RCC Structural Deformation and Damage Quantification Using Unmanned Aerial Vehicle Image Correlation Technique

Abstract

Reinforced cement concrete (RCC) is universally acknowledged as a low-cost, rigid, and high-strength construction material. Major structures like buildings, bridges, dams, etc., are made of RCC and subjected to repetitive loading during their service life for which structural performance deteriorates with time. Bridges and high-rise structures, being above ground level, are hard to equip with the contact mechanical methods to inspect strains and displacements for structural health monitoring (SHM). A non-contact, optical and computer vision based full field measuring technique called digital image correlation (DIC) technique was developed in the recent past to specifically evaluate bridge decks. Generally, optical images of structure in field conditions are not acquired precisely perpendicular to the object, which instinctively affects the deformation results obtained during loading conditions. An unmanned aerial vehicle (UAV) equipped with DIC vision-based technique acts as a rapid and cost-effective tool to quantify the serviceability of bridges by measuring strains and displacements at inaccessible locations. In this study, a non-contact unmanned aerial vehicle image correlation (UAVIC) technique is used on a scaled bridge girder and a contact method of measuring deformations with a dial gauge. Both investigations are correlated for accuracy assessment, and it is understood that results in laboratory conditions are 90% accurate. Similarly, the UAVIC technique is also performed on a rail over the bridge in the field conditions to understand the feasibility of the proposed method and evaluate damage quantification of it.

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

“…Drones can check the condition of the bridge from relatively any angle and pinpoint specific damage thanks to their high altitude of flight. In addition to being able to fly in a variety of weather situations, UAVs may also be equipped with high-tech cameras that allow them to continuously monitor the bridges from every angle [153]. Given recent developments in the fields of Internet of Things, 5G and 6G Internet, as well as artificial intelligence, on the one hand, and experts' desire to use drones in SHM systems on the other, many developments and studies are still required.…”

A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective

“…The quantitative information of the crack was subsequently acquired by multiplying the actual physical size corresponding to the unit pixel, assuming the length of a single pixel as the unit length. Kumarapu et al [123] introduced UAVIC, a system that integrates UAVs with an image processing technique, i.e., digital image correlation. This approach is employed for damage quantification on scaled bridge girders.…”

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

“…In [39,47,63], only a comparison between algorithms was performed. In [40,45,52,[56][57][58]62,69,72,[77][78][79][80]85] a comparison with damage, deformation, and, in general, real-world manual measurements was performed. Finally, a proof of concept was provided in [84], with only qualitative results.…”

“…This choice applies to the following studies: [34,[40][41][42]44,45,[48][49][50][51][53][54][55][58][59][60][61][62][63][64]66,67,[69][70][71][72][73][74][75][76][77][78][80][81][82]. In a few cases, the experimental scenarios were outdoors where, on the other hand, it was necessary to consider, at least in part, the environmental conditions and the problems previously indicated, testing the solution on scale models in most cases as well.…”