ISSN: 1945-7553
CODEN: JTEVAB
Published Online: 23 May 2011
Page Count: 5

CT Image Entropy Analysis Technique for the Determination of Damage to Indirect Tensile Test of Asphalt Mixtures

Zhang, Qian
Associate Professor, School of Civil Engineering, Xi’an Univ. of Architecture and Technology, Xi’an, Shaanxi

Ma, Juan
Graduate Student, School of Civil Engineering, Xi’an Univ. of Architecture and Technology, Xi’an, Shaanxi

Bai, Yan
Lecturer, School of Science, Xi’an Univ. of Architecture and Technology, Xi’an, Shaanxi

(Received 12 October 2010; accepted 15 April 2011)

Abstract

This study identifies how the microstructure system of asphalt mixture is influenced by moisture and the Indirect Tensile Test (IDT) using digital image analysis methods. Concepts such as digital image segmentation technology, CT (Computer Tomography) value, grey scale of the image, edge detection, and entropy are introduced and incorporated to describe the image characteristics. The impact of moisture and the IDT test on the mixture is analyzed on 24 scanning sections of two specimens. Two moisture conditions are designed to treat the specimens separately before the indirect tension test according to the specification. The original internal structures of the specimens and the corresponding internal structures after the IDT test are obtained through CT scanning. An edge detection process is conducted to find the micro point contact between the binder and the aggregate using a Laplacian arithmetic operator. Differences in the destruction degree of different moisture conditions in IDT are shown through image entropy calculation. A statistical hypothesis test method, the Student t-test, is introduced to examine the significance of the calculation results. From the viewpoint of statistics, the test and calculation results of the 24 sections of the two specimens are of representative significance.

Keywords:
highway engineering, entropy, CT image, asphalt mixture, indirect tensile test (IDT)

Paper ID: JTE103446
DOI: 10.1520/JTE103446
ASTM International is a member of CrossRef.