3 research outputs found

    Autonomous Wall-climbing Robots for Inspection and Maintenance of Concrete Bridges

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    Since 2002, the PI’s group has developed four generations of wall-climbing robots for NDE inspection of civil infrastructure. These robots combine the advantages of aerodynamic attraction and suction to achieve a desirable balance of strong adhesion and high mobility. They don’t require perfect sealing and can thus move on smooth and rough surfaces, such as brick, concrete, stucco, wood, glass, and metal. For example, Rise-Rover uses two drive modules to carry their middle compartment with payload up to 450 N. Ground penetrating radar (GPR)-Rover and Mini GPR-Rover are custom designed to carry a GSSI’s GPR antenna for subsurface defect detection and utility survey on concrete structures such as bridges and tunnels. The robots can also carry other devices such as impact echo and ultrasonic flaw detectors for bridge evaluation. To date, all the robots are remotely controlled to scan concrete surfaces. This project aims to develop motion control and localization methods to make wall-climbing robots a fully autonomous system with automated inspection process using various NDE devices and sensors, and design innovative mechanisms and tools and integrate them into the robots for maintenance actions

    Development of an autonomous system for assessment and prediction of structural integrity

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    Kako bi se osiguralo racionalnije, plansko održavanje prometne infrastrukture uz smanjenje troškova te u konačnici minimalizirao rizik od katastrofalnih posljedica, nužan je razvoj inovativnih rješenja u području održavanja građevina prometne infrastrukture. Kroz projekt ASAP razvija se sustav za autonomni pregled građevina, koji se zasniva na naprednim mjernim metodama integriranim na robota penjača i bespilotnu letjelicu. Cilj ovog rada je dati osvrt i upozoriti na nedostatke konvencionalnog načina ispitivanja materijala i konstrukcija za potrebu ocjene stanja, koji su bili osnovna motivacija okupljanja multidisciplinarnog tima kroz projekt ASAP. U radu su također prikazane mogućnosti i izazovi razvoja autonomnog sustava za pregled građevina, a sve u svrhu povećanja pouzdanosti i efikasnosti sustavnog pregleda građevina.Development of innovative solutions for the maintenance of transport infrastructure facilities is needed in order to ensure a more rational, planned and lower-cost maintenance of transport infrastructure, and to ultimately minimise the risk of catastrophic consequences. A system for an autonomous inspection of structures, based on advanced measuring methods integrated on a wall-climbing robot and an unmanned aerial vehicle, is currently developed in the scope of the ASAP project. The objective of this paper is to provide an overview and draw attention to disadvantages of conventional methods for testing materials and structures in order to assess their condition. This objective was the main motivation for forming a multidisciplinary team through the ASAP project. Possibilities and challenges in the development of an autonomous structural-assessment system are also presented in the paper, with the purpose of increasing the reliability and efficiency of systemic assessment of structures

    Classification, Localization, and Quantification of Structural Damage in Concrete Structures using Convolutional Neural Networks

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    Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have recently become of great interest owing to their superior ability to detect damage in engineering structures. ML algorithms used in this domain are classified into two major subfields: vibration-based and image-based SHM. Traditional condition survey techniques based on visual inspection have been the most widely used for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience and engineering judgment. However, this process is subjective, time-consuming, and hampered by difficult access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing, signal processing, and deep learning for detecting defects and identifying damage locations in typically inaccessible areas of concrete structures. The work conducted in this thesis achieved excellent damage localization and classification performance and could offer a nearly automated inspection platform for the colossal backlog of ageing civil engineering structures
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