Guest Editors
Dr. Emmanuel Gbenga Dada
Email: gbengadada@unimaid.edu.ng
Affiliation: Department of Computer Science, University of Maiduguri, Maiduguri P.M.B. 1069, Nigeria
Homepage:
Research Interests: machine learning, health care, optimization, soft computing, robot motion planning, computational intelligence, cyber security, data science, neural networks and artificial intelligence, pattern recognition
Dr. Stephen Joseph Bassi
Email: sjbassi74@unimaid.edu.ng
Affiliation: Department of Computer Science, University of Maiduguri, Maiduguri P.M.B. 1069, Nigeria
Homepage:
Research Interests: network algorithmic, data analytics, artificial intelligence, big data, data mining
Dr. Ayodele Lasisi
Email: alasisi@kku.edu.sa
Affiliation: Department of Computer Science, College Of Computer Science, King Khalid University, Abha, Saudi Arabia
Homepage:
Research Interests: artificial intelligence, artificial immune system, negative selection algorithm, data security
Summary
The integration of Machine Learning (ML) into Structural Health Monitoring (SHM) has transformed traditional civil infrastructure assessment by enabling more timely, efficient, and accurate detection of structural damage. As modern infrastructure grows in complexity, conventional SHM methods—reliant on manual inspections—are proving inadequate due to their time-intensive and error-prone nature. ML algorithms offer powerful solutions by processing large volumes of sensor-generated data in real time, identifying subtle patterns and anomalies that may indicate structural degradation or failure. Techniques such as supervised, unsupervised, and semi-supervised learning, along with deep learning models like CNNs and RNNs, are increasingly being used to enhance detection accuracy and predict failure mechanisms.
This special issue invites contributions on advanced ML-based SHM systems applied to critical infrastructure such as bridges, tunnels, high-rise buildings, and heritage structures. Topics may include data fusion from multi-sensor networks, edge computing integration, real-time monitoring frameworks, and model interpretability. Also encouraged are studies addressing current challenges such as data quality, algorithm transparency, and generalization capabilities. Through innovative research and cross-disciplinary collaboration, this issue aims to advance ML-enabled SHM for safer, more resilient, and cost-efficient infrastructure management. Contributions from both academia and industry that explore practical implementations and future research directions are highly welcome.
Keywords
structural health monitoring (SHM), machine learning (ML), real-time damage detection, deep learning models, civil infrastructure, sensor data fusion
Published Papers
Login
Register
Submit a Paper
Propose a Special lssue