Effects of ageing, temperature and frequency-dependent properties of asphalt concrete on top-down cracking

2020 ◽  
pp. 1-21
Author(s):  
Mohsen Alae ◽  
Yanqing Zhao ◽  
Zhen Leng
Keyword(s):  
Asphalt Concrete ◽  
Ageing Temperature ◽  
Top Down ◽  
Frequency Dependent

scholarly journals Evaluation of top-down crack propagation in asphalt pavement under dual tire loading

2019 ◽  
Vol 46 (8) ◽  
pp. 704-711
Author(s):  
Mohsen Alae ◽  
Hamzeh F. Haghshenas ◽  
Yanqing Zhao
Keyword(s):  
Asphalt Concrete ◽  
Asphalt Pavement ◽  
Propagation Rate ◽  
Base Layer ◽  
Major Type ◽  
Fracture Mechanisms ◽  
Top Down ◽  
Pavement Distress ◽  
Paris Law

Top-down cracking (TDC) has been recognized worldwide and is regarded as a major type of asphalt pavement distress. In this study, fracture mechanisms behind the TDC propagation and fatigue life of pavements were investigated under dual tire loads using finite element (FE) analysis. By considering the most influencing factors on TDC propagation, stress intensity factors (SIF), including KI and KII, were calculated at critical transverse locations. According to Modes I and II SIF, a greater SIF indicates a faster rate of TDC propagation. The SIF results indicated that considering temperature gradient in asphalt concrete (AC) layer is necessary in determination of critical SIF, and KI and KII are not distributed uniformly within the AC depth. In addition, TDC growth rate significantly depends on AC thickness and base layer type. Finally, the number of load repetitions for TDC propagation rate at different transverse locations is predicted based on Paris’ law equation.

scholarly journals Classification of Surface Pavement Cracks as Top-down, Bottom-up, and Cement-Treated Reflective Cracking Based on Deep Learning Methods

2021 ◽  
Author(s):  
Nirmal Dhakal ◽  
Mostafa A. Elseifi ◽  
Zia U. Zihan ◽  
Zhongjie Zhang ◽  
Christophe N. Fillastre ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Asphalt Concrete ◽  
Fatigue Cracking ◽  
Ann Model ◽  
Reflective Cracking ◽  
Top Down ◽  
Bottom Up ◽  
Concrete Layer ◽  
Artificial Neural Network Ann

The treatment and repair strategies of reflective and fatigue cracking that initiate at the pavement surface (i.e. top-down cracking) and at the bottom of the asphalt concrete layer (i.e. bottom-up cracking) are noticeably different. However, pavement engineers are facing difficulties in identifying these cracks in the field as they usually appear in visually identical patterns. The objective of this study was to develop Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) applications to differentiate and classify top-down, bottom-up, and cement-treated reflective cracking in in-service pavements using deep-learning models. The developed CNN model achieved an accuracy of 93.8% in the testing and 91% in the validation phases and the ANN model showed an overall accuracy of 92%. The ANN classification tool was developed based on variables related to pavement and crack characteristics including age, Average Daily Traffic , thickness of Asphalt Concrete layer, type of base, crack orientation and location.

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