Structural Health Monitoring in Bridge Engineering

Dear Colleagues,

As bridges worldwide age and become more complex, the need for effective Structural Health Monitoring (SHM) tools has never been more urgent. SHM systems can provide real-time assessments, early detection of potential problems, and proactive maintenance strategies to prevent catastrophic failures. Recent advancements in artificial intelligence and big data have significantly accelerated the development of SHM techniques for critical civil structures and infrastructure, such as bridges and viaducts.

This Special Issue brings together the latest research, findings, and practical applications in the field of SHM for bridge structures. Contributions should focus on innovative approaches that enhance the reliability, efficiency, and cost-effectiveness of bridge health assessment and management.

Potential topics include but are not limited to:

• Novel SHM technologies: Wireless sensor networks, fiber optic sensors, unmanned aerial vehicles (UAVs), ground-penetrating radar (GPR), computer vision (CV), and other emerging techniques.
• Data-driven SHM: Machine learning, deep learning, and artificial intelligence applications for data analysis, damage detection, and prognosis.
• Multi-sensor fusion: Integration of different sensor modalities for comprehensive bridge monitoring and assessment.
• SHM for specific bridge types: Applications for various bridge types, such as concrete, steel, composite, and cable-stayed bridges.
• SHM in extreme events: Monitoring and assessment of bridge structures during and after natural disasters, earthquakes, and other extreme events.
• SHM for long-term monitoring: Long-term monitoring strategies and data management techniques for bridge health evaluation.
• SHM for maintenance and rehabilitation: Integration of SHM data into bridge maintenance and rehabilitation planning and decision-making.
• Case studies and practical applications: Real-world examples of SHM implementation in bridge engineering projects.

Authors are invited to submit original research papers, review articles, or technical notes on SHM of bridges. Submissions should present novel methods, experiments, or applications that advance SHM technology. We encourage interdisciplinary approaches, theoretical developments, and practical innovations. All submitted papers will undergo rigorous peer review. This Special Issue aims to foster collaborations and knowledge exchange in bridge engineering to address critical SHM challenges.

Dr. Alexandre A. Cury
Dr. Abdollah Malekjafarian
Dr. Alessandro Sabato
Dr. Flavio Barbosa
Dr. Marco Civera
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• novel SHM technologies
• data-driven SHM
• multi-sensor fusion
• SHM for specific bridge types
• SHM in extreme events
• SHM for long-term monitoring
• SHM for maintenance and rehabilitation
• case studies and practical applications

Title: Damage Identification in Frames using Approximate Bayesian Computations
Authors: Michael L. M. Souza; Daniel A. Castello; Ney Roitman
Affiliation: Civil Engineering Department of Universidade Federal do Rio de Janeiro；Mechanical Engineering Department of Universidade Federal do Rio de Janeiro
Abstract: The Bayesian framework is a feasible alternative to handle Structure Health Monitoring problems since these dynamic analyses are prone to numerous uncertainties, such as boundary conditions and physical properties. In this regard, the standard Bayesian method imposes the formulation of the likelihood density function, which can be tricky depending on the measured data and the observation model hypothesis. The present paper evaluates the performance of the proposed method, an Approximate Bayesian Computation framework with an adaptive error schedule, in a spatial frame SHM's Benchmark structure. The damage identification study is tackled through a model selection problem, which encompasses the integrity status of the structure and phenomenological uncertainties of structures' modelling behaviour, such as the damping hypothesis. The proposed methodology provided accurate predictions regarding the considered damage scenario. Keywords: Structure Health Monitoring; Damage Identification; Approximate Bayesian Computation; Likelihood-Free; Model Selection.