Bonding performance between ultra-high performance concrete and asphalt pavement layer. (20th December 2021)

• Record Type: Journal Article
• Authors: Lu, Zhe; Feng, Zhen-gang; Yao, Dongdong; Li, Xinjun; Jiao, Xiaolai; Zheng, Kaixiang
• Is Part Of: Construction & building materials. Volume 312(2021)
• Publication Date: 2021-12-20
• Subjects: Bonding performance -- Ultra-high performance concrete -- Asphalt pavement layer -- Shear strength -- MATLAB -- Neural network; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2021.125375
• Languages: English
• ISSNs: 0950-0618

Abstract

Highlights: The surfaces of UHPC specimens were treated using four different techniques. The bonding performance between UHPC and asphalt pavement layers was evaluated. The MATLAB software was employed to build prediction model. Abstract: The bonding performance between the ultra-high performance concrete (UHPC) and asphalt pavement layers is crucial to prevent the interface of asphalt pavement and bridge deck from falling off. In order to improve the bonding performance between UHPC and asphalt pavement layers, the surfaces of UHPC specimens were treated using four different techniques: spreading gravels, spreading steel balls, pickings and grooving. Through the pull-out and shear tests, the bonding performance between UHPC and asphalt pavement layers was evaluated. The pull-out strength and shear strength were used to evaluate the influence of different surface patterns on the bonding performance between UHPC and asphalt mixture layers. Additionally, the MATLAB software was employed to extract the digital image characteristic parameters of UHPC surfaces, and the neural network was constructed using the characteristic parameters to predict the bonding performance between UHPC and asphalt pavement layers. Results show that the spreading gravels, spreading steel balls, broom picking, cutting machine picking and grooving can all improve the bonding performance. Among the techniques, the cutting machine picking has the most obvious improvement on the pull-out strength and shear strength. The accuracy of neural network prediction model is reliable, and the correlation between the predicted value and the measured value is high. The result is of great significance to enhance the bonding performance between UHPC bridge deck and asphalt pavement layer, thus improving the durability of UHPC bridge structures.