Search

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.

Research has shown that alkali-activated concrete (AAC) is comparable to ordinary Portland cement concrete (OPCC) in terms of mechanical properties and may offer ecological benefits compared to OPCC. This study evaluates the energy and emission of AAC and OPCC across different classifications of concrete compressive strength (40, 60, and 100 ​MPa). Analysis indicates that the selection of constituent materials can substantially affect the energy and emission of AAC and OPCC. Ordinary Portland cement (OPC) is the principal contributor to the energy and emission of OPCC, accounting for 80% of energy and 91% of emissions of OPCC. The activating solution, meanwhile, is the main contributor to the energy and CO2 emission of AAC. Normal strength AAC (40 ​MPa) shows 46% less energy and 73% less CO2 emission than OPCC. However, high-strength AAC (60 ​MPa), using metakaolin as a base material, experiences higher energy (8%) than OPCC yet the emission is 40% less than OPCC. A substitution of fly ash for metakaolin results in superior efficiency of AAC compared to OPCC. Two mixtures of ultra-high-strength AAC (100 ​MPa) result in contradictory findings. One mixture with a sodium hydroxide and silica fume activating solution shows 5% and 30% less energy and CO2 emission, while the other mixture with a sodium hydroxide and sodium silicate activating mixture is less efficient than OPCC.

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(εx) 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(εx) 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(εx) 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(εx) 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.

Autonomous vehicles (AVs) are expected to change driving perspectives once they are available in the markets. This type of vehicle has received substantial attention lately from media and researchers. This technology is still under rapid advancements, and further research studies are needed to address the potential outcomes, opportunities, and challenges. The fuel system of the AVs is expected to be electrical; therefore, this study addresses the current status of electric vehicles (EVs), including charging time, charging type, and driving range. The study also discusses the barriers that may hindrance the transformation to AVs from the users, planners, and government perspectives. These barriers include the current charging stations of EVs, which provide 2 kW power. These stations can be insufficient for the AVs since these cars are expected to utilize advanced sensors and computers. The authors also propose comprehensive recommendations that could facilitate the so-called transformation of EVs to AVs and AVs' associated marketing. The authors found out that conducting a survey is essential to observe the public perception, and marketing and communication plans are essential to educating the public regarding AVs’ features and advantages. Also, a collaboration between auto manufacturers and planners is critical to the success of these vehicles. Finally, a public survey which consisted of 95 participants were conducted to examine the general perspective as surveys are necessary to assist planners, governments and automakers for their future movement toward a sustainable transportation system.

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.