Titlebook: Experimental Vibration Analysis for Civil Engineering Structures; EVACES 2023 - Volume Maria Pina Limongelli,Pier Francesco Giordano,Alfr C

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,Die Gastst?tte und ihre Akteure,lopment and territorial cohesion. However, bridges, which are crucial elements of transportation networks, are increasingly susceptible to degradation caused by growing traffic volumes and severe weather events. Recent research efforts have focused on developing damage-sensitive features specificall

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Theorien zur Sozialisation im Jugendalter,ifying bridge damage from the response of an instrumented sensing vehicle. Most current methods are based on identifying bridge properties and supervised learning techniques. However, these approaches require data from the bridge at its different states (i.e., healthy and damaged conditions), which

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https://doi.org/10.1007/978-3-642-70917-3hile, with the recent growth and advancement in Machine Learning (ML) techniques, data-driven based Structural Health Monitoring (SHM) systems have piqued the interest of many scholars, as they have the potential to provide a fast and accurate solution to damage detection problems. Although some eff

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https://doi.org/10.1007/978-3-322-91404-0cted visually or using special vehicles called track geometry vehicles (TRVs). These methods are normally labor-expensive and not always effective. In this paper, vibration data collected from in-service trains are employed for the purpose of track monitoring. The proposed approach could be more eff