. Consequently, key findings about rainfall-induced landslides are reviewed. The strategy selection is generally governed by data and input factors from a macroscopic perspective, while the better prediction model is defined by dataset quality and analysis model performance from a microscopic perspective. Physically based causative thresholds can be used with limited geotechnical or hydrological data; otherwise, numerical analysis provides optimal accuracy. Among all statistical models, the hybrid artificial intelligence model achieved the best accuracy. Finally, current challenges have concentrated on integrating AI and physical models to obtain high accuracy with little data, prompting research suggestions. Advanced constitutive models for real-time situations are lacking. Dynamic and spatiotemporal susceptibility maps are also used, although their subjectivity needs further research. This study analyses how to choose the best model and determine its key traits. This research provides valuable insights for scholars and practitioners seeking innovative approaches to lessen the severity of landslides.

Proactive exfiltration severity management in sewer networks: A hyperparameter optimization for two-tiered machine learning prediction - Shihui Ma; Nehal Elshaboury; Eslam Ali; Tarek Zayed (Tunnelling and Underground Space Technology, 2024)

A state-of-the-art review for the prediction of overflow in urban sewer systems - Shihui Ma; Tarek Zayed; Jiduo Xing; Yuyang Shao (Journal of Cleaner Production, 2024)

Modelling the relationship between digital twins implementation barriers and sustainability pillars: Insights from building and construction sector - Ahmed Farouk Kineber; Atul Kumar Singh; Abdulwahed Fazeli; Saeed Reza Mohandes; Clara Cheung; Mehrdad Arashpour; Obuks Ejohwomu; Tarek Zayed (Sustainable Cities and Society, 2023)

A multi-tier deterioration assessment models for sewer and stormwater pipelines in Hong Kong - Sherif Abdelkhalek; Tarek Zayed (Journal of Environmental Management, 2023)

Review of numerical approaches used in soil-pipe interaction analysis of water mains - Rui Zhang; Sherif M.M.H. Gomaa; Mohamed Hussein; Tarek Zayed; Mohamed Meguid (Transportation Geotechnics, 2023)

Evaluating the sensory and health impacts of exposure to sewer overflows on urban population - Ehsan Aghdam; Saeed Reza Mohandes; Tarek Zayed (Journal of Cleaner Production, 2023)

Predicting quality parameters of wastewater treatment plants using artificial intelligence techniques - Ehsan Aghdam; Saeed Reza Mohandes; Patrick Manu; Clara Cheung; Akilu Yunusa-Kaltungo; Tarek Zayed (Journal of Cleaner Production, 2023)

A review of climatic impacts on water main deterioration - Tayyab Ahmad; Ibrahim Abdelfadeel Shaban; Tarek Zayed (Urban Climate, 2023)

Development of sustainable water infrastructure: A proper understanding of water pipe failure - Ridwan Taiwo; Ibrahim Abdelfadeel Shaban; Tarek Zayed (Journal of Cleaner Production, 2023)