Crack detection using tap-testing and machine learning techniques to prevent potential rockfall incidents

• URL: http://arxiv.org/abs/2110.04923v1
• Date: Sun, 10 Oct 2021 22:53:36 GMT
• Title: Crack detection using tap-testing and machine learning techniques to prevent potential rockfall incidents
• Authors: Roya Nasimi, Fernando Moreu, John Stormont
• Abstract summary: This paper proposes a system towards an automated inspection for potential rockfalls. A robot is used to repeatedly strike or tap on the rock surface. The sound from the tapping is collected by the robot and classified with the intent of identifying rocks that are broken and prone to fall.
• Score: 68.8204255655161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rockfalls are a hazard for the safety of infrastructure as well as people. Identifying loose rocks by inspection of slopes adjacent to roadways and other infrastructure and removing them in advance can be an effective way to prevent unexpected rockfall incidents. This paper proposes a system towards an automated inspection for potential rockfalls. A robot is used to repeatedly strike or tap on the rock surface. The sound from the tapping is collected by the robot and subsequently classified with the intent of identifying rocks that are broken and prone to fall. Principal Component Analysis (PCA) of the collected acoustic data is used to recognize patterns associated with rocks of various conditions, including intact as well as rock with different types and locations of cracks. The PCA classification was first demonstrated simulating sounds of different characteristics that were automatically trained and tested. Secondly, a laboratory test was conducted tapping rock specimens with three different levels of discontinuity in depth and shape. A real microphone mounted on the robot recorded the sound and the data were classified in three clusters within 2D space. A model was created using the training data to classify the reminder of the data (the test data). The performance of the method is evaluated with a confusion matrix.

Related papers

• A comparative study on machine learning approaches for rock mass classification using drilling data [0.3749861135832073]
  Current rock engineering design in drill and blast tunnelling relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised. This study aims to automate the translation of MWD data into actionable metrics for rock engineering.
  arXiv  Detail & Related papers  (2024-03-15T15:37:19Z)
• Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms [0.0]
  This paper explores the resilience of autonomous driving systems against three main physical adversarial attacks (tape, graffiti, illumination) To build robustness against attacks, defense techniques like adversarial training and transfer learning were implemented. Results demonstrated transfer learning models played a crucial role in performance by allowing knowledge gained from shape training to improve generalizability of road sign classification.
  arXiv  Detail & Related papers  (2023-11-02T04:11:45Z)
• A Skeleton-based Approach For Rock Crack Detection Towards A Climbing Robot Application [0.0]
  Multi-limbed climbing robot designs, such as ReachBot, are able to grasp irregular surface features and execute climbing motions to overcome obstacles. To support grasp site identification, we present a method for detecting rock cracks and edges, the SKeleton Intersection Loss.
  arXiv  Detail & Related papers  (2023-09-10T21:16:56Z)
• How adversarial attacks can disrupt seemingly stable accurate classifiers [76.95145661711514]
  Adversarial attacks dramatically change the output of an otherwise accurate learning system using a seemingly inconsequential modification to a piece of input data. Here, we show that this may be seen as a fundamental feature of classifiers working with high dimensional input data. We introduce a simple generic and generalisable framework for which key behaviours observed in practical systems arise with high probability.
  arXiv  Detail & Related papers  (2023-09-07T12:02:00Z)
• Automatic Segmentation of Aircraft Dents in Point Clouds [0.0]
  This paper reports on two developments towards automated dent inspection. The first is a method to generate a synthetic dataset of dented surfaces to train a fully convolutional neural network. The second is a strategy to convert 3D point clouds to 2.5D.
  arXiv  Detail & Related papers  (2022-05-03T16:48:31Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• End-to-End Intelligent Framework for Rockfall Detection [1.8594711725515676]
  Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner or digital cameras. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the domain geology and decision support systems.
  arXiv  Detail & Related papers  (2021-02-12T12:48:17Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching [69.24460241328521]
  Machine learning algorithms can be negatively affected by mismatches between training (source) and test (target) data distributions. We propose an unsupervised domain adaptation method that consists of aligning the first- and second-order sample statistics of each frequency band of target-domain acoustic scenes to the ones of the source-domain training dataset. We show that the proposed method outperforms the state-of-the-art unsupervised methods found in the literature in terms of both source- and target-domain classification accuracy.
  arXiv  Detail & Related papers  (2020-04-30T23:56:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.