Big Data and Machine Learning for Health Monitoring and Maintenance of Transportation Infrastructure

Submission Deadline: 01 June 2026 View: 134 Submit to Special Issue

Guest Editors

Assoc. Prof. Derun Zhang

Email: derunzhang@hust.edu.cn

Affiliation: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 430074, Wuhan, China

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Research Interests: sustainable pavement infrastructure materials, intelligent pavement infrastructure maintenance, life cycle assessment, nondestructive testing and evaluation

Assoc. Prof. Yong Deng

Email: yongdeng@scut.edu.cn

Affiliation: School of Civil Engineering and Transportation, South China University of Technology, 510006, Guangzhou, China

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Research Interests: nondestructive testing, material and structural simulation, data analysis, applied artificial intelligence

Prof. Ruben Paul Borg

Email: ruben.p.borg@um.edu.mt

Affiliation: Faculty for the Built Environment, University of Malta, Msida, MSD 2080, Malta

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Research Interests: concrete materials and reinforced concrete structures, waste recycling and circular economy, sustainable construction & life cycle analysis, climate adaptation and resilience, quality management systems & product certification

Dr. Odette Lewis

Email: odette.lewis@um.edu.mt

Affiliation: Faculty for the Built Environment, University of Malta, MSD 2080, Msida, Malta

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Research Interests: planning and design of road transport infrastructure, greener and smarter mobility options

Summary

The safe and efficient operation of transportation infrastructure – including highways, railways, airports, and port facilities – is essential for economic development and societal mobility. However, the rapid increase in traffic demand, combined with aging structures, extreme weather events, and limited maintenance budgets, has created significant challenges for infrastructure management. In recent years, the integration of big data and machine learning has opened up transformative opportunities for health monitoring and decision-making in maintenance planning. With the proliferation of advanced sensing systems, unmanned aerial vehicles (UAVs), Internet of Things (IoT) devices, and large-scale inspection records, unprecedented volumes of multi-source data can be collected from transportation infrastructure. Machine learning and data-driven models provide powerful tools for extracting hidden patterns, predicting deterioration trends, and optimizing maintenance strategies under uncertainty. This special issue aims to bring together recent research advances in health monitoring, data analytics, and intelligent decision-making approaches for resilient and sustainable transportation systems. Topics of interest include but are not limited to:
· Novel sensing technologies and big data platforms for transportation infrastructure monitoring.
· Machine learning methods for damage detection and condition prediction.
· Digital twins and data-driven models for infrastructure life cycle assessment and life cycle management.
· Optimization and decision-support systems for maintenance planning.
· Case studies and practical applications of big data–driven infrastructure management.

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