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

TL;DR

This paper surveys the use of Digital Image Correlation (DIC) for asphalt concrete (AC) characterization, detailing best practices in speckle-pattern preparation, 2D- and 3D-DIC imaging and analysis, and the derivation of mechanistic parameters such as CTOD, FPZ, SIF, and J-integral. It discusses established and emerging approaches, including Digital Volume Correlation (DVC) and deep-learning-based DIC, and presents a comprehensive flowchart to guide implementation. The review reveals that 2D-DIC dominates AC literature while 3D-DIC remains promising but underutilized, highlighting opportunities to adopt DVC and DL-based methods for interior measurements and automation. Collectively, the work supports standardization, more rigorous post-processing, and the integration of fundamental fracture-mechanics concepts with DIC to enhance reliability, automation, and insight in AC testing.

Abstract

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.

Figures (14)

• Figure 1: Number of articles published using DIC in AC characterization.
• Figure 2: Schematic illustration of speckle pattern fabrication using a spray can or airbrush.
• Figure 3: Pinhole camera model.
• Figure 4: Mechanical camera positioning tool (a) camera's placement in relation to the specimen's surface; (b) diagram
• Figure 5: Exposure triangle.