Work

Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images

Year: 2022

Type: article

Abstract: The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher-student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.

Source: Sensors

DOI: 10.3390/s22134964

Authors: Alireza Entezami, Carlo De Michele, Ali Nadir Arslan, Bahareh Behkamal

Institutions: Politecnico di Milano, Finnish Meteorological Institute

Language: English

Topic: Synthetic Aperture Radar (SAR) Applications and Techniques

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,513