## 1 Introduction  
Reinforced cement concrete (RCC) structures are designed for a specific serviceability life span. Many of the high-rise structures are made up of RCC. During its functionality, RCC structure tends to deteriorate on repetitive loading and other environmental conditions. Infrastructure development resembles a country's overall growth, and as a part of the transportation infrastructure, bridges act as a major connectivity link in highways and railways. Bridges are expensive to construct and maintain, and it is also evident that various factors influence their serviceability [1]. Ninety percent of bridges are built in unavoidable conditions, either on water bodies or on rail tracks, and they are critical zones in a transportation network. There is a need for periodic monitoring of RCC structures for damage analysis and maintaining it for further commencement to service. As per the guidelines issued by the Federal Highway Administration inspection standards, bridges need to be inspected every 2-4 years based on the age of the structure [2]. Structures being at ground level are easy to inspect for their service life with the available wide range of non-destructive testing (NDT) techniques. Structures above ground level i.e., more than 5 m are hard to inspect for serviceability, as the height of the structure poses safety risks to inspectors and engineers [3]. Bridge performance evaluation to quantify service life based on deterioration at critical regions are challenging and are typically carried out with the assistance of snooper trucks and contact NDT methods. During situations of natural calamities, it is hard to check the damage to infrastructure through traditional methods like visual and in-contact NDT methods. The first non-contact damage quantification for structure and property is performed by using high-resolution satellite data sets. The usage of in-contact sensors for vibration observations has given a great advantage to real-time monitoring of the structure connected to data acquisition platforms. These in-contact sensors are highly sensitive to handle and also vulnerable in external loading and harsh environmental conditions [4]. These sensors are highly capable of identifying defects at their installed locations, but they have demonstrated ineffectiveness in monitoring the entire structure at the same time [5]. Infrastructure failures are mostly associated with improper identification and maintenance of the damage zone in a structure [6]. Visual inspection, assessment and documentation of repairs are the most common methodology followed by inspectors and engineers [7]. Although it is a quite simple and common process, the machinery involved in inspection makes it more expensive, and the occurrence of accidents and manual errors are high. In addition, human vision inspection can perceive limited observations at a time compared to computer vision techniques [3]. Photogrammetry concepts clubbed with computer vision are developed by researchers to identify the critical zones by analyzing the digital image for crack properties, spalling and damage in a structure. With the advent of digital image correlation (DIC) in the recent past, many civil engineering applications like structural health monitoring (SHM) are simplified [8]. The DIC vision technique is a remote sensing and photogrammetric method that can perceive the deformation of an object by analyzing the prior and post-loading digital image [9]. The availability of numerous and diversified image processing algorithms has facilitated the researcher to choose according to the requirement of an application [10]. Algorithms, such as pattern recognition, have made locating the cracks in a structure and monitoring easy [11; 12]. The DIC technique is a rapid, reliable, cost-effective and non-contact NDT method. DIC methods have paved the way for new cost-effective procedures for SHM in major areas, such as bridge monitoring and vibrations in wind turbines [13; 14].