Abstract
Structural failure prevention is a crucial issue in civil engineering. The causes of structure or infrastructure collapse include phenomena that slowly deform the ground and could affect the stability of foundations such as differential settlements, subsidence, groundwater changes, slope failure, or landslides. When large urban areas need to be monitored, such phenomena are hard to be mapped by means of classical structural health monitoring methods due to the unaffordable quantity of in situ measurements these methods would entail. A very effective alternative is exploiting multitemporal interferometric synthetic aperture radar (MT-InSAR) displacement timeseries which would enable the monitoring of wide geographical areas over a weekly basis and extended spatial coverage. Analyzing the enormous amount of data produced by MT-InSAR may help to assess the time evolution of phenomena but can barely highlight “anomalous” ground deformations in time, to prevent likely structural failure. This paper proposes a method which analyzes the InSAR data through an unsupervised learning paradigm with the purpose of detecting critical events at their early stage. On the basis of similarities among time sequences, this method allows the finding of precursors of anomalous ground settlement behaviors, the correct framing of which should be directed to specialist evaluation and in situ inspections.
Highlights
Structural integrity and collapse prevention are very important aspects of civil engineering
This paper presented a method to monitor ground surface displacements in urban areas with the purpose of forecasting anomalous behaviors from their early occurrence
Through training an self-organized map (SOM) neural network, which highlights similarities among recorded timeseries, the method analyzes datasets of MT-InSAR timeseries recorded at hundreds of thousands of geographical points and provides warning signals according to a preset alert criterion
Summary
Structural integrity and collapse prevention are very important aspects of civil engineering. Ground subsidence [4], debris flow, excavation and tunneling activities [5], groundwater changes, slope failure, problematic foundation soils [6], bad interaction between soil and foundation, landslide due to volcanic activity or to pipes that burst, and liquefaction of soil after strong ground motions [7] are just some instances of phenomena that may affect the stability of structures and infrastructures. Regardless of the causes and the likely solutions of a given critical phenomenon, a preliminary monitoring stage is essential to prevent catastrophic events. For this purpose, a largescale continuous monitoring of time-evolving phenomena that might affect the stability of engineering facilities in urban areas would be very useful
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