# Asphalt Concrete Characterization Using Digital Image Correlation: A Systematic Review of Best Practices, Applications, and Future Vision

arXiv: https://arxiv.org/abs/2402.17074 · HTML: https://mlsanity.com/paper/2402.17074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 188 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss](https://mlsanity.com/paper/1711.07837/) (2017)
- [When Deep Learning Meets Digital Image Correlation](https://mlsanity.com/paper/2009.03993/) (2020)
- [Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End Displacement and Strain Measurement](https://mlsanity.com/paper/2110.13720/) (2021)
- [Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network](https://mlsanity.com/paper/2303.05957/) (2023)
- [Crack Detection of Asphalt Concrete Using Combined Fracture Mechanics and Digital Image Correlation](https://mlsanity.com/paper/2305.05057/) (2023)

## Abstract (arXiv)

Digital Image Correlation (DIC) is an optical technique that measures displacement and strain by tracking pattern movement in a sequence of captured images during testing. DIC has gained recognition in asphalt pavement engineering since the early 2000s. However, users often perceive the DIC technique as an out-of-box tool and lack a thorough understanding of its operational and measurement principles. This article presents a state-of-art review of DIC as a crucial tool for laboratory testing of asphalt concrete (AC), primarily focusing on the widely utilized 2D-DIC and 3D-DIC techniques. To address frequently asked questions from users, the review thoroughly examines the optimal methods for preparing speckle patterns, configuring single-camera or dual-camera imaging systems, conducting DIC analyses, and exploring various applications. Furthermore, emerging DIC methodologies such as Digital Volume Correlation and deep-learning-based DIC are introduced, highlighting their potential for future applications in pavement engineering. The article also provides a comprehensive and reliable flowchart for implementing DIC in AC characterization. Finally, critical directions for future research are presented.