Infrastructure Management and Maintenance: Methods and Applications (2nd Edition)

Dear Colleagues,

Transport infrastructure has increasingly emerged as one of the most critical enablers of modern societal development. Well-designed and efficiently functioning infrastructure facilitates economic growth, enhances mobility, and fosters social integration. For instance, bridges overcome natural obstacles such as rivers, valleys, and straits, thereby linking geographically separated regions and enabling the smooth movement of goods, services, and people. Road networks form the backbone of terrestrial transport, connecting urban and rural areas, facilitating commerce, and ensuring accessibility to essential services. Tunnels, whether excavated through mountainous terrain or constructed beneath bodies of water, significantly reduce travel times and open access to otherwise isolated regions, stimulating tourism, trade, and regional development. Collectively, these infrastructures not only provide the physical framework for mobility but also serve as catalysts for economic vitality and societal cohesion.

In recent decades, there has been growing recognition that the future of infrastructure lies not solely in the construction of new assets but also in the sustainable management and preservation of existing ones. Much of the world’s infrastructure is ageing, and replacing it entirely is often economically, environmentally, and socially impractical. Consequently, maintenance, rehabilitation, and life extension have become top priorities for engineers, policymakers, and researchers. In this context, sustainability and resilience are at the forefront of infrastructure engineering, guiding the development of strategies that ensure safety, performance, and longevity while minimising environmental impact.

Achieving these goals requires advanced tools and methodologies capable of supporting all phases of the infrastructure lifecycle in a more accurate, efficient, cost-effective, and environmentally responsible manner. Numerical and computational methods play a pivotal role in this process. Reliability analysis provides probabilistic assessments of structural safety, enabling the identification of vulnerabilities and the planning of preventive maintenance before failures occur. Nonlinear analysis allows engineers to capture complex material and structural behaviours under extreme loading conditions, leading to more robust and resilient designs. Machine learning for damage assessment offers automated and highly scalable capabilities for detecting and classifying defects from inspection data, significantly reducing the time, cost, and subjectivity associated with traditional evaluation methods. Anomaly detection algorithms monitor performance data in real time, identifying irregular patterns that may indicate the early stages of deterioration or malfunction. Predictive analytics, leveraging both historical records and live sensor data, enables the anticipation of maintenance needs, optimises resource allocation, and extends asset service life.

When integrated, these advanced methods enable a data-driven, proactive approach to infrastructure management, enhancing safety, reducing life-cycle costs, and promoting long-term sustainability. As infrastructure systems face increasing demands from urbanisation, climate change, and resource constraints, such innovations are essential for ensuring that society’s critical transport networks remain reliable, resilient, and fit for purpose in the decades to come.

Dr. José Campos Matos
Dr. Ngoc-Son Dang
Dr. Hélder Sousa
Prof. Dr. Alfred Strauss
Prof. Dr. Rade Hajdin
Guest Editors

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• infrastructure
• sustainability
• resilience
• reliability analysis
• machine learning
• damage assessment
• anomaly detection
• predictive analytics

• Infrastructure Management and Maintenance: Methods and Applications in Applied Sciences (6 articles)