Comparison of Approaches for Data Analysis of Multi-Parametric Monitoring Systems: Insights from the Acuto Test-Site (Central Italy)

• Main Authors: Matteo Fiorucci, Salvatore Martino, Francesca Bozzano, Alberto Prestininzi
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Applied Sciences
• Subjects: multi-parametric monitoring system; data analysis; landslides; rock masses; slope stability
• Online Access: https://www.mdpi.com/2076-3417/10/21/7658

This paper deals with monitoring systems to manage the risk due to fast slope failures that involve rock masses, in which important elements (such as infrastructures or cultural heritages, among the others) are exposed. Three different approaches for data analysis were here compared to evaluate their suitability for detecting mutual relations among destabilising factors, acting on different time windows, and induced strain effects on rock masses: (i) an observation-based approach (OBA), (ii) a statistics-based approach (SBA) and (iii) a semi-empirical approach (SEA). For these purposes, a test-site has been realised in an abandoned quarry in Central Italy by installing a multi-parametric monitoring sensor network on a rock wall able to record strain effects induced by natural and anthropic forcing actions (like as temperature, rainfall, wind and anthropic vibrations). The comparison points out that the considered approaches allow one to identify forcing actions, responsible for the strain effects on the rock mass over several time windows, regarding a specific size (i.e., rock block dimensional scale). The OBA was more suitable for computing the relations over short- to medium time windows, as well as the role of impulsive actions (i.e., hourly to seasonal and/or instantaneous). The SBA was suitable for computing the relations over medium- to long time windows (i.e., daily to seasonal), also returning the time lag between forcing actions and induced strains using the cross-correlation statistical function. Last, the SEA was highly suitable for detecting irreversible strain effects over long- to very long-time windows (i.e., plurennial).