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.