Abstract
Abstract. Technologically advanced strategies in infrastructural maintenance are increasingly required in countries such as Italy, where recovery and rehabilitation interventions are preferred to new works. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) techniques have been employed in recent years, achieving reliable outcomes in the identification of infrastructural instabilities. Nevertheless, using the InSAR survey exclusively, it is not feasible to recognize the reasons for such vulnerabilities, and further in-depth investigations are essential.The primary purpose of this paper is to predict infrastructural displacements connected to surface motion and the related causes by combining InSAR techniques and Machine Learning algorithms. The development and application of a Regression Tree-based algorithm have been carried out for estimating the displacement of road pavement structures detected by the Persistent Scatterer InSAR technique.The study area is located in the province of Pistoia, Tuscany, Italy. Sentinel-1 images from 2014 to 2019 were used for the interferometric process, and a set of 29 environmental parameters was collected in a GIS platform. The database is randomly split into a Training (70%) and Test sets (30%). With the Training set, through a 10-Fold Cross-Validation, the model is trained, validated, and the Goodness-of-Fit is evaluated. Also, with the Test set, the Predictive Performance of the model is assessed. Lastly, we applied the model onto a stretch of a two-lane rural road that crosses the area. Results show that the suggested procedure can be used for supporting decision-making processes on planning road maintenance by National Road Authorities.
Highlights
One of the most prominent challenges in infrastructure Pavement Management Systems (PMSs) is timely detection for applying preventive actions and early recovery
Through the combination of SAR-based monitoring devices and Machine Learning (ML) techniques, we suggested a procedure for providing a road maintenance strategy that accounting for exogenous events of the infrastructure, such as subsidence effects
And quantitatively speaking, the research has shown that Regression Tree-based models can be considered a satisfactory tool for the prediction of PS-Interferometric Synthetic Aperture Radar (InSAR)-based surface motion estimations
Summary
One of the most prominent challenges in infrastructure Pavement Management Systems (PMSs) is timely detection for applying preventive actions and early recovery. Accurate planning of infrastructure maintenance enhances the service life and reduce the total maintenance cost. SAR-based systems enable detection of surface motions on and around infrastructures. Such systems can increase the effectiveness of the maintenance planning (Fagrhi and Ozden, 2015). The scope of this research is to evaluate the possibility of using SAR-based systems for planning the strategy of infrastructure maintenance. In combination with SAR, we want to define a procedure that exploits the capability of advanced statistical modeling, such as Machine Learning (ML) techniques, that can associate conditioning factors with the target variable examined. The target variable is the surface motion detected by a SAR sensor, while factors are those related to exogenous events of the infrastructure. Exogenous events can cause modification in infrastructure conditions and are connected to major extreme natural events, such as earthquakes, landslides, subsidence, sinkholes, and floods
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