Intelligent Diagnosis, Performance Assessment and Life-Cycle Management of Engineering Structures

Dear Colleagues,

Engineering structures are continuously subjected to complex mechanical loads, environmental actions, material degradation, accidental events, and long-term deterioration during their service life. Ensuring their safety, reliability, durability, and resilience requires not only accurate structural analysis and performance assessment but also effective damage diagnosis, risk evaluation, maintenance decision-making, and strengthening interventions. With the rapid development of artificial intelligence, advanced computational mechanics, intelligent sensing, probabilistic analysis, digital twins, and high-performance materials, structural engineering is undergoing a transition from conventional analysis and reactive maintenance toward intelligent, predictive, and life-cycle-oriented management.

Accurate numerical modeling and structural performance assessment provide the fundamental basis for understanding structural behavior and supporting engineering decision-making. Advanced finite element methods, model updating, sensitivity analysis, perturbation methods, optimization algorithms, inverse analysis, and structural parameter identification have increasingly been used to improve the consistency between numerical models and actual structural responses. These approaches are particularly important for complex structural systems, prefabricated structures, cable and pipeline structures, transmission structures, and other engineering systems with uncertain material, geometric, boundary, and connection parameters.

Meanwhile, artificial intelligence and intelligent optimization techniques are providing new tools for structural analysis and diagnosis. Machine learning, deep learning, evolutionary optimization, swarm intelligence, physics-informed neural networks, and hybrid physics–data-driven methods can be integrated with structural mechanics and finite element models to facilitate parameter identification, damage detection, response prediction, model updating, and uncertainty reduction. These developments provide promising solutions for structural problems characterized by high dimensionality, incomplete observations, nonlinear behavior, or uncertain parameters.

Structural health monitoring and intelligent inspection are also becoming essential components of modern structural management. Multi-source information obtained from vibration sensors, strain monitoring, displacement measurements, images, videos, point clouds, acoustic signals, unmanned aerial vehicles, robotic inspection systems, and Internet of Things devices can be used to identify structural abnormalities and evaluate deterioration. By combining these data with computational models and artificial intelligence, structural damage can be detected, localized, quantified, and interpreted more effectively.

In addition to global structural response, the mechanical behavior and deterioration of structural materials, members, interfaces, and connections directly affect structural safety. Particular attention should therefore be paid to bond and anchorage behavior, connection performance, composite action, interface failure, fatigue, corrosion, and material degradation. Advanced structural materials and strengthening materials, including fiber-reinforced polymer composites, high-performance cementitious materials, smart materials, and multifunctional composites, also provide important opportunities for improving structural durability, repair efficiency, and long-term performance.

Extreme loads and environmental deterioration introduce additional uncertainties into structural performance. Seismic performance assessment, structural vulnerability and fragility analysis, soil–structure interaction, corrosion-induced degradation, fatigue damage, environmental deterioration, and multi-hazard effects are therefore important topics for structural safety assessment. Probabilistic methods, stochastic finite element analysis, Bayesian inference, reliability analysis, and uncertainty quantification can provide a rational basis for evaluating structural risk and supporting risk-informed maintenance and strengthening decisions.

Digital twins further provide an integrated framework connecting physical structures, numerical models, monitoring systems, inspection information, and intelligent algorithms. Through continuous or periodic updating of structural states and model parameters, digital twin systems can support structural visualization, condition assessment, performance prediction, damage diagnosis, risk assessment, predictive maintenance, and life-cycle management. The integration of digital twins with artificial intelligence, finite element models, sensing networks, and engineering inspection technologies is expected to play an increasingly important role in the smart operation and maintenance of engineering structures.

This Special Issue bring togethers recent advances in structural computational methods, intelligent diagnosis, structural performance assessment, monitoring, risk and reliability analysis, digital twins, and smart operation and maintenance. It particularly encourages submissions of interdisciplinary studies that integrate structural mechanics, artificial intelligence, optimization algorithms, probabilistic methods, sensing technologies, and advanced materials. Original research articles, review papers, numerical and experimental investigations, methodological developments, and practical engineering case studies are all welcome.

Topics of interest include, but are not limited to, the following:

• Artificial intelligence and machine learning for structural engineering;
• Intelligent optimization and evolutionary algorithms for structural analysis;
• Structural health monitoring and intelligent condition assessment;
• Smart sensors, IoT, and wireless sensing networks;
• Multi-source sensing and monitoring data fusion;
• UAV-, robot-, vision-, and image-based structural inspection;
• Structural damage detection, localization, identification, and quantification;
• Vibration-based and data-driven structural damage diagnosis;
• Physics-informed neural networks and physics–data hybrid modeling;
• Advanced finite element theories and computational methods;
• Finite element modeling and finite element model updating;
• High-order perturbation methods and stochastic computational mechanics;
• Structural sensitivity analysis and sensitivity-based identification;
• Structural parameter identification and inverse analysis;
• Intelligent and metaheuristic algorithms for model updating and parameter identification;
• Bayesian inference and Bayesian model updating;
• Stochastic finite element methods and uncertainty quantification;
• Structural reliability and probabilistic safety assessment;
• Risk assessment and risk-informed decision-making;
• Seismic response analysis and seismic performance assessment;
• Seismic vulnerability and fragility analysis of structural systems;
• Soil–structure interaction and the influence of site and soil conditions;
• Multi-hazard and extreme-event performance of engineering structures;
• Structural deterioration, corrosion, fatigue, and durability;
• Performance degradation of structural members and connection systems;
• Mechanical behavior of structural connections, joints, interfaces, and anchorage systems;
• Bond and interfacial behavior between reinforcing materials and concrete;
• Fiber-reinforced polymer reinforcement and composite structural systems;
• GFRP, CFRP, and other advanced materials for structural engineering;
• Innovative repair, rehabilitation, and strengthening materials;
• Performance assessment of prefabricated and assembled structures;
• Computational and experimental analysis of cable, pipeline, and underground structures;
• Safety and performance assessment of transmission towers and tower–line systems;
• Corrosion-induced degradation and performance assessment of transmission structures;
• Digital twins for structural analysis, monitoring, diagnosis, and maintenance;
• Digital twin-based structural state updating and performance prediction;
• Predictive, preventive, and condition-based maintenance;
• Remaining service life prediction and life-cycle performance assessment;
• Intelligent operation and maintenance of aging structures and infrastructure;
• Structural resilience, robustness, and sustainability;
• Integration of structural diagnosis, risk assessment, maintenance, repair, and strengthening;
• Experimental validation, benchmark studies, and practical engineering applications.

Through this Special Issue, we will promote the integration of fundamental structural mechanics, advanced computational methods, artificial intelligence, structural monitoring, probabilistic assessment, materials engineering, and digital twin technologies, thereby supporting the development of safer, more reliable, durable, resilient, and intelligent engineering structures.

Dr. Zhifeng Wu
Prof. Dr. Hao Xu
Dr. Zhihao Li
Guest Editors

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• intelligent structural diagnosis
• structural performance assessment
• structural health monitoring
• finite element model updating
• artificial intelligence and machine learning
• digital twin
• uncertainty quantification and Bayesian inference
• risk and reliability assessment
• smart operation and maintenance
• advanced structural and strengthening materials