journal article·2013-08

Investigation of fracture properties of California asphalt mixtures using semicircular bending and beam fatigue tests

Hamed Hakimelahi, Shadi Saadeh, John Harvey

Road Materials and Pavement Design· Volume 14· Issue sup2
Publisher page

Abstract

Fatigue cracking is a primary distress in asphalt concrete (AC) due to repetitive stresses and strains caused by both traffic loading and environmental factors. The fatigue resistance of AC is investigated by a number of fatigue tests. The main objective of this study is to investigate the use of the semi-circular bending (SCB) test as a quality assurance/quality control (QA/QC) measure for field construction. The comparison of fracture properties of seven AC mixtures from the SCB and beam fatigue test (BFT) is conducted. The J c and K 1c values for the seven mixtures were determined. The BFT was performed on the same mixtures and the initial stiffness, N f and plateau value (PV) were determined. The coefficient of variation (CV) ranged from 0% to 38% for J c and from 0% to 35% for K 1c. The CV ranged from 10% to 93% for the initial stiffness, 2% to 83% for N f and 8% to 167% for PV. The SCB J c and BFT N f and PV indicated lower fracture properties for PG64-10RAP (LIME), AN-HMA and WMA-ADVERA mixtures than other mixtures. The BFT N f and PV achieved similar ranking for all mixtures. There is good correlation between N f and PV with J c, and not a good correlation between the initial stiffness with J c, N f and PV. This has indicated that the initial stiffness is not a good representative for fracture properties of AC while J c, N f and PV are better indicators. The results of this study indicate that the SCB test has a great potential as a QA/QC test of fracture properties of asphalt mixtures.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Asphalt Pavement Performance Evaluation · Material Properties and Failure Mechanisms · Geotechnical Engineering and Underground Structures

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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