Across China, the rapid construction of high-speed railways has led to many routes passing through areas prone to ground subsidence, posing a significant threat to safe railway operations. This is especially true in the Taiyuan Basin, where groundwater overexploitation has caused persistent drops in water levels and triggered widespread ground subsidence. To address this challenge, a team of researchers has developed a novel approach that combines advanced satellite monitoring techniques and sophisticated data analysis models to track and predict ground deformation along high-speed rail lines.
The researchers used Click Here
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</a> technology to monitor ground subsidence in the Taiyuan Basin, focusing on the segment of the Taiyuan-Jiaozuo High-speed Railway (TJHR) that runs through the region. By processing 29 sets of data from the European Space Agency’s Sentinel-1A satellite, they were able to generate detailed maps of ground deformation patterns, identifying areas with significant subsidence rates of up to 82.63 mm per year.</p>
<p><figure class="wp-block-image size-large"><img decoding="async" src="https://famefreq.s3.us-east-2.amazonaws.com/1729417688_41598_2024_76485_Fig1_HTML.png" alt="" class="wp-image-1" /><figcaption class="wp-element-caption">Table 1 Comparison of PS-InSAR and land-state network GNSS continuously operating stations.</figcaption></figure>
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<p>To better understand the dynamics of ground deformation along the TJHR, the researchers selected 10 representative monitoring points and analyzed their deformation trends over time. They found that the cumulative subsidence within a 200-meter buffer of the railway centerline ranged from -37.58 to -89.19 millimeters, posing a clear threat to the railway’s structural integrity and safe operations.</p>
<p><h3>Predicting Ground Subsidence with Machine Learning</h3>
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<p>To address this challenge, the researchers turned to advanced data analysis techniques, combining the <b>Variational Modal Decomposition (VMD)</b> method with the <b>Long Short-Term Memory (LSTM)</b> neural network model. The VMD approach allowed them to break down the complex, non-linear ground deformation sequences into smoother, more manageable components, which were then individually predicted using the LSTM model.</p>
<p><figure class="wp-block-image size-large"><img decoding="async" src="https://famefreq.s3.us-east-2.amazonaws.com/1729417692_41598_2024_76485_Fig2_HTML.png" alt="figure 1" class="wp-image-2" /><figcaption class="wp-element-caption">Fig. 1</figcaption></figure>
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<p>The researchers further enhanced the predictive power of the LSTM model by integrating it with the <b>AdaBoost</b> algorithm, a powerful ensemble learning technique. This hybrid VMD-LSTMAda-LSTMAda model demonstrated impressive performance, with root-mean-square errors (RMSE) as low as 0.82 millimeters and mean absolute errors (MAE) of just 0.25 millimeters for the selected monitoring points.</p>
<p><h3>Ensuring the Safety of High-Speed Rail</h3>
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<p>The ability to accurately predict ground subsidence along high-speed rail lines is critical for ensuring the safety and reliability of these vital transportation networks. By combining the high-resolution monitoring capabilities of InSAR technology with advanced data analysis models, the researchers have developed a powerful tool that can help railway operators and engineers anticipate and mitigate the risks posed by ground deformation.</p>
<p><figure class="wp-block-image size-large"><img decoding="async" src="https://famefreq.s3.us-east-2.amazonaws.com/1729417695_41598_2024_76485_Fig3_HTML.jpg" alt="figure 2" class="wp-image-3" /><figcaption class="wp-element-caption">Fig. 2</figcaption></figure>
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<p>Looking ahead, the researchers suggest that further refinements to the VMD-LSTMAda-LSTMAda model, such as incorporating additional data sources and exploring ensemble techniques, could lead to even more accurate and robust predictions. Additionally, the application of this approach to other high-speed rail corridors and linear infrastructure projects could significantly enhance their long-term resilience and sustainability.</p>
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<p>Overall, this research represents a significant step forward in the effort to address the challenges of ground subsidence along high-speed railways, paving the way for safer, more reliable, and more sustainable transportation networks across China and beyond.</p>
<p>Author credit: This article is based on research by Yong-An Xue, You-Feng Zou, Hai-Ying Li, Wen-Zhi Zhang.</p>
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