The primary goal of this research project is to enhance the safety and comfortability of High-Speed Railway (HSR) infrastructures by addressing key challenges associated with their maintenance and operation during regular public transportation service. The project aims to answer the following critical questions: 1) How can deep learning techniques be applied to detect and assess structural distresses in High-speed Railway infrastructures, such as cracks in concrete slabs, more accurately and efficiently? 2) What are the best practices for using deep learning to monitor and predict foundation settlements that may compromise the stability and comfort of HSR systems? During the course of the project, an innovative integration of advanced deep learning techniques, including computer vision, Large Language Models (LLMs), etc. will be developed to address the challenges in HSR infrastructure engineering monitoring and maintenance: 1) Deep learning methods: This involves the development of algorithms capable of analysing images and videos of railway infrastructures to detect and quantify cracks, deformations, and other structural issues. 2) Large Language Models (LLMs): These models will be used to process and analyze vast amounts of vibration and settlement data related to HSR infrastructures under long-term service. Students involved in this project will undertake a variety of tasks, including: 1) Data Collection and Pre-processing: Gathering and preparing large datasets from various sources, including field inspections, historical maintenance records, and sensor data. 2) Algorithm Development: Designing and implementing deep learning models for detecting structural issues and predicting future maintenance needs. This will involve programming, model training, and fine-tuning. 3) Simulation and Analysis: Using deep learning methods to simulate the behavior of High-speed railway infrastructures under different conditions and validate the models against real-world data. 4) Integration and Testing: Developing an integrated system, and testing this system in a controlled environment before deployment on actual HSR infrastructure systems. 5) Reporting and Presentation: Documenting findings, preparing technical reports, and presenting results to stakeholders and the broader scientific community. This project not only aims to improve the safety and comfort of HSR systems but also seeks to provide students with hands-on experience in cutting-edge technologies, preparing them for future careers in engineering and data science.