By Nidhi DhullReviewed by Susha Cheriyedath, M.Sc.Oct 31 2024A recent study in Buildings has introduced an IoT-based solution for detecting grouting voids in tunnel construction. Utilizing the Raspberry Pi microcomputer, this system monitors conductivity in real-time through embedded electrical wires in the tunnel’s secondary lining, identifying potential voids through disruptions in measurement.
Study: Raspberry Pi-Based IoT System for Grouting Void Detection in Tunnel Construction. Image Credit: Sidorov_Ruslan/Shutterstock.com
Background
Tunnels are critical components of modern infrastructure, enabling transportation across difficult terrains. Their stability and safety are essential, supported by the use of primary and secondary linings in tunnel construction. The primary lining offers immediate structural support to the surrounding rock mass, while the secondary lining provides enhanced long-term durability, waterproofing, and resistance to external pressures.
Voids between the primary and secondary linings present a significant challenge, as they can compromise structural integrity and lead to issues such as cracking, water ingress, and, in severe cases, tunnel collapse.
Traditional methods for detecting these voids, including pre-hole grouting, manual tapping, and ground-penetrating radar (GPR), are limited in their capacity for continuous, real-time monitoring.
To overcome this limitation, automated, real-time monitoring solutions are necessary to ensure thorough grouting without interrupting construction workflows. This study introduces a novel detection system that leverages concrete conductivity, IoT technology, and the Raspberry Pi microcomputer to provide precise, non-intrusive, and continuous monitoring of potential voids during tunnel construction.
Methods
This study developed and tested an IoT-based system for real-time detection of grouting voids behind the secondary tunnel lining.
To enable continuous monitoring, electrical wires were embedded within the secondary lining structure during construction. The system detected voids by measuring electrical current flow through the grouting material (concrete), with disruptions in current indicating areas requiring additional grouting. This approach optimized material and labor usage by preventing both overfilling and underfilling.
A Raspberry Pi microcomputer served as the system’s processing unit, housed in a waterproof enclosure and mounted on self-propelled tunnel formwork to ensure mobility throughout the construction process. Detection was initiated once the formwork reached specific positions, where the Raspberry Pi’s wires connected to the pre-embedded wires in the secondary lining.
The Raspberry Pi was internet-enabled to transmit data to a cloud-based server hosted on Alibaba Cloud, allowing for responsive, reliable monitoring. The cloud server continuously recorded the status of each section, creating a detailed, remotely accessible database for construction managers and engineers.
The system’s effectiveness was demonstrated in a real-world tunnel construction project through extensive field tests. Void detection accuracy was evaluated across different tunnel geometries—such as straight sections, curves, and intersections—and validated against manual inspection and GPR data, showcasing its reliability in diverse settings.
Results and Discussion
The void detection system developed in this study effectively monitored the grouting process, indicating the presence and distribution of voids in real-time. However, relying solely on the system’s readings did not guarantee full grout coverage; therefore, GPR scans were used as a secondary validation method to ensure structural integrity and confirm complete grout filling.
The geometry of the tunnel sections and the grouting pressure influenced the void size and distribution detected by the system. Higher grouting pressures generally resulted in fewer and smaller voids. However, tunnel geometry was a significant factor. Curved sections, for example, showed larger voids and more irregular grout flow due to their complex shape, even at high pressures.
Intersection areas also presented challenges for complete grout coverage due to the intricate grout flow patterns, leading to larger voids that could not be entirely resolved, even with increased pressure. In contrast, straight sections exhibited the most favorable results, with minimal void formation under consistent grouting pressure.
Environmental factors such as temperature and humidity inside the tunnel had a minor effect on detection accuracy, as they remained within acceptable ranges for the grout curing process.
The system’s results were cross-referenced with GPR scans, highlighting its accuracy and reliability. While GPR scans offered high precision in void detection and sizing, the developed system was able to identify most voids, especially in critical areas like curves, joints, and intersections. This suggests that while the detection system is effective, GPR remains a valuable secondary tool to verify smaller residual voids.
Conclusion
In summary, this study successfully developed an IoT-based tunnel grouting void detection system utilizing Raspberry Pi for real-time monitoring in tunnel construction. This system provides a cost-effective, reliable, and scalable alternative to traditional void detection methods, with the added benefit of remote monitoring through cloud integration, making it well-suited for large-scale infrastructure projects.
Despite its strengths, the system has limitations, particularly in handling electromagnetic interference, which can affect the accuracy of detecting smaller or irregular voids. Additionally, scaling the system for broader applications presents challenges. To address these, the researchers recommend incorporating complementary sensing technologies and advanced data management strategies to enhance the system’s accuracy and adaptability in diverse construction environments.
Journal Reference
Luo, W., Zheng, J., Miao, Y., & Gao, L. (2024). Raspberry Pi-Based IoT System for Grouting Void Detection in Tunnel Construction. Buildings, 14(11), 3349. DOI: 10.3390/buildings14113349, https://www.mdpi.com/2075-5309/14/11/3349
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