Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data

2025

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 9582-9592 (2025)

As a crucial component of the transportation infrastructure, the health of bridge plays a direct role in the traffic safety. Over time, gradual structural deformation can compromise a bridge's stability and safety. Therefore, accurately predicting bridge deformation is essential for analyzing its causes and detecting potential safety hazards in a timely manner. Satellite-based synthetic aperture radar interferometry (InSAR) technology, which detects deformation at millimeter-scale precision over large areas, offers significant advantages in monitoring bridge deformation. However, most existing time-series deformation prediction methods based on InSAR data primarily focus on land subsidence. Given that bridge is complex, singular structures with unique spatial-temporal characteristics, existing methods designed for land subsidence are not directly applicable to bridge deformation prediction. To address this challenge, we propose a novel K-shape and complete linkage hierarchical cluster long short-term memory (KCC-LSTM) approach for predicting bridge deformation based on time-series InSAR data. The approach initially combines two machine learning based clustering algorithms, K-Shape for better capturing shape features of time series and complete linkage hierarchical clustering combined with spatial geographic location captures the spatial characteristics of time series to derive clusters with unique spatiotemporal deformation behavior, improving clustering accuracy and spatiotemporal correlation. Clustering results generated from this unsupervised machine learning approach are later used as training labels to develop long short-term memory (LSTM) networks. We validate the proposed approach using time-series data from 100 X-band TerraSAR-X images, acquired from 13 April 2010 to 13 December 2019. Our results demonstrate that compared to standard LSTM, the proposed approach reduces root mean square error of Bridge 1 from 3.6 to 0.5 mm and Bridge 2 from 3.6 to 1.3 mm, improving prediction accuracy. The results underscore the effectiveness of the KCC-LSTM model in predicting deformation in complex infrastructure, such as bridge.

article

English

IEEE, 2025.

Bridge deformation prediction, long short-term memory (LSTM), spatiotemporal clustering, time-series synthetic aperture radar interferometry (InSAR), Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809