A new powerful method deciding how to prioritize asset repair needs was described in a recent scientific study by the University of Salerno. It points out the methodological steps to be followed for rapid assessment at scale, identifying the most appropriate Subsidence Related Intensity parameters (SRI) to create Empirical Fragility Curves for probabilistic geotechnical forecasting models. The research area was in The Netherlands, a low-lying river delta close to the North Sea. It has several distinct geographic regions determined by its dominant soil profile. The subsoil consists of highly compressible soils in the researched western and northern regions.

Probabilistic geotechnical forecasting models

In addition to regular techniques like leveling and GNSS (GPS), useful information on displacement rates can be derived from advanced differential interferometric techniques (DInSAR). These displacement cates can be used in probabilistic geotechnical forecasting models.

In this study, 706 masonry buildings with shallow and mainly wooden piled foundations, belonging to four municipalities, were investigated. The municipalities are Zaanstad, Rotterdam, Schiedam, and Dordrecht. The study was led by Dario Peduto, Associate Professor in Geotechnics at the Department of Civil Engineering, assisted by Gianfranco Nicodemo, Antonio Marchese and Settimio Ferlisi, all associated to the University of Salerno Italy, and Mandy Korff, Delft University of Technology, Faculty of Civil Engineering and Geosciences and Deltares.

Probabilistic approaches like empirical fragility curves are particularly promising provided that a comprehensive dataset for both the subsidence-related intensity parameters and the corresponding damage severity to buildings is available. Fragility is in general the susceptibility of a structure to suffer a loss due to external events. Mathematically, this fragility curve provides the probability that a structure suffers a certain degree of damage for a given ground motion intensity. These curves derived from statistical data of damage are Empirical Fragility Curves and are particularly promising. A comprehensive dataset for both the SRI parameters and the corresponding damage severity to buildings is key to creating a valid curve.

Subsidence forecasting

Subsidence causes damage to affected facilities. For this reason, scientists, engineers, politicians, and civilian communities are interested in studies aimed at predicting the consequences of damage to buildings in subsiding areas. As for the DInSAR data used in the research, they were provided by SkyGeo within the advanced Antares platform. For this research we provided iterative improved time series, post-processing geostatistics, visualization and interpretation support. The 4 different municipalities all have significant local influencers in the forecasting model, from subsidence by groundwater lowering, clay and peat soils, sandy soils, shallow foundations, and wooden piled foundations.

Subsidence affected Soft soil regions

From regional authorities to Asset Managers, engineers around the world affected by soft soil subsidence can use the methodology to their advantage. Read the full article here https://doi.org/10.1016/j.sandf.2018.12.009. For subsidence monitoring projects, we’ve gathered great InSAR cases for your convenience and can configure custom services for probabilistic insights.