Remote inspection of RC structures using unmanned aerial vehicles and heuristic image processing. (November 2020)

• Record Type: Journal Article
• Authors: Ribeiro, D.; Santos, R.; Shibasaki, A.; Montenegro, P.; Carvalho, H.; Calçada, R.
• Is Part Of: Engineering failure analysis. Volume 117(2020)
• Publication Date: 2020-11
• Subjects: Remote structural inspection -- UAV -- Digital image processing -- Heuristic feature-extraction -- BIM; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2020.104813
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: Methodology for remote inspection of RC structures using UAVs and image processing. Heuristic feature extraction based on edge detectors and morphological filters. Identification of biological colonies, efflorescences, cracks and exposed rebars. Characterization of pathologies based on 3D reconstitutions and BIM integration. Helps infrastructure owners to implement optimal facility management strategies. Abstract: This article describes an innovative methodology for remote inspection of reinforced concrete (RC) structures using Unmanned Aerial Vehicles (UAVs) and based on advanced digital image processing. The use of specific heuristic feature-extraction methods, developed in MATLAB©, allows the automatic identification of various types of RC pathologies, particularly, biological colonies, efflorescences, cracks and exposed steel rebars. The developed inspection methodology is applied to the Monte da Virgem telecommunications tower, which has a total height of 177 m and is the highest structure of this kind in Portugal. The results demonstrate the efficiency and robustness of the methodology in: i) its ability to accurately locate and characterize the existing pathologies, ii) visualizing the full extent of pathologies in 3D photogrammetric reconstitutions of the structure, and iii) integrating the identified pathologies into a Building Information Modeling (BIM) model. The regular use of the proposed methodology over time will allow accurate monitoring of the evolution of pathologies to assess the condition of the structure. Additionally, the integration of the inspection results into BIM digital models will help infrastructure managers to decide on the implementation of the most adequate facility management strategies in order to optimize the scheduling of maintenance, rehabilitation and retrofitting interventions.