As-Built Inventory and Deformation Analysis of a High Rockfill Dam under Construction with Terrestrial Laser Scanning

The construction of large earth/rock fill dams, albeit its remarkable progress, still relies largely on past experiences. Therefore, a comprehensive yet dependable monitoring program is particularly beneficial for guiding the practice. However, conventional measurements can only produce limited discrete data. This paper exploits the potential of the terrestrial laser scanning (TLS) for an accurate inventory of as-built states of a concrete-faced rockfill dam under construction and for a full-field analysis of the 3D deformation pattern over its upstream face. For the former, a well-designed 3D geodetic system, with a particular consideration of the topography, promises a regulated acquisition of high-quality and blind-zone-free point cloud at field and also eases the cumbersome data registration process while maintaining its precision in house. For the latter, a problem-tailored processing pipeline is proposed for deformation extraction. Its core idea is to achieve a highly precise alignment of the point clouds with Iterative Closed Point algorithms from different epochs in datum areas that displays a featured, undeformed geometry at stable positions across epochs. Then, the alignment transformation matrix is applied to the point clouds of respective upstream face for each epoch, followed by pairwise comparisons of multiple adjusted point clouds for deformation evaluation. A processing pipeline is used to exploit the peal scene data redundancy of the GLQ dam acquired at six different epochs. Statistical analysis shows that satisfactory accuracy for deformation detection can be repeatably achieved, regardless of the scanner’s positioning uncertainties. The obtained 3D deformation patterns are characterised by three different zones: practically undeformed, outward and inward deformed zones. Their evolutions comply well with real construction stages and unique 3D valley topography. Abundant deformation results highlight the potential of TLS combined with the proposed data processing pipeline for cost-efficient monitoring of huge infrastructures compared to conventional labor-intense measurements.

Application of Terrestrial Laser Scanning (TLS) in the Architecture, Engineering and Construction (AEC) Industry

As a revolutionary technology, terrestrial laser scanning (TLS) is attracting increasing interest in the fields of architecture, engineering and construction (AEC), with outstanding advantages, such as highly automated, non-contact operation and efficient large-scale sampling capability. TLS has extended a new approach to capturing extremely comprehensive data of the construction environment, providing detailed information for further analysis. This paper presents a systematic review based on scientometric and qualitative analysis to summarize the progress and the current status of the topic and to point out promising research efforts. To begin with, a brief understanding of TLS is provided. Following the selection of relevant papers through a literature search, a scientometric analysis of papers is carried out. Then, major applications are categorized and presented, including (1) 3D model reconstruction, (2) object recognition, (3) deformation measurement, (4) quality assessment, and (5) progress tracking. For widespread adoption and effective use of TLS, essential problems impacting working effects in application are summarized as follows: workflow, data quality, scan planning, and data processing. Finally, future research directions are suggested, including: (1) cost control of hardware and software, (2) improvement of data processing capability, (3) automatic scan planning, (4) integration of digital technologies, (5) adoption of artificial intelligence.

3D AIR POLLUTION COMPUTATIONAL FLUID MODELLING DATA ANALYSIS USING TERRESTRIAL LASER SCANNING (TLS) AND UNMANNED AERIAL VEHICLE (UAV) APPROACH

Air pollution is a global event that can harm the environment and people. It is recommended that effective management be implemented to allow for the sustainable development of a specific area. The 3D building model is employed in the study to support air pollution modelling for this purpose. A proper mode of data acquisition is required to produce the building model. Many data acquisition (Terrestrial Laser Scanning and Unmanned Aerial Vehicle) approaches can be utilized, but the most appropriate one for the use in outdoor air pollution is needed. This is because it can assist in providing precise data for the modelling of a 3D building while maintaining the shape and geometry of the real-world structure. The accurate data can support modelling of surrounding air pollution concerning wind data and surrounding conditions, where different generated structures can influence the flow of the pollutants. The suitable model can be determined by using suitability analysis and with the implementation of Computational Fluid Dynamics (CFD) simulation. However, from these, no specific technique is chosen because the generated models presented incomplete model. Hence, it is suggested to combine both techniques to acquire building data as the missing surfaces from each technique can be completed by another technique. Thus, this study provides a good reference for responsible agencies or researchers in selecting the best technique for modelling the building model in air pollution-related studies.

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales.