Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques

Abstract

Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability.

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

“…If the R dm < 0, the SCS and MUCS indicate evidence conflict. The similarity metric is calculated and the weight of all evidence is calculated by the similarity metric (in formula (11)). (2) In Formula ( 12) and ( 13), the conflict evidence is corrected by weight.…”

“…to as Formulas (3).P m : the MUCS evidence composed of single-phase observation P mo to as Formulas (4). ω j : the calculated weight as (11). P do to ′ : the single-phase SCS evidence based 3).…”

“…With the development of sensor technology, the RS data have found extensive applications in disaster monitoring [2][3][4][5][6][7], precision agriculture [8,9], environmental risk assessment [10,11], and oil spill detection [12]. Furthermore, leveraging the repeated earth observations facilitated by satellites, multi-temporal RS data have been utilized to enhance land cover classification accuracy [13][14][15][16][17][18][19][20][21], object recognition [22][23][24], as well as disaster monitoring and assessment [25][26][27].…”

Analysis of Overpass Displacements Due to Subway Construction Land Subsidence Using Machine Learning

“…However, the authors solely considered GNSS data, without additional parameters. The research [58] deals with various machine learning techniques to rule out the temperature effect from monitoring results. The main subject of the paper is bridge monitoring, but without prediction model development.…”