An ML-based framework for predicting prestressing force reduction in reinforced concrete box-girder bridges with unbonded tendons

Calò, Mirko; Ruggieri, Sergio; Buitrago, Manuel; Nettis, Andrea; Adam, Jose M.; Uva, Giuseppina

doi:10.1016/j.engstruct.2024.119400

Engineering Structures

2025

DOI: 10.1016/j.engstruct.2024.119400

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An ML-based framework for predicting prestressing force reduction in reinforced concrete box-girder bridges with unbonded tendons

Mirko Calò,

Sergio Ruggieri,

Manuel Buitrago

et al.

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AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

Plevris,

Papazafeiropoulos

2024

Infrastructures

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This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety. This study begins with a bibliometric analysis to identify current research trends, key contributing countries, and emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions. This review also addresses the ethical considerations and societal impacts of AI in SHM, such as data privacy, equity, and transparency. We conclude by discussing future research directions and challenges, emphasizing the potential of AI to enhance the efficiency, safety, and sustainability of infrastructure systems.

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AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

Plevris,

Papazafeiropoulos

2024

Infrastructures

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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An ML-based framework for predicting prestressing force reduction in reinforced concrete box-girder bridges with unbonded tendons

Cited by 1 publication

References 51 publications

AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

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