Digital image correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of the contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for the precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the effectiveness of the DIC methodology has been validated as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

A vision-based approach has been employed in Structural Health Monitoring (SHM) of bridge infrastructure. The approach has many advantages: non-contact, non-destructive, long-distance, high precision, immunity from electromagnetic interference, and multiple-target monitoring. This review aims to summarise the vision- and Digital Image Correlation (DIC)-based SHM methods for bridge infrastructure because of their strategic significance and security concerns. Four different bridge types were studied: concrete, suspension, masonry, and steel bridge. DIC applications in SHM have recently garnered attention in aiding to assess the bridges’ structural response mechanisms under loading. Different non-destructive diagnostics methods for SHM in civil infrastructure have been used; however, vision-based techniques like DIC were only developed over the last two decades, intending to facilitate damage detection in bridge systems with prompt and accurate data for efficient and sustainable operation of the bridge structure throughout its service life. Research works reviewed in this article demonstrated the DIC capability to detect damage such as cracks, spalling, and structural parameters such as deformation, strains, vibration, deflection, and rotation. In addition, the reviewed works indicated that the DIC as an efficient and reliable technique could provide sustainable monitoring solutions for different bridge infrastructures.

This research is aimed at investigating the dynamic behaviour of, and to analyse the dynamic response and dynamic performance of steel frames strengthened with welded haunches subjected to a typical hydrocarbon blast loading. The structural dynamic analysis was carried out incorporating the selected blast load, the validated 3D model of the structures with different welded haunch configurations, steel dynamic material properties, and non-linear dynamic analysis of multiple degree of freedom (MDOF) structural systems. The dynamic responses and effectiveness of the reinforced connections were examined using ABAQUS finite element software. Results showed that the presence of the welded haunch reinforcement decreased the maximum frame ductility ratio. Based on the evaluation of the results, the haunch reinforcements strengthened the selected steel frame and improved the dynamic performance compared to the frame with unreinforced connections under blast loading, and the biggest haunch configuration is the “best” type.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

This research provides a practical guideline for Digital Image Correlation (DIC) data variations minimization in structural engineering through simple image processing techniques. The main objective of this research is to investigate the Pixel Averaging (P.A.) effect on the differential strain Diff(ε_x) variations. Three concrete arches were tested with three-point bending using the DIC technique for strain measurements. The measured strains are obtained through two virtual horizontal extensometers in the middle of each arch. The Diff(ε_x) was selected to avoid other 2D-DIC issues, such as the sample-camera out-of-plane movement. Three image cases, namely, one, ten, and twenty averaged images, were used for DIC analysis of each arch. The conditions of each image case are assessed by computing the Diff(ε_x) variance and the linear least square criterion (R²) between the two extensometers. The second objective is to examine the speckles’ dilation effects on the speckle pattern density and surface component quality utilizing the Image Erode (I.E.) technique. The (P.A.) technique provided consistent differential strain Diff(ε_x) values with a variance reduction of up to (90%) when averaged images were used. The (R²) has considerably increased (from 0.46, 0.66, 0.91 to 0.90, 0.96, 0.99), respectively, for the three samples. Moreover, the (I.E.) technique provided qualitatively denser speckles with a highly consistent DIC surface component.