Miele, Pietro (2022) Implementation of a Web-Based Spatial Decision Support System (WB-SDSS) for integrated structures and infrastructures monitoring. [Tesi di dottorato]


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Item Type: Tesi di dottorato
Resource language: English
Title: Implementation of a Web-Based Spatial Decision Support System (WB-SDSS) for integrated structures and infrastructures monitoring
Miele, Pietropietro.miele@unina.it
Date: 8 March 2022
Number of Pages: 152
Institution: Università degli Studi di Napoli Federico II
Department: Scienze della Terra, dell'Ambiente e delle Risorse
Dottorato: Scienze della Terra, dell'ambiente e delle risorse
Ciclo di dottorato: 34
Coordinatore del Corso di dottorato:
Morra, Vincenzophddistar@unina.it
Di Martire, DiegoUNSPECIFIED
Date: 8 March 2022
Number of Pages: 152
Keywords: Remote Sensing, DInSAR analysis, Web-based decision support system, infrastructures monitoring
Settori scientifico-disciplinari del MIUR: Area 04 - Scienze della terra > GEO/05 - Geologia applicata
Date Deposited: 18 Mar 2022 08:46
Last Modified: 28 Feb 2024 14:00
URI: http://www.fedoa.unina.it/id/eprint/14549

Collection description

Nowadays, cities are a complex mix of diverse ecosystems, institutions, assets and infrastructure. The rapid expansion of cities as the global population grows is exposing more people and economies to the risk of disasters and the effects of them (Ritchie and Roser 2018). Disruption to one part of the city ecosystem, such as transport networks, water supply, drainage or energy systems, affects both urban management, local economies and the delivery of key services, as well as the lives and livelihoods of the people within these cities. In the last years, the widespread deterioration and recent collapses of bridges (Tan et al. 2020), dams (Silva Rotta et al. 2020; Parente et al. 2021), tunnels (Cheng et al. 2020; Spyridis and Proske 2021) and other key services have highlighted the importance of structural health monitoring (SHM) (Faber and Thöns 2014; Mahmud et al. 2018) in supporting maintenance decisions and preventing collapses. Landslides and other geologic hazards cause several billion dollars of damage every year in the United States, resulting also in environmental damage and societal disruption (Schuster & Highland 2001; Spiker & Gori 2003). Every two years, in Italy, the Institute for Environmental Protection and Research (ISPRA) presents the National Report on the situation of geo-hydrological instability in the country. The latest available data are those presented to the Chamber of Deputies in July 2018, according to which 91% of Italian municipalities are at risk (88% in 2015) and more than 3 million households reside in these highly vulnerable areas. Overall, 16.6% of the national territory is mapped in the highest hazard classes for landslides and floods (50 thousand km2). Almost 4% of Italian buildings (over 550 thousand) are located in areas of high and very high landslide hazard and more than 9% (over 1 million) in flood zones in the medium scenario (ISPRA, 2018). In early 2019, the plan for the mitigation of geo-hydrological risk named 'Proteggi Italia' has been presented by the Italian government. The plan provides 11 billion euros for interventions against the instability of the territory in the three-year period 2019-2021. The mission aims to carry out urgent interventions in a short time to secure the strategic parts of the country and therefore also the infrastructure network. In fact, the Italian context is characterized by different geological and geomorphological settings and often the functionality of the transport system is affected by natural phenomena as well as earthquakes, subsidence and landslides. Rich information exists on landslides, their physical characteristics and consequences, while little is known on their economic impact. Thus, risk assessment and prevention are the key steps for sustainable land management and territory planning. The transport network in Italy includes the following infrastructures: 282 ports, a rail network of 16799 km, a road network (state, regional, provincial and municipal roads) of 167565 km, a motorway network of 6,185 km and 52 ENAC (Civil Aviation Authority) airports (ISPRA, 2021). Current standard practice for the monitoring of bridges, viaducts and other structural elements of road networks in most countries is to periodically schedule visual inspections, relying on inspectors to be able to spot signs of problems or unusual behaviours before they reach a catastrophic stage. Considering the aforementioned dimension of the Italian road network, the traditional monitoring procedure results in high costs and a very long period to cover all the structural elements of the infrastructure system. Given the fact of the transportation network ageing, deterioration and maintenance presents a global problem. The topic of infrastructure health monitoring and sharing via Web-Based Spatial Decision Support System (WB-SDSS) platform will be the focus of this research. In particular, the present study is focused on Remote Sensing techniques potential as additional monitoring tools in terms of ancillary data for conventional ground monitoring systems in preliminarily phenomena evaluations. Remote Sensing disciplines are based on the acquisition of sensors located at high distances from the target object. Passive sensors onboard satellites and aircraft use the Sun as a source of illumination. When they use their own source of illumination and measure the reflected energy, they are called active sensors. Such systems use a radar antenna that is able to produce and receive electromagnetic signals. In fact, modern satellite platforms are equipped with Synthetic Aperture Radar (SAR) technologies which can ensure metric ground resolution. Among the various Earth Observation mission equipped with active sensors, another important one is the ICESat NASA/GSFC mission (started in January 2003) within the ESE (Earth Science Enterprise) program (http://icesat.gsfc.nasa.gov). The prime objective of the mission is to monitor the mass balance of the polar ice sheets and their contributions to global sea level change. Secondary goals are to measure cloud heights and the vertical structure of clouds and aerosols in the atmosphere, further to measure roughness, reflectivity, vegetation heights, snow-cover, and sea-ice surface characteristics, and to map topography of land surfaces (Schutz et al. 2005). The ICESat satellite was equipped with three Geoscience Laser Altimeter System (GLAS), a lidar that combined the accuracy of a double wavelength lidar for terrestrial investigations with the sensitivity of an atmospheric lidar. During the ICESat’s orbits, GLAS, which emitted infrared and visible pulses at wavelengths of 1064 nm and 532 nm, analyzed a series of 70-meter diameter areas, about 170 meters apart along the satellite’s ground track. Started in 2018, ICESat-2 is a NASA follow-up mission to ICESat with the goal to continue measuring and monitoring the impacts of the changing environment. The ICESat-2 observatory contains a single instrument, an improved laser altimeter called ATLAS (Advanced Topographic Laser Altimeter System). The main goal of ICESat-2 mission, with high altimetric fidelity, is to provide high-quality topographic measurements that allow estimates of ice sheet volume change (McLennan 2010). High-accuracy altimetry will also prove valuable for making long-sought repeat estimates of sea ice freeboard and hence sea ice thickness change, which is used to estimate the flux of low-salinity ice out of the Arctic basin into the marginal seas. Altimetry is the best (and perhaps only) technique for change studies, because sea ice areas and extends have been well observed from space since the 19070s and significant trends have been shown, but there is no such record for sea ice thickness. In the last decades, many scientific contributions have highlighted how the Interferometry SAR (InSAR - Franceschetti et al. 1992) technique demonstrates a strong potential for the detection of landslides (Galli et al. 2008; Cigna et al. 2012; Bovenga et al. 2012; Confuorto et al. 2017; Novellino et al. 2021), particularly slow and very slow mass movements (Cascini et al. 2009; Herrera et al. 2013; Huang et al. 2019) and earth surface deformations in general. Considering the large amount of available data, it is useful to implement appropriate IT architectures to organize, visualize and share this spatial data. In this way, a synoptic view of the context to be monitored could be achieved. This information is accessible on a web platform and could be helpful for administrative authorities in the decision-making processes as demonstrated by different applications (Maina et al. 2014; Modica et al. 2016; Buonanno et al. 2019; Yang and Frangopol 2019; Kuang et al. 2019). Moreover, over the past thirty years, SAR sensors have greatly improved in terms of quality and numbers of acquisition: many satellite constellations have been launched starting from ERS 1-2 and ENVISAT (European Space Agency), RADARSAT 1-2 (Canadian Space Agency), TerraSAR-X (DLR), Cosmo-SkyMed (Italian Space Agency) and Sentinel Mission (European Space Agency), the latters with very short revisiting time (about 6 days). This has allowed to develop image processing algorithms which produce reliable velocity maps and temporal series of deformation also considering available archive imagery (since 1992). Therefore, InSAR products validation procedures are based on the comparison with traditionally acquired field surveys data. During the testing step of the research, the A16 Highway (Southern Italy) has been discretized considering deformations recorded both by remote sensed data and in-situ measurements (inclinometers, rainfall data, GPS/GNSS data). Concerning the modeling of the considered data, a specific algorithm, based on the assumptions explained in the I-Pro_MONALISA approach (Infante et al., 2019), has been implemented for both the server and client sides. Also, the official landslide inventory map (LIM) released by Hydrographic Basin Authority has been considered and included in the platform. This methodology has made it possible to rapidly identify road sections with significant deformation allowing to optimize maintenance times ensuring the correct and constant highway functionality.


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