nullNon-invasive DInSAR monitoring of ground subsidences induced by tunnelling excavation in urban areas
Non-invasive DInSAR monitoring of ground subsidences induced by tunnelling excavation in urban areas
Big cities future plans usually require the construction of large underground infrastructures, in order to ensure proper communication and optimize urban use. Monitoring ground subsidences is therefore one of the main challenges in changing urban environments. Current conventional monitoring techniques are very accurate (topography, levelling, etc.) although they are quite expensive and limited to small areas. For these reasons, applications based on complementary techniques applied to wide areas and capable of analyzing time series and with positive relation profit/cost are being developed.
Since 2000 satellite radar techniques have become a complementary method for measuring ground surface displacements. Among these, differential SAR interferometry (DInSAR) has shown the capability for successfully measuring small displacement of structures with millimetric precision. DInSAR applied to measure surface subsidence has several advantages including large area coverage with a single image (10,000 km2), lower cost per m2 compared to conventional techniques and the possibility of obtaining data before, during and after the construction or phenomenon that is being studied. In this case, a total of 26 images from the Envisat satellite were used from August 2003 to April 2008.
An overall RMSE of deformation between 2.6mm and 3.5mm was obtained. The results were validated with more than 1500 field measurements made from leveling reference points and strips. The capabilities of the technique to assess deformations have been proven and, it is important to take into account that the number of controlled buildings when using the PSI technique dramatically increases when compared to the on ground techniques. Differential interferometry is a valuable tool to complement in-situ monitoring techniques in tunneling works.
The integration of artificial intelligence (AI) algorithms into the DInSAR deformation time series analysis, together with other technologies such as cloud computing and online interactive data analysis & visualization to early detect anomalies would allow increasing: i) Security and reduction of incidents through the improvement of early warning systems ii) Exploitation efficiency and preventive maintenance of the infrastructure iii) Infrastructure profitability due to the reduction of in-situ control instrumentation costs iv) Improving maintenance efficiency in all the phases of the infrastructure