An Efficient Damage Quantification Method for Cylindrical Structures Enhanced by a Dry-Point-Contact Torsional-Wave Transducer

Quantification of damage sizes in cylindrical structures such as pipes and rods is of paramount importance in various industries. This work proposes an efficient damage quantification method by using a dry-point-contact (DPC) transducer based on the non-dispersive torsional waves in the low-frequency range. Theoretical analyses are first carried out to investigate the torsional wave interaction with different sizes of defects in cylindrical structures. A damage quantification algorithm is designed based on the wave reflections from the defect and end. Capitalizing on multiple excitations at different frequencies, the proposed algorithm constructs a damage image that identifies the geometric parameters of the defects. Numerical simulations are conducted to validate the characteristics of the theoretically-predicted wave-damage interaction analyses as well as the feasibility of the designed damage quantification method. Using the DPC transducer, experiments are efficiently carried out with a simple physical system. The captured responses are first assessed to confirm the capability of the DPC transducer for generating and sensing torsional waves. The sizes of the defects in two representative steel rods are then quantified with the proposed method. Both numerical and experimental results demonstrate the efficacy of the proposed damage quantification method. The understandings of the wave-damage interaction and the concept of the damage quantification algorithm lay out the foundation for engineering applications.

Sensors and Measurements for UAV Safety: An Overview

Unmanned aerial vehicles’ (UAVs) safety has gained great research interest due to the increase in the number of UAVs in circulation and their applications, which has inevitably also led to an increase in the number of accidents in which these vehicles are involved. The paper presents a classification of UAV safety solutions that can be found in the scientific literature, putting in evidence the fundamental and critical role of sensors and measurements in the field. Proposals from research on each proposed class concerning flight test procedures, in-flight solutions including soft propeller use, fault and damage detection, collision avoidance and safe landing, as well as ground solution including testing and injury and damage quantification measurements are discussed.

Rail RCF damage quantification and comparison for different damage models

There are several fatigue-based approaches that estimate the evolution of rolling contact fatigue (RCF) on rails over time and built to be used in tandem with multi-body simulations of vehicle dynamics. However, most of the models are not directly comparable with each other since they are based on different physical models even though they shall predict the same RCF damage at the end. This article studies different approaches to quantifying RCF and puts forward a measure for the degree of agreement between them. The methodological framework studies various steps in the RCF quantification procedure within the context of one another, identifies the ‘primary quantification step’ in each approach and compares results of the fatigue analyses. In addition to this, two quantities—‘similarity’ and ‘correlation’—have been put forward to give an indication of mutual agreement between models. Four widely used surface-based and sub-surface-based fatigue quantification approaches with varying complexities have been studied. Different operational cases corresponding to a metro vehicle operation in Austria have been considered for this study. Results showed that the best possible quantity to compare is the normalized damage increment per loading cycle coming from different approaches. Amongst the methods studied, approaches that included the load distribution step on the contact patch showed higher similarity and correlation in their results. While the different approaches might qualitatively agree on whether contact cases are ‘damaging’ due to RCF, they might not quantitatively correlate with the trends observed for damage increment values.

Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge

For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.