Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.

Structural and stratigraphic control on hypogene dissolution in carbonate rocks: case studies of cave analogues and implications for karst reservoirs

The aim of this research is to improve the understanding of the factors that control the formation of karst porosity in hypogene settings and its associated patterns of void-conduit networks. Subsurface voids created by hypogene dissolution may span from few microns to decametric tubes providing interconnected conduit systems and forming highly anisotropic permeability domains in many reservoirs. Characterizing the spatial-morphological organization of hypogene karst is a challenging task that has dramatic implications for the applied industry, given that only partial data can be acquired from the subsurface by indirect techniques. Therefore, two outcropping cave analogues are examined: the Cavallone-Bove Cave in the Majella Massif (Italy), and the karst systems of the Salitre Formation (Brazil). In the latter, a peculiar example of hypogene speleogenesis associated with silicification has been studied, providing an analogue of many karstified reservoirs hosted in cherts or cherty-carbonates within mixed sedimentary sequences. The first part of the thesis is focused on the relationships between fracture patterns and flow pathways in deformed units in: 1) a fold-and-thrust setting (Majella Massif); 2) a cratonic block (Brazil). These settings represent potential playgrounds for the migration and accumulation of geofluids, where hypogene conduits may affect flow pathways, fluid storage, and reservoir properties. The results indicate that localized deformation producing cross-formational fracture zones associated with anticline hinges or fault damage zones is critical for hypogene fluid migration and karstification. The second part of the thesis deals with the multidisciplinary study of hydrothermal silicification and hypogene dissolution in Calixto Cave (Brazil). Petrophysical analyses and a geochemical characterization of silica deposits are used to unravel the spatial-morphological organization of the conduit system and its speleogenesis. The novel results obtained from this cave shed new light on the relationship between hydrothermal silicification, hypogene dissolution and the development of multistorey cave systems in layered carbonate-siliciclastic sequences.

Seismic vulnerability of buildings and non-structural elements: insights from the 2011 Lorca earthquake

This PhD dissertation presents a profound study of the vulnerability of buildings and non-structural elements stemming from the investigation of the Mw 5.2 Lorca 2011 earthquake; which constitutes one of the most significant earthquakes in Spain. It left nine fatalities due to falling debris from reinforced concrete buildings, 394 injured and material damage valued at 800 million euros. Within this framework, the most relevant initiatives concerning the vulnerability of buildings and the exposure of Lorca are studied. This work revealed two lines of research: the elaboration of a rational method to determine the adequacy of a specific fragility curve for the particular seismic risk study of a region; and the relevance of researching the seismic performance of non-structural elements. As a consequence, firstly, a method to assess and select fragility curves for seismic risk studies from the catalogue of those available in the literature is elaborated and calibrated by means of a case study. The said methodology is based on a multidimensional index and provides a ranking that classifies the curves in terms of adequacy. Its results for the case of Lorca led to the elaboration of new fragility curves for unreinforced masonry buildings. Moreover, a simplified method to account for the unpredictable directionality of the seism in the creation of fragility curves is contributed. Secondly, the characterisation of the seismic capacity and demand of the non-structural elements that caused most of the human losses is studied. Concerning the capacity, an analytical approach derived from theoretical considerations to characterise the complete out-of-plane seismic response curve of unreinforced masonry cantilever walls is provided; as well as a simplified and more practical trilinear version of it. Concerning the demand, several methods for characterising the Floor Response Spectra of reinforced concrete buildings are tested through case studies.

Integrating structural variant calling, annotation and prioritization into whole genome analysis workflows: a practical application in the molecular diagnosis of neurodevelopmental disorders

Background: WGS is increasingly used as a first-line diagnostic test for patients with rare genetic diseases such as neurodevelopmental disorders (NDD). Clinical applications require a robust infrastructure to support processing, storage and analysis of WGS data. The identification and interpretation of SVs from WGS data also needs to be improved. Finally, there is a need for a prioritization system that enables downstream clinical analysis and facilitates data interpretation. Here, we present the results of a clinical application of WGS in a cohort of patients with NDD. Methods: We developed highly portable workflows for processing WGS data, including alignment, quality control, and variant calling of SNVs and SVs. A benchmark analysis of state-of-the-art SV detection tools was performed to select the most accurate combination for SV calling. A gene-based prioritization system was also implemented to support variant interpretation. Results: Using a benchmark analysis, we selected the most accurate combination of tools to improve SV detection from WGS data and build a dedicated pipeline. Our workflows were used to process WGS data from 77 NDD patient-parent families. The prioritization system supported downstream analysis and enabled molecular diagnosis in 32% of patients, 25% of which were SVs and suggested a potential diagnosis in 20% of patients, requiring further investigation to achieve diagnostic certainty. Conclusion: Our data suggest that the integration of SNVs and SVs is a main factor that increases diagnostic yield by WGS and show that the adoption of a dedicated pipeline improves the process of variant detection and interpretation.