Artículo

AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

Resumen

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.
Herrera-Franco, Gricelda (57212546828); Carrión-Mero, Paúl (57208038096); Montalván-Burbano, Néstor (57210814655); Mora-Frank, Carlos (57216865424); Berrezueta, Edgar (23007383200)
2022
10.3390/w14071082
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128338875&doi=10.3390%2fw14071082&partnerID=40&md5=4a66094020184931155000a0e9ebbecb
Facultad de Ciencias de La Ingeniería, Universidad Estatal Península de Santa Elena (UPSE), Avda. Principal La Libertad-Santa Elena, La Libertad, 240204, Ecuador; Geo-Recursos y Aplicaciones GIGA, ESPOL Polytechnic University, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, Guayaquil, 090112, Ecuador; Centro de Investigación y Proyectos Aplicados a las Ciencias de la Tierra (CIPAT), ESPOL Polytechnic University, Campus Gustavo Galindo, Km 30.5 Via Perimetral, Guayaquil, 090112, Ecuador; Facultad de Ingeniería en Ciencias de la Tierra, Campus Gustavo Galindo, ESPOL Polytechnic University, Km 30.5 Vía Perimetral, Guayaquil, 090112, Ecuador; Business and Economy Department, University of Almería, Ctra. Sacramento s/n, La Cañada de San Urbano, Almeria, 04120, Spain; Departamento de Recursos para la Transición Ecológica, Instituto Geológico y Minero de España (IGME, CSIC), Oviedo, 33005, Spain
All Open Access; Gold Open Access; Green Open Access
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