Innovative practical crack propagation measurement of asphalt concrete specimens

Zhu, Zehui

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https://hdl.handle.net/2142/120495 Copy

Description

Title

Innovative practical crack propagation measurement of asphalt concrete specimens

Author(s)

Zhu, Zehui

Issue Date

2023-03-08

Director of Research (if dissertation) or Advisor (if thesis)

Al-Qadi, Imad L.

Doctoral Committee Chair(s)

Al-Qadi, Imad L.

Committee Member(s)

• Lambros, John
• Roesler, Jeffery R.
• Birgisson, Bjorn
• Hajj, Ramez

Department of Study

Civil & Environmental Eng

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Asphalt Concrete, Crack Propagation, Digital Image Correlation, Computer Vision, Deep Learning

Abstract

Approximately 95 percent of paved roads in the United States are surfaced with asphalt concrete (AC). Cracking is a common failure mode in pavements. The cracking potential of AC significantly affects pavement durability and serviceability. Numerous tests have been developed and employed to predict AC cracking potential. Accurate crack measurement during testing is crucial. However, there is a lack of an efficient and accurate crack propagation measurement technique. This dissertation aimed to develop an automated crack measurement technique that can efficiently deliver accurate results for AC cracking tests. To achieve this goal, a generalized crack detection framework was developed using fundamental fracture mechanics theory and digital image correlation (DIC). Multiseed incremental reliability-guided DIC analysis was proposed to solve the decorrelation issue due to large deformation and discontinuities. A robust method was developed to detect cracks based on displacement fields. It uses critical crack tip opening displacement (δc) to define the onset of cleavage fracture. The proposed threshold δc has a physical meaning and can be easily determined from DIC measurement. To enable automated crack propagation measurement in AC cracking tests, a deep neural network, CrackPropNet, was trained. An image library representing the diversified cracking behavior of AC was built for supervised learning. CrackPropNet could accurately and efficiently measure crack propagation with an F-1 of 0.781 at a running speed of 26 frame-per-second. The model showed promising generalization on fundamentally different images. An accurate measurement can only be achieved when the camera’s principal axis is perpendicular to the specimen surface. However, this requirement may not be met during testing due to device constraints. A simple and reliable method was proposed to correct errors induced by non-perpendicularity. The method is based on image feature matching and rectification. A theoretical analysis was performed to quantify the effect of a non-perpendicular camera alignment on measurement accuracy. The proposed method showed satisfactory accuracy in compensating errors induced by non-perpendicularity. It was verified as a valid approach assisting the CrackPropNet in measuring crack propagation with a non-perpendicular camera alignment. Engineers and practitioners could use smartphones to monitor crack development under complex imaging environments as a part of AC mix design and quality control/quality assurance. In addition, this technique may assist researchers in characterizing cracking phenomena, evaluating AC cracking potential, validating test protocols, and verifying theoretical models.

Graduation Semester

2023-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Zehui Zhu

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