Probabilistic Approaches for Structural Health Monitoring of Structures and Infrastructures
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 2085
Special Issue Editors
Interests: structural health monitoring; bayesian inference; bayesian methods; bayesian modeling; bayesian networks; bridge engineering; reliability analysis; damage detection; multiobjective optimization
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; lifecycle cost analysis; wind-excited tall buildings; structural control
Interests: structural health monitoring; seismic risk assessment; historic buildings; digital twins and SHM data fusion
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Civil structures and infrastructures may suffer damage due to the action of natural hazards, (e.g., earthquakes, strong winds), other mechanical degradation phenomena (e.g., fatigue, corrosion), and their synergistic effects. Data-driven SHM techniques have emerged as valuable tools to monitor the health of a structure, enabling the identification of damage and plan repair operations and thus guaranteeing life safety and functionality.
Traditionally, SHM systems involve the on-site integration of dynamic and static sensors, along with data acquisition systems to continuously record information of different nature. Such information can be subjected to a large amount of uncertainty due to the inherent complexity of the structural systems and environmental disturbances (e.g., noise).
Although SHM is a growing area, research efforts are still needed to generate probabilistic frameworks based on automated numerical tools capable of managing different sources of uncertainties, and to comprehensively address the five-step hierarchy of a damage identification process: (i) detection, (ii) localization, (iii) classification, (iv) assessment, and (v) prediction.
This Special Issue invites high-quality contributions that address the investigation of the current state of the art, recent advances, real-world applications, as well as future perspectives in SHM for structures and infrastructures, including the following topics:
- State-of-the-art reviews and novel contributions in probabilistic-based SHM techniques for structures and infrastructures;
- Recent advances in SHM technologies;
- Machine learning applications;
- Novel methods on data fusion;
- Recent developments in Bayesian-based techniques as decision-support tools for the evaluation of structural integrity;
- The use of surrogate modeling for automated damage identification;
- Application of lifecycle cost analysis for reducing SHM-related operational costs and risks.
Dr. Laura Ierimonti
Dr. Laura Micheli
Prof. Dr. Ilaria Venanzi
Guest Editors
Manuscript Submission Information
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Keywords
- structural health monitoring
- damage detection
- Bayesian model updating
- machine learning
- data fusion
- probabilistic risk assessment
- decision-making
- lifecycle cost analysis
- surrogate modeling
