Characterization of temperature- and rate-dependent fracture properties of fine aggregate bituminous mixtures using an integrated numerical-experimental approach. (July 2017)

• Record Type: Journal Article
• Authors: Aragão, Francisco Thiago Sacramento; Badilla-Vargas, Gustavo Adolfo; Hartmann, Diego Arthur; Oliveira, Alex Duarte de; Kim, Yong-Rak
• Is Part Of: Engineering fracture mechanics. Volume 180(2017)
• Page Start: 195
• Page End: 212
• Publication Date: 2017-07
• Subjects: Bituminous mixtures -- Viscoelastic fracture -- Finite element method -- Cohesive zone; Fracture mechanics -- Periodicals; Rupture, Mécanique de la -- Périodiques; Fracture mechanics; Periodicals; 620.112605
• DOI: 10.1016/j.engfracmech.2017.05.036
• Languages: English
• ISSNs: 0013-7944

Abstract

Highlights: Fracture characteristics of bituminous mixes are temperature- and rate-dependent. FAM fracture properties are key information for modeling damage in asphalt concrete. Fracture properties can be determined using the proposed numerical-experimental approach. Abstract: This paper employs an integrated numerical-experimental approach to evaluate the temperature- and rate-dependent fracture characteristics of four fine aggregate bituminous matrices. Two bending tests (semicircular bending, and single-edge notched beam) and one tension test (disk-shaped compact tension) were performed in the laboratory at three temperatures (−10 °C, 10 °C, and 25 °C) and three loading rates (0.5 mm/min, 1.0 mm/min, and 2.0 mm/min). The fracture tests were further simulated using a computational model based on the finite element method that is incorporated with material viscoelasticity and cohesive zone fracture. Two cohesive zone fracture parameters, i.e., cohesive strength and fracture energy, were determined via a calibration process between experimental and numerical results. The results obtained indicated that temperature- and rate-dependent fracture characteristics are obvious in viscoelastic bituminous mixtures, and an accurate identification of such characteristics is a key step towards the implementation of successful computational microstructure predictive models. This would lead to core insights into the effects of constituents on the overall mixture performance, with significant savings in experimental costs and time.