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

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

Surface and Sub-Surface Analyses for Bridge Inspection

The development of bridge inspection solutions has been discussed in the recent past. In this dissertation, significant development and improvement on the state-of-the-art in the field of bridge inspection using multiple sensors (e.g. ground penetrating radar (GPR) and visual sensor) has been proposed. In the first part of this research (discussed in chapter 3), the focus is towards developing effective and novel methods for rebar detection and localization for sub-surface bridge inspection of steel rebars. The data has been collected using Ground Penetrating Radar (GPR) sensor on real bridge decks. In this regard, a number of different approaches have been successively developed that continue to improve the state-of-the-art in this particular research area. The second part (discussed in chapter 4) of this research deals with the development of an automated system for steel bridge defect detection system using a Multi-Directional Bicycle Robot. The training data has been acquired from actual bridges in Vietnam and validation is performed on data collected using Bicycle Robot from actual bridge located in Highway-80, Lovelock, Nevada, USA. A number of different proposed methods have been discussed in chapter 4. The final chapter of the dissertation will conclude the findings from the different parts and discuss ways of improving on the existing works in the near future

INSPIRE Newsletter Fall 2018

https://scholarsmine.mst.edu/inspire-newsletters/1003/thumbnail.jp

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

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

Computer Vision based inspection on post-earthquake with UAV synthetic dataset

The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evaluated and used with unmanned aerial vehicles (UAVs) in real-world conditions. Thanks to the methods presented in the article, it is possible to obtain high accuracy in detecting buildings defects, segmenting constructions into their components and estimating their technical condition based on a single drone flight.Comment: 15 pages, 8 figures, published version, software available from https://github.com/MatZar01/IC_SHM_P

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

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

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader