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Seismic measurements to recognize rock mass damaging induced by recurrent vibrations

TitleSeismic measurements to recognize rock mass damaging induced by recurrent vibrations
Publication TypePresentazione a Congresso
Year of Publication2018
AuthorsD’Angio, D., Iannucci R., Lenti L., Martino S., and Paciello A.
Conference NameGeomechanics and Geodynamics of Rock Masses
KeywordsAcoustic noise, Artificial sources, Earthquakes, High frequency signals, Long term stability, Low-frequency signals, Risk mitigation strategy, Rock bursts, Rock mass mechanical parameter, Rock mechanics, Rocks, Seismic measurements, Seismic monitoring, Spectrum Analysis, Vibration analysis
Abstract

Recurrent train-induced vibrations can affect the long-term stability of rock slopes located in proximity of railways, where rock falls can cause high-risk conditions for transit and passengers. Seismic monitoring of the unstable rock walls was experienced as a risk-mitigation strategy, in order to detect variations in the vibrational behaviour over time that can be related to microcracking able to modify the pre-existing crack-net. This effect, known as “rock-mass damaging”, can justify changes of rock mass mechanical parameters and consequently of rock mass rheology and they can lead toward slope failures (i.e. rock falls or slides). By deriving mean ambient noise levels from signals recorded over a representative time window and by comparing time histories and related Fourier spectra, potential changes in the rock mass vibrational trend can be observed. Moreover, by spectral analyses of seismic ambient noise records it is possible to distinguish the contributions to vibration due to natural sources (generally generating low-frequency signals), including wind, thermal effects or earthquakes, respect to the ones induced by artificial sources (generally generating high-frequency signals), including the train transit. © 2018 Taylor & Francis Group, London, UK.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061393288&partnerID=40&md5=b000f6903bccc134dc57981b052439e0
Citation KeyD’Angio2018485