By Nidhi DhullReviewed by Susha Cheriyedath, M.Sc.Oct 31 2024A recent article published in Buildings presented an Internet of Things (IoT)-based solution to detect grouting voids in tunnel construction using the Raspberry Pi microcomputer. This system is capable of real-time monitoring using embedded electrical wires in the secondary tunnel lining to measure conductivity and relate disruptions with unfilled voids.
Study: Raspberry Pi-Based IoT System for Grouting Void Detection in Tunnel Construction. Image Credit: Sidorov_Ruslan/Shutterstock.com
Background
Tunnels are critical elements of modern infrastructure, facilitating transportation across challenging terrains. Thus, ensuring the stability and safety of tunnels is very important. Tunnel construction is supported by primary and secondary lining. While the former provides immediate support to the surrounding rock mass, the latter offers long-term strength, waterproofing, and resistance to external pressures.
However, voids between the primary and secondary linings are a major challenge during tunnel construction. These voids can compromise the structure’s integrity, ultimately leading to cracking, water ingress, and even tunnel collapse.
Common methods to detect tunnel voids include pre-hole grouting, manual tapping, and ground-penetrating radar (GPR). However, these methods do not provide for consistent, real-time monitoring.
Automated, real-time solutions are required to monitor the grouting process during tunnel construction without interrupting workflows. Therefore, this study leveraged concrete conductivity to develop a novel detection system based on IoT and the Raspberry Pi microcomputer.
Methods
Electrical wires were embedded within the secondary lining structure during tunnel construction. The proposed system automatically detected voids behind the secondary lining by measuring electrical current flow through the grouting material (concrete) using the embedded wire network.
A void disrupted the current flow, allowing the system to identify areas requiring further grouting. Thus, the system ensured no overfilling or underfilling occurred, optimizing the use of materials and labor.
The Raspberry Pi was housed inside a waterproof enclosure. The entire system was mounted on the self-propelled tunnel formwork to ensure mobility as the construction progressed. The grouting void detection began when the formwork moved to a specific position and the system’s two wires connected to the pre-embedded wires in the secondary lining.
The Raspberry Pi was connected to the internet to transmit this information to a cloud-based server hosted on Alibaba Cloud, ensuring the responsiveness and reliability of the monitoring process. The cloud server kept a continuous record of the status of each section, creating a detailed, remotely accessible database for construction managers and engineers.
The performance of the developed system was demonstrated in a real-world tunnel construction project through large-scale field tests. The system’s void-detection accuracy in different tunnel geometries (straight, curved, and intersection) was compared with the results from manual inspections and GPR data.
Results and Discussion
The developed void detection system successfully indicated the status of the grouting process. However, relying solely on this system’s readings could not guarantee complete grout coverage. Thus, GPR scans were employed as a secondary validation method to ensure structural integrity and verify the completeness of the grout filling.
Tunnel section geometry and grouting pressure influenced the size and distribution of voids detected by the developed system. Increasing grouting pressure generally led to fewer and smaller voids. However, the tunnel geometry played a significant role in void detection. For instance, curved sections exhibited larger voids and more irregular grout flow despite high pressure due to their complex geometry.
Intersection areas were also problematic due to the complexity of grout flow. These were prone to forming larger voids, and even high pressure could not fully eliminate void formation due to the complex geometry. Among all, straight sections exhibited the best results in terms of void reduction.
The temperature and humidity variations inside the tunnel had a relatively minor impact on the overall void detection results, as the overall environmental conditions were within the acceptable ranges for the grout curing process.
The comparison between the GPR scans and the void detection system demonstrated the effectiveness and accuracy of the latter. While GPR scans were highly reliable in detecting voids and measuring their sizes, the detection system successfully identified the majority of voids, specifically in critical areas such as curves, joints, and intersections. Thus, GPR could serve as a secondary verification tool to identify any remaining minor voids.
Conclusion
Overall, the researchers successfully developed an IoT-based tunnel grouting void detection system using Raspberry Pi for tunnel construction monitoring. It offered a cost-effective, reliable, and scalable solution for detecting voids in real-time. This can be a significant advancement over traditional detection methods. Integration with cloud platforms for remote monitoring further enhanced the applicability of the developed system in large-scale infrastructure projects.
However, the system had certain limitations related to electromagnetic interference, which hindered the accurate detection of small or irregular voids. Additionally, scaling the system for broader applicability is complex. Therefore, the researchers suggest integrating complementary sensing technologies and data management methods to further strengthen the system’s performance.
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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