Automatic Mapping of Discontinuity Persistence on Rock Masses Using 3D Point Clouds

Finding new ways to quantify discontinuity persistence values in rock masses in an automatic or semi-automatic manner is a considerable challenge, as an alternative to the use of traditional methods based on measuring patches or traces with tapes. Remote sensing techniques potentially provide new ways of analysing visible data from the rock mass. This work presents a methodology for the automatic mapping of discontinuity persistence on rock masses, using 3D point clouds. The method proposed herein starts by clustering points that belong to patches of a given discontinuity. Coplanar clusters are then merged into a single group of points. Persistence is measured in the directions of the dip and strike for each coplanar set of points, resulting in the extraction of the length of the maximum chord and the area of the convex hull. The proposed approach is implemented in a graphic interface with open source software. Three case studies are utilized to illustrate the methodology: (1) small-scale laboratory setup consisting of a regular distribution of cubes with similar dimensions, (2) more complex geometry consisting of a real rock mass surface in an excavated cavern and (3) slope with persistent sub-vertical discontinuities. Results presented good agreement with field measurements, validating the methodology. Complexities and difficulties related to the method (e.g. natural discontinuity waviness) are reported and discussed. An assessment on the applicability of the method to the 3D point cloud is also presented. Utilization of remote sensing data for a more objective characterization of the persistence of planar discontinuities affecting rock masses is highlighted herein

A maximum entropy-based optimal neighbor selection for multispectral airborne LiDAR point cloud classification

202311 bcchVersion of RecordRGCPublishe

Radiological method for measuring patellofemoral tracking and tibiofemoral kinematics before and after total knee replacement

OBJECTIVES: Numerous complications following total knee replacement (TKR) relate to the patellofemoral (PF) joint, including pain and patellar maltracking, yet the options for in vivo imaging of the PF joint are limited, especially after TKR. We propose a novel sequential biplane radiological method that permits accurate tracking of the PF and tibiofemoral (TF) joints throughout the range of movement under weightbearing, and test it in knees pre- and post-arthroplasty. METHODS: A total of three knees with end-stage osteoarthritis and three knees that had undergone TKR at more than one year’s follow-up were investigated. In each knee, sequential biplane radiological images were acquired from the sagittal direction (i.e. horizontal X-ray source and 10° below horizontal) for a sequence of eight flexion angles. Three-dimensional implant or bone models were matched to the biplane images to compute the six degrees of freedom of PF tracking and TF kinematics, and other clinical measures. RESULTS: The mean and standard deviation for the six degrees of freedom of PF tracking and TF kinematics were computed. TF and PF kinematics were highly accurate (< 0.9 mm, < 0.6°) and repeatable. CONCLUSIONS: The developed method permitted measuring of in vivo PF tracking and TF kinematics before and after TKR throughout the range of movement. This method could be a useful tool for investigating differences between cohorts of patients (e.g., with and without pain) impacting clinical decision-making regarding surgical technique, revision surgery or implant design

Use of LIDAR in landslide investigations: A review

This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDARderived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed