Damage detection with image processing: a comparative study

Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by image processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.

A real-time sensing system based on triboelectric nanogenerator for dynamic response of bridges

The strain of bridges under traffic loads is time-varying and of small amplitude(~10^(-6)),which is a type of cumulative response and needs long-term continuous monitoring.To precisely capture the time-varying responses,a dynamic strain triboelectric nanogenerator(TENG)sensor with superior response capability,sensitivity,self-powered,and long-term stability is proposed in this paper.An analytical correlation between the structural strain response signal and the detected electrical signal is established for long-term continuous quantitative strain measurements based on the principles of contact electrification and electrostatic induction.A series of experiments are conducted to investigate the output performance of the proposed lateral-sliding mode TENG sensors.The results reveal that,with the loading condition with frequencies lower than 10 Hz,the time-varying strain responses of the steel bridge within the range of 3 to 150 microstrains can also be detected with high precision of 0.1 microstrains.And it achieves long-term stability after 10000 loading cycles compared with commercial sensors.The proposed novel sensing theory and method based on TENG technology can be applied as a new alternative approach for monitoring realtime structural strain information quantitatively with general applicability and feasibility for bridges.

Distributed Simulation Platforms and Data Passing Tools for Natural Hazards Engineering: Reviews, Limitations,and Recommendations

here has been a strong need for simulation environments that are capable of modeling deep interdependencies between complex systems encountered during natural hazards,such as the interactions and coupled effects between civil infrastructure systems response,human behavior,and social policies,for improved community resilience.Coupling such complex components with an integrated simulation requires continuous data exchange between different simulators simulating separate models during the entire simulation process.This can be implemented by means of distributed simulation platforms or data passing tools.In order to provide a systematic reference for simulation tool choice and facilitating the development of compatible distributed simulators for deep interdependent study in the context of natural hazards,this article focuses on generic tools suitable for integration of simulators from different fields but not the platforms that are mainly used in some specific fields.With this aim,the article provides a comprehensive review of the most commonly used generic distributed simulation platforms(Distributed Interactive Simulation(DIS),High Level Architecture(HLA),Test and Training Enabling Architecture(TENA),and Distributed Data Services(DDS))and data passing tools(Robot Operation System(ROS)and Lightweight Communication and Marshalling(LCM))and compares their advantages and disadvantages.Three specific limitations in existing platforms are identified from the perspective of natural hazard simulation.For mitigating the identified limitations,two platform design recommendations are provided,namely message exchange wrappers and hybrid communication,to help improve data passing capabilities in existing solutions and provide some guidance for the design of a new domain-specific distributed simulation framework.