Utilization of 3D Laser Scanning for Stability Evaluation and Deformation Monitoring of Landslides

Guo, Yanwei; Li, Xiongwei; Ju, Shangwei; Lyu, Qifeng; Liu, Tao

doi:10.1155/2022/8225322

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…The photogrammetric method [ 16 , 17 , 18 ], the ALS method [ 15 ], and the TLS method [ 4 , 5 , 13 , 14 , 19 , 22 , 23 ] are successfully used in the periodic geodetic monitoring of facilities similar in their construction to MSW landfills (large-scale, soil-built facilities). These include open-pit mines, natural landslide slopes, post-production landfills, and various types of bulk product storage areas.…”

Section: Resultsmentioning

confidence: 99%

“…Early detection of displacement of slowly creeping material is an important part of early warning against landslides. Stability assessment and prediction of large deformations and failures, including in open-pit mines and slopes during construction works, are usually performed using surveying techniques [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Unlike traditional methods, monitoring using 3D laser scanning allows for non-contact point measurement, so it can complement traditional monitoring methods, improve the efficiency of slope monitoring, and provide data support/supplementation for site protection [ 4 ]. For example, Huang et al [ 14 ] proposed a method for monitoring slow-moving landslides using long-range TLS.…”

Section: Introductionmentioning

confidence: 99%

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Surface Monitoring of an MSW Landfill Based on Linear and Angular Measurements, TLS, and LIDAR UAV

Pasternak

Zaczek-Peplinska

Onyszko

et al. 2023

Sensors

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Surface monitoring of landfills is crucial not only during their operation but also for later land restoration and development. Measurements concern environmental factors, such as leachate, migration of pollutants to water, biogas, and atmospheric emissions, and geotechnical factors, such as stability and subsidence. Landfill subsidence can be measured using modern surveying techniques. Modern measurement methods for landfill body displacement monitoring and their control after restoration and adaptation as recreational areas include terrestrial laser scanning (TLS), and scanning and low-altitude photogrammetric measurements from an unmanned aerial vehicle (UAV). The acquired measurement data in the form of 3D point clouds should be referenced to the local control network to enable a comprehensive analysis of data acquired using various techniques, including geotechnical sensors such as benchmarks, piezometers, and inclinometers. This study discusses the need for surface monitoring of municipal solid waste (MSW) landfills. A properly 3-D mapped landfill mass is the basis for ensuring the geotechnical safety of the restored landfill. Based on archival data and current measurements of the Radiowo landfill (Poland), this study compares the advantages and limitations of the following measurement techniques: linear and angular measurements, satellite measurements, TLS, and UAV scanning and photogrammetry, considering specific conditions of the location and vegetation of the landfill. Solutions for long-term monitoring were proposed, considering the cost and time resolution necessary for creating a differential model of landfill geometry changes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surface Monitoring of an MSW Landfill Based on Linear and Angular Measurements, TLS, and LIDAR UAV

Pasternak

Zaczek-Peplinska

Onyszko

et al. 2023

Sensors

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“…The factors 'distance from river' and 'rainfall' hold relatively high importance in both regions. The mechanical properties of loose rock mass will change under the action of water [43,44]. The fragile surface materials of slopes exacerbate their instability during intense rainfall, seismic events, and drastic fluctuations in river water levels [45][46][47].…”

Section: Region B Serves As An Additional Study Area To Independently...mentioning

confidence: 99%

A Comparative Study of Landslide Susceptibility Mapping Using Bagging PU Learning in Class-Prior Probability Shift Datasets

Zhao,

Ma,

Dong

et al. 2023

Remote Sensing

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Landslide susceptibility mapping is typically based on binary prediction probabilities. However, non-landslide samples in modeling datasets are often unlabeled data, and the phenomenon of class-priori shift, that is, the proportion of landslide samples frequently deviates from real-world scenarios and is spatially heterogeneous. By comparing the classification performance and predicted probability distributions across multiple unbalanced datasets with known and unknown sample proportions, this study assesses the landslide susceptibility model’s generalization ability in the context of class-prior shifts. The study investigates the potential of Bagging PU Learning, a semi-supervised learning approach, in improving the generalization performance of landslide susceptibility models and proposes the Bagging PU-GDBT algorithm. Our findings highlight the effectiveness of Bagging PU Learning in enhancing the recall of landslides and the generalization capabilities of models on unbalanced datasets. This method reduces prediction uncertainties, especially in high and very high susceptibility zones. Furthermore, results emphasize the superiority of models trained on balanced datasets with 1:1 sample ratio for landslide susceptibility mapping over those trained on unbalanced datasets.

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“…An engineering-geological survey provides parameters to characterize the nature of the discontinuities (orientation, spacing, persistence, surface roughness, etc.). Slope 3D models, based on high-resolution point cloud data, can be produced using several techniques, such as total station (TS) [1], terrestrial laser scanning (TLS) [2][3][4], airborne light detection and ranging (LiDAR) [5,6], unmanned aerial vehicle (UAV) photogrammetry [5,7,8], and UAV LiDAR [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Stress–Strain Investigation of the Rock Mass Based on Overcoring with CSIRO HI Cell Test and Numerical Modeling: A Case Study from an Italian Underground Marble Quarry

et al. 2022

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The present research illustrates the application of a methodological approach to studying the stress–strain distribution in a marble quarry of the Apuan Alps mining area (Italy). This study has been carried out in the framework of a project involving the University of Siena and the UOC Ingegneria Mineraria—USL Toscana Nord-Ovest, Tuscany Region. This stress–strain analysis aims foremost to monitor the slope stability conditions to guarantee a safe workplace for the personnel involved in mining activities, and to enable more sustainable long-term planning for excavation and production. The involved survey activities are as follows: (i) terrestrial laser scanning; (ii) engineering–geological data mapping; and (iii) in situ marble stress measuring through four CSIRO-type cell tests executed in different locations and at various depths within the underground excavation walls. The gathered data converged into numerical models of the quarry, both in 2D (DEM) and 3D (FEM), calibrated by in situ stress results through a rigorous back analysis assessment using least squares procedures. The created models represent a valuable tool for the identification and securing of risk areas and for future excavation planning in respect of the site efficiency and safety.

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Utilization of 3D Laser Scanning for Stability Evaluation and Deformation Monitoring of Landslides

Cited by 10 publications

References 16 publications

Surface Monitoring of an MSW Landfill Based on Linear and Angular Measurements, TLS, and LIDAR UAV

Surface Monitoring of an MSW Landfill Based on Linear and Angular Measurements, TLS, and LIDAR UAV

A Comparative Study of Landslide Susceptibility Mapping Using Bagging PU Learning in Class-Prior Probability Shift Datasets

Stress–Strain Investigation of the Rock Mass Based on Overcoring with CSIRO HI Cell Test and Numerical Modeling: A Case Study from an Italian Underground Marble Quarry

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