In the bustling heart of Shanghai, where the metro system is a lifeline for millions, a groundbreaking approach to predicting foundation pit deformation is emerging, promising to revolutionize construction safety and efficiency. Di Wu, a researcher from Shanghai Shentong Metro Co., Ltd., has pioneered a method that leverages advanced deep learning techniques to monitor and predict the complex behaviors of foundation pits during excavation. This innovation, detailed in a recent study published in ‘Chengshi guidao jiaotong yanjiu’ (translated to English as ‘Urban Rail Transit Research’), could significantly impact the construction industry, particularly in urban infrastructure projects.
Wu’s research focuses on the Longdong Avenue Station of Shanghai Metro Line 21, a project that exemplifies the challenges of urban excavation. Traditional methods of predicting foundation pit deformation often fall short in capturing the intricate spatiotemporal dynamics at play. “The conventional approaches have limitations in handling the complex evolution of deformation over time and space,” Wu explains. “This is where our method stands out.”
The proposed solution employs a ConvLSTM (convolutional long short-term memory) network, a type of deep learning model that excels at processing sequential data with spatial dependencies. By analyzing monitoring data from three critical points in the foundation pit, the model accurately predicts horizontal displacement of the underground diaphragm wall during different excavation stages. The results are impressive, with root mean square error (RMSE) values as low as 1.20 mm at one of the monitoring points, demonstrating the model’s precision.
The commercial implications of this research are substantial. Accurate prediction of foundation pit deformation can prevent costly delays and ensure the safety of construction workers and the surrounding infrastructure. “This method not only enhances safety but also optimizes the construction process, reducing potential economic losses,” Wu notes. For the energy sector, which often involves large-scale excavation and underground construction, this technology could be a game-changer, ensuring the stability and safety of critical infrastructure projects.
The study’s findings suggest that the ConvLSTM model can identify key deformation features such as maximum displacement locations and inflection points, providing real-time monitoring and early warning capabilities. This predictive power could transform how construction projects are managed, particularly in densely populated urban areas where the margin for error is minimal.
As the construction industry continues to evolve, the integration of advanced technologies like deep learning will play a pivotal role in enhancing safety and efficiency. Wu’s research is a testament to the potential of these technologies, offering a glimpse into a future where data-driven decisions are the norm. “Our method provides a robust framework for predicting foundation pit deformation, which can be adapted to various construction scenarios,” Wu concludes.
With the publication of this research in ‘Urban Rail Transit Research’, the industry now has a powerful tool to address one of its most pressing challenges. As cities continue to grow and infrastructure projects become more complex, the need for accurate, reliable prediction methods will only increase. Wu’s work not only meets this need but also sets a new standard for the future of construction safety and efficiency.