Explorative data analysis from multiparametric monitoring at the Acuto Field Laboratory (Central Italy) for detecting preparatory conditions to rock block instabilities

• Published in: Italian journal of engineering geology and environment no. 2; p. 59
• Main Authors: Grechi, Guglielmo, Fernandes, Jennypher Rosa, Hu, Jean-Pierre, Gallais, Anne-Charlotte Le, Sampieri, Hugo, Amato, Gabriele, D'Angiò, Danilo, Fiorucci, Matteo, Iannucci, Roberto, Marmoni, Gian Marco, Martino, Salvatore
• Format: Journal Article
• Language: English
• Published: Rome Universita degli Studi di Roma "La Sapienza" 01.01.2022
• Subjects: Anthropogenic factors; Data analysis; Deformation; Deformation effects; Environmental parameters; Freeze-thaw; Freezing; Laboratories; Limestone; Machine learning; Mitigation; Monitoring; Monitoring systems; Parameters; Quarries; Rainfall; Rock falls; Rock masses; Rocks; Thawing; Vibrations; Italy
• ISSN: 1825-6635; 2035-5688
• DOI: 10.4408/IJEGE.2022-02.O-05

This study summarises the research activity carried out in the Acuto Field Laboratory (FR, Italy), where experiments testing the stability of a subvertical rock wall in limestone are ongoing within an abandoned quarry, now devoted to studies focused on the mitigation of geological risks. The research focuses on the natural factors that can prepare a subvertical rock mass to evolve through subsequent rock fall if predisposing conditions are verified. A network of multiparameter monitoring sensors is installed on three different sectors of the rock wall to record both the natural and anthropogenic stressors and the effects of deformation induced by them. In terms of stressors, the multiparametric monitoring system is able to detect the environmental parameters, such as temperature, rainfall, wind, strain, and vibrations. In terms of induced effects on the rock mass, the multiparametric monitoring system is suitable to detect deformation, displacement, and microseismicity. In this paper, the different monitored parameters are presented along with detailed analyses to highlight cause to effect relationships, such as freezing and thawing, to retrieve correlations among different factors. The obtained results represent the first analysis of the data recorded in the three instrumented sectors of the field laboratory and allowed evaluating the role of preparatory factors in inducing rock falls, opening further perspective on numerical modelling or machine learning applications based on monitoring data.