Monitoring of ground and building settlements induced by tunneling based on terrestrial LiDAR data: a case study in Singapore

Researchers have developed a settlement monitoring method based on terrestrial LiDAR data. Algorithms are developed to automatically process the 3D point cloud data obtained from terrestrial LiDAR and obtain settlement values for grounds and buildings. The proposed technique was applied and validated in a region with on-going tunneling works in Singapore. In general, it is demonstrated that the LiDAR based settlement monitoring method is feasible in engineering practice, with measurement errors controlled within 2–3 mm, and has great potential to improve efficiency and reduce labor cost required by the traditional method.

Traffic congestion in big cities is becoming significant nowadays and leads to the development of underground transportation spaces, such as tunnels. Tunnel excavation will strongly affect the safety of existed subsurface structures or pedestrian and vehicle traffic. In recent years, monitoring of ground settlement with novel sensing techniques has attracted great attentions in engineering communities. Compared with traditional settlement measurement methods based on total stations, novel sensing techniques can help to reduce the labor force required with an acceptable accuracy.

The Light Detection And Ranging (LiDAR), also known as 3D laser scanning, is an emerging 3D sensing technology to acquire the geometric information of object surfaces. A LiDAR can measure the distance to a target by emitting laser beams and detecting the reflected signals from the target. As a result, a dense and accurate 3D point cloud (PC) dataset containing all the distance measurements will be generated. Based on the applications of LiDAR stated in available literatures, using LiDAR for monitoring ground settlements induced by tunnel excavation is therefore expected to be feasible.

In this research, a methodology to utilize LiDAR data for analyzing the ground settlement induced by tunnelling in an effective way is proposed. The methodology is adopted for measuring the ground settlement of a major road in Singapore. The settlement measurements from the proposed method are compared to the measurements from the traditional survey method, which is considered as the ground truth value. Two monitoring strategies including local scan-based method and full area registration-based method are also compared in the case study.

The proposed settlement monitoring method has four steps. Firstly, the point cloud data are acquired by a LiDAR at specific areas on different dates. Secondly, the acquired raw scans are registered before settlement calculation. Thirdly, to calculate the settlement of a certain area over a period, the two point cloud datasets acquired on different dates should be aligned based on the settlement-free region. The alignment process includes two steps: rough alignment and fine alignment. Lastly, the settlement of a certain area over a period is calculated by comparing the elevation values of two point cloud datasets at the settlement region.

The ground settlement along a major road in Singapore was monitored and measured based on LiDAR technique and the proposed method. Different scan strategies including local scan-based method and full area registration-based method were examined and compared with the results obtained from the traditional survey method. Results from the case study showed that LiDAR point cloud data are feasible for monitoring and measuring the ground and building settlements induced by tunnelling excavation at millimeter level, especially for the local scan-based method. Although the full area registration-based method has higher calculation efficiency, its measurement accuracy is not satisfactory.

This paper ”Monitoring of ground and building settlements induced by tunneling based on terrestrial LiDAR data: a case study in Singapore” was published in Smart Construction.

Zhang X, Li J, Chian SC, Wang Q. Monitoring of ground and building settlements induced by tunneling based on terrestrial LiDAR data: a case study in Singapore. Smart Constr. 2025(1):0003, https://doi.org/10.55092/sc20250003.