Innovative monitoring systems for civil engineering applications

Global urbanization is increasingly threatened by landslide hazards, which jeopardize public safety, building stability, and the integrity of strategic infrastructure. Among these, slow-moving deep-seated landslides present a formidable challenge due to their vast spatial scales and the significant socio-economic consequences associated with infrastructure repair, community relocation, and the loss of cultural heritage. In this critical context, the development of reliable Landslide Early Warning Systems (LEWS) is essential to detect hazardous accelerations and implement effective mitigation strategies. This PhD thesis, entitled "Innovative monitoring systems for civil engineering applications", explores the development of advanced monitoring solutions within a multidisciplinary framework, bridging geotechnical, opto-electronic, and environmental engineering. The research addresses the characterization of natural hazards with a primary focus on the mechanical behaviour of natural deposits prone to complex landslide phenomena, both superficial and deep-seated. The title reflects an ambitious scope because, although the thesis specifically presents case studies focused on slow-moving landslides, the resulting methodologies and insights can be effectively extended to fast-moving landslide phenomena. The central scientific objective was to define an integrated monitoring architecture for geotechnical applications, synergizing traditional sensing techniques with innovative distributed fiber-optic technologies. To this end, the innovative New Smart Hybrid Transducer (NSHT) was used, applied to monitoring in both geotechnical and structural fields. Exploiting Fiber Optic Sensing (FOS), this system was designed to overcome the critical limitations of current technologies, particularly regarding installation complexity, data reliability, and the standardization of production processes. Within the context of the thesis, the NSHT transducer was implemented as a Smart Extenso-Inclinometer (SEI) as a key functional tool for geotechnical monitoring of landslides and data acquisition. The findings demonstrate that integrating high-resolution geotechnical modelling with these sensing innovations significantly enhances the reliability and responsiveness of LEWS. Ultimately, this research provides a robust, scalable tool for the real-time monitoring of unstable slopes, offering a proactive approach to safeguarding populations and civil infrastructure exposed to geohazards on a global scale. The project includes PNRR research themes for the implementation of a system to prevent at national level hydrogeological risks that can involve populations and some territories. The thesis consists of five chapters. The first chapter introduces the methodological framework for landslide risk management, outlining the steps of analysis, assessment and management of slope instability. After examining the predisposing and triggering factors, the discussion focuses on the Risk Management phase, demonstrating how the integration of advanced geotechnical monitoring systems is crucial to optimize the effectiveness of Landslide Early Warning Systems (LEWS) and mitigate the socio-economic impact of landslides phenomena. The second chapter outlines the technological framework related to surface and deep geotechnical monitoring systems. Through the analysis of significant case studies, the performance of these methodologies is examined, highlighting their advantages, without however neglecting the intrinsic limitations and the critical issues in acquisition in complex geomorphological contexts. Moreover, the chapter, starting from the analysis of the state of the art, explores the advantages of distributed optical sensors over conventional systems, without neglecting the technical challenges still open in the current scientific panorama. The third chapter introduces the Smart Extenso-Inclinometer (SEI) as a pivotal instrument for establishing effective geotechnical monitoring systems. Developed to address the inherent limitations of current deep-seated monitoring technologies, the chapter details the system’s core components and outlines the data processing techniques. The fourth chapter presents the first geotechnical application of the proposed monitoring strategy, focusing on a case study characterized by complex slow-moving, deep-seated landslide kinematics. The chapter details the design and deployment of a monitoring system that integrates traditional instrumentation with innovative sensing solutions. The fifth chapter is dedicated to the analysis of a second case study, characterized by surface landslide phenomena, with faster kinematics than the site examined in the previous chapter. An integrated monitoring system has also been implemented in this context, based on the use of both traditional and innovative instrumentation.