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PhD Final Defense for Zehui Zhu

Event Type
Seminar/Symposium
Sponsor
Civil and Environmental Engineering
Location
3019 CEE Building (Hydro)
Date
Feb 14, 2023   1:00 pm  
Views
66
Originating Calendar
CEE Seminars and Conferences

Innovative Practical Crack Propagation Measurement of Asphalt Concrete Specimens

Advisor: Professor Imad L. Al-Qadi

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). Multi-seed 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 (𝛿􀯖 ) to define the onset of cleavage

fracture. The proposed threshold 𝛿􀯖 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.

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