In bridge structure monitoring and evaluation, deformation data serve as a crucial basis for assessing structural conditions. Different from discrete monitoring points, spatially continuous deformation modes provide a comprehensive understanding of deformation and potential information. Terrestrial laser scanning (TLS) is a three-dimensional deformation monitoring technique that has gained wide attention in recent years, demonstrating its potential in capturing structural deformation models. In this study, a TLS-based bridge deformation mode monitoring method is proposed, and a deformation mode calculation method combining sliding windows and surface fitting is developed, which is called the SWSF method for short. On the basis of the general characteristics of bridge structures, a deformation error model is established for the SWSF method, with a detailed quantitative analysis of each error component. The analysis results show that the deformation monitoring error of the SWSF method consists of four parts, which are related to the selection of the fitting function, the density of point clouds, the noise of point clouds, and the registration accuracy of point clouds. The error caused by point cloud noise is the main error component. Under the condition that the noise level of point clouds is determined, the calculation error of the SWSF method can be significantly reduced by increasing the number of points of point clouds in the sliding window. Then, deformation testing experiments were conducted under different measurement distances, proving that the proposed SWSF method can achieve a deformation monitoring accuracy of up to 0.1 mm. Finally, the proposed deformation mode monitoring method based on TLS and SWSF was tested on a railway bridge with a span of 65 m. The test results showed that in comparison with the commonly used total station method, the proposed method does not require any preset reflective markers, thereby improving the deformation monitoring accuracy from millimeter level to submillimeter level and transforming the discrete measurement point data form into spatially continuous deformation modes. Overall, this study introduces a new method for accurate deformation monitoring of bridges, demonstrating the significant potential for its application in health monitoring and damage diagnosis of bridge structures.

Extreme loadings, a hostile environment and dangerous operation lead to the unsafe state of bridges under construction, especially large-span bridges. Global Navigation Satellite Systems (GNSS) tend to be the best choice for real-time deformation monitoring due to the significant advantage of automation, continuation, all-weather operation and high precision. Unfortunately, the traditional geodetic GNSS instrument with its high price and large volume is limited in its applications. Hence, we design and develop low-cost GNSS equipment by simplifying the monitoring module. The performance of the proposed solution is evaluated through an experimental dynamic scenario, proving its ability to track abrupt deformation down to 3–5 mm. We take Chongqing Guojiatuo Suspension Bridge in China as a case study. We build a real-time low-cost GNSS monitoring cloud platform. The low-cost bridge GNSS monitoring stations are located at the top of the south and north towers, midspan upstream and downstream respectively and the reference station is located in the stable zone 400 m away from the bridge management buildings. We conducted a detailed experimental assessment of low-cost GNSS on 5 April and a real-time deformation detection experiment of the towers and main cables during the dynamic cable saddle pushing process on 26 February 2022. In the static experiment, the standard deviation of the residual using the multi-GNSS solution is 2 mm in the horizontal direction and 5 mm in the vertical direction. The multi-GNSS solution significantly outperforms the BDS/GPS single system. The dynamic experiment shows that, compared with the movement measured by the robotic total station, the horizontal error of the south tower and north tower measured by low-cost GNSS is below 0.005 m and 0.008 m respectively. This study highlights the potential of low-cost GNSS solutions for Structural Health Monitoring (SHM) applications.

The extraction of landslide deformation using terrestrial laser scanning (TLS) has many important applications. The landslide deformation can be extracted based on a digital terrain model (DTM). However, such methods usually suffer from the ill-posed problem of a multiplicative error model as illustrated in previous studies. Moreover, the edge drift of commonly used spherical targets for point cloud registration (PCR) is ignored in the existing method, which will result in the unstable precision of the PCR. In response to these problems, we propose a method for extracting landslide deformations from TLS data. To archive the PCR of different period point clouds, a new triangular pyramid target is designed to eliminate the edge drift. If a fixed target is inconvenient, we also propose a PCR method based on total station orientation. Then, the use of the Tikhonov regularization method to derive the weighted least squares regularization solution is presented. Finally, the landslide deformation is extracted by DTM deference. The experiments are conducted on two datasets with more than 1.5 billion points. The first dataset takes Lashagou NO. 3 landslide in Gansu Province, China, as the research object; the point cloud data were collected on 26 February 2021 and 3 May 2021. The registration accuracy was 0.003 m based on the permanent triangular pyramid target and 0.005 m based on the total station orientation. The landslide deforms within 3 cm due to the ablation of the frozen soil. The second dataset is TLS data from the Lihua landslide in Chongqing, China, collected on 20 April 2021 and 1 May 2021. The overall deformation of the Lihua landslide is small, with a maximum value of 0.011 m. The result shows that the proposed method achieves a better performance than previous sphere-based registration and that the weighted least square regularization iterative solution can effectively reduce the ill-condition of the model.

The extraction of building information with terrestrial laser scanning (TLS) has a number of important applications. As the density of projected points (DoPP) of facades is commonly greater than for other types of objects, building points can be extracted based on projection features. However, such methods usually suffer from density variation and parameter setting, as illustrated in previous studies. In this paper, we present a building extraction method for single-scan TLS data, mainly focusing on those problems. To adapt to the large density variation in TLS data, a filter using DoPP is applied on a polar grid, instead of a commonly used rectangular grid, to detect facade points. In DoPP filtering, the threshold to distinguish facades from other objects is generated adaptively for each cell by calculating the point number when placing the lowest building in it. Then, the DoPP filtering result is further refined by an object-oriented decision tree mainly based on grid features, such as compactness and horizontal hollow ratio. Finally, roof points are extracted by region growing on the non-facade points, using the highest point in each facade cell as a seed point. The experiments are conducted on two datasets with more than 1.7 billion points and with point density varying from millimeter to decimeter levels. The completeness and correctness of the first dataset containing more than 50 million points are 91.8% and 99.8%, with a running time of approximately 970 s. The second dataset is Semantic3D, of which the point number, completeness and correctness are about 1.65 billion, 90.2% and 94.5%, with a running time of about 14,464 s. The test shows that the proposed method achieves a better performance than previous grid-based methods and a similar level of accuracy to the point-based classification method and with much higher efficiency.

Digital Twin (DT) plays a crucial role in intelligent bridge management, and the geometric DT (gDT) serves as its foundation. Notably, the fast and high-precision generation of bridge gDT models has gained increasing attention. This research presents a method for generating high-precision and fast RC bridges with chambers for gDT using terrestrial laser scanning. The method begins with a proposed fast point cloud data collection technique designed specifically for bridges with internal chambers. Subsequently, Euclidean clustering and grid segmentation algorithms are developed to automatically extract contour features from the sliced point clouds. Finally, a framework based on the Dynamo–Revit reverse modelling method is introduced, enabling the automatic generation of gDT models from the identified point cloud features. To validate the feasibility and accuracy of the proposed method, a concrete variable section bridge is used. A comparison is made between the generated gDT model and the point cloud model in terms of 3D deviation, revealing a maximum deviation of 6.6 mm and an average deviation of 3 mm. These results affirm the feasibility of the proposed method.

Terrestrial laser scanning (TLS) is an effective tool for extracting stem distribution, providing essential information for forest inventory and ecological studies while also assisting forest managers in monitoring and controlling forest stand density. A feature-based method is commonly integrated into the pipelines of stem detection, facilitating the transition from stem point to stem instance, but most studies focus on feature effectiveness from the point level, neglecting the relationship between stem point extraction and stem detection. In this paper, a feature-based method is proposed to identify stems from TLS data, with features selected from stem levels. Firstly, we propose a series of voxel-based features considering the stem characteristics under the forest. Then, based on the evaluation of some commonly used and proposed features, a stem-based feature selection method is proposed to select a suitable feature combination for stem detection by constructing and evaluating different combinations. Experiments are carried out on three plots with different terrain slopes and tree characteristics, each having a sample plot size of about 8000 m². The results show that the voxel-based features can supplement the basic features, which improve the average accuracy of stem point extraction and stem detection by 9.5% and 1.2%, respectively. The feature set obtained by the proposed feature selection method achieves a better balance between accuracy and feature number compared with the point-based feature selection method and the features used in previous studies. Moreover, the accuracies of the proposed stem detection methods are also comparable to the three methods evaluated in the international TLS benchmarking project.