Chinese scientists have proposed a new use for the Raspberry Pi computer in monitoring the state of railway tunnels. The system developed by the scientists is capable of detecting voids in the cladding of tunnels, which can help prevent potential damage or collapse.
According to the authors of the study, railway tunnels are typically constructed using primary and secondary cladding, with the secondary cladding serving to enhance the structure and reduce the impact of local loads. However, voids can develop within the cladding due to poor-quality construction or changes in geological conditions, which can compromise the integrity of the tunnel.
Previously, manual inspections involving tapping of the tunnel walls and the use of georadars were employed to assess the condition of tunnels. Despite the high accuracy of georadars, continuous monitoring was not possible and the devices were quite costly. To address this issue, the scientists proposed a system that measures concrete electro-conductivity as an indicator of voids.
The system utilizes the Raspberry Pi, which is equipped with 40 GPIO contacts suitable for such tasks. Wires are integrated into the cladding of a newly constructed 600-meter-long tunnel, creating a circuit between the current and the ground. In the presence of a void, the circuit remains open, and the Raspberry Pi does not register any current flow. Once the void is filled, the current resumes flowing, indicating the completion of the process. Additionally, a temperature sensor is integrated into the system to ensure more precise data collection.
Given the delicate nature of the Raspberry Pi, the scientists utilized waterproof enclosures to protect the devices from underground conditions. Data collected is transmitted to the cloud via a 5G connection, where it is stored in a database for real-time monitoring and analysis. The authors suggest that in the future, machine learning can be applied to the data to create digital tunnel models and facilitate real-time decision-making.
Comparing the data obtained from the Raspberry Pi system with georadar results, the scientists found that the system effectively detects voids, although it may be less accurate in identifying small or irregularly shaped defects.