Monitoring and Comparative Analysis of Hohhot Subway Subsidence Using StaMPS-PS Based on Two DEMS

Abstract

The subway alleviates the traffic pressure in the city but also brings the potential risk of land subsidence. The land subsidence caused by the subway is a global problem that seriously affects the safety of subway operations and surrounding buildings. Therefore, it is very important to carry out long-term deformation monitoring on the subway system. StaMPS-PS is a time-series Interferometric Synthetic Aperture Radar (InSAR) technique that serves as an effective means for monitoring urban ground subsidence. However, the accuracy of external (Digital Elevation Models) DEM will affect the accuracy of StaMPS-PS monitoring, and previous studies have mostly used SRTM-1 arc DEM (30 m) as the external DEM. In this study, to obtain a more precise measurement of surface deformation caused by the excavation of the Hohhot subway, a total of 85 scenes of Sentinel-1A data from July 2015 to October 2021, as well as two different resolution digital elevation models (DEMs) (ALOS PALSAR DEM and SRTM-1 arc DEM), were used to calculate and analyze the subsidence along the subway line in Hohhot city. The StaMPS-PS monitoring results showed the ALOS PALSAR DEM, as an external DEM, had higher accuracy, and there was regional subsidence in both the construction processes of Line 1 and Line 2 of the Hohhot subway, with a maximum subsidence rate of −21.1 mm/year. The dynamic changes in subway subsidence were fitted using the Peck formula and the long short-term memory (LSTM) model. The Peck formula results showed the width and maximum subsidence of the settlement troughs gradually expanded during the construction of the subway. The predicted values of the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the LSTM model were less than 4 mm and 10%, respectively, consistent with the measured results. Furthermore, we discussed the factors that affect settlement along the subway line and the impact of two external DEMs on StaMPS-PS. The study results provide a scientific method for DEM selection and subsidence analysis calculations in the StaMPS-PS monitoring of urban subway subsidence.

Simulating the mine subsidence and deformations of highway using a stochastic model

“…Nowadays, surface subsidence monitoring is realized using advanced geoinformation technologies such as InSAR (Hejmanowski et al, 2019;Herrera et al, 2007), Aerial photogrammetry (Hindistan and Yetis, 2012), Remote Sensing (Vishwakarma et al, 2021;Bianchini et al, 2015;Zhao et al, 2023), GeoSLAM-based mobile laser scanning systems supported by GNSS (Singh et al, 2021;Dewez et al, 2017) and TLS (Lian et al, 2020;Gong et al, 2012). Rapid mapping and monitoring of surface subsidence and deformations in huge areas is possible whereby precise 3D model of the scanned egion is obtained.…”

The potential impact of rising sea levels and subsidence on coastal flooding along the northern coast of Saint-Louis, Senegal.

“…Yet it remains one of the biggest problems in coastal zones, especially in urban areas. Moreover, according to Zhao et al (2023), subsidence is becoming increasingly common in urban agglomerations and is worsening due to rising sea levels and accelerating construction.…”

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

“…Since that time, the role of new analysis methods has grown and been extensively studied, respectively. The development of the new analysis methods is evolving in two directions: structural analysis using methods of structural mechanics [32][33][34][35], and mathematical models based on machine learning technology achievements [36][37][38][39][40][41][42]. Applying the structural mechanics models is justified for cases of structures for which geometric and material properties are well known.…”

“…A similar task for dam deformations is addressed in [36,37], but using neural network simulation. Specifically, the question of urban land subsidence simulation, especially due to subway construction, is considered in the papers [42][43][44][45][46][47][48][49][50][51][52][53][54][55]. These works investigate interferometric SAR as an observation method.…”

“…These works investigate interferometric SAR as an observation method. For subsidence simulations along subway lines [42] and land subsidence [55,56] in general, the authors have suggested the LSTM neural network. This neural network is based on the recurrent neural network and is intended for predicting time series with long intervals [49].…”