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Author Archives: mayecreate

An overview of smartphone technology for citizen-centered, real-time and scalable civil infrastructure monitoring

Published: 10/31/2018

By: Amir H. Alavi , William G. Buttlar

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Modern smartphones are equipped with various sensors along with on-board storage, computing and communication capabilities. Owing to these features, they can become an intelligent, scalable, autonomous and potentially cost-free component of the next generation civil infrastructure monitoring systems in future smart cities. Over the past few years, there has been a growing interest in the

Nondestructive Low-Temperature Cracking Characterization of Asphalt Materials

Published: 02/05/2019

By: Behzad Behnia, William G. Buttlar, and Henrique Reis

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An acoustic-emission approach to evaluate the low-temperature cracking performance of asphalt binders is presented. The acoustic activity of a thin film of asphalt binder bonded to a granite substrate is monitored while the layer is exposed to decreasing temperatures from around 20°C to approximately −50°C. Results of eight different asphalt binders at three different aging

Performance Characteristics Of Modern Recycled Asphalt Mixes in Missouri, Including Ground Tire Rubber, Recycled Roofing Shingles, and Rejuvenators

Published: 01/10/2019

By: William G. Buttlar, Jim Meister, Behnam Jahangiri, and Hamed Majidifard

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A comprehensive lab and field investigation was carried out to evaluate the performance of recycled asphalt mixtures in Missouri by researchers at the University of Missouri-Columbia, in collaboration with the Missouri Department of Transportation and the Midwest Transportation Center. Sixteen field sections were evaluated, including a number of sections from the recent Long-Term Pavement Performance

An efficient mixed-mode rate-dependent cohesive fracture model using sigmoidal functions

Published: 12/11/2017

By: Oliver Giraldo-Londoño, Daniel W. Spring, Glaucio H. Paulino, William G. Buttlar

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Rate-dependent fracture processes can be investigated by means of cohesive zone models (CZMs). For instance, one approach enhances existing CZMs with phenomenological expressions used to represent the fracture energy, cohesive strength, and/or maximum crack opening as a function of the crack opening rate. Another approach assumes a viscoelastic CZM in front of the crack tip.

Inverse Estimation of Cohesive Fracture Properties of Asphalt Mixtures Using an Optimization Approach

Published: 01/25/2017

By: B. C. HillO. Giraldo-LondoñoG. H. PaulinoW. G. Buttlar

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Tensile cracking in asphalt pavements due to vehicular and thermal loads has become an experimental and numerical research focus in the asphalt materials community. Previous studies have used the discrete element method (DEM) to study asphalt concrete fracture. These studies used trial-and-error to obtain local fracture properties such that the DEM models approximate the experimental

Evaluation of polymer modification in asphalt mixtures through digital image correlation and performance space diagrams

Published: 06/21/2016

By: Brian Hill, William G. Buttlar

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Asphalt polymer modification typically yields improved mixture performance. Little research has been conducted to determine how SBS polymer affects mixture performance in interaction plots. Furthermore, research to date has not used digital image correlation to measure the fracture process zone (FPZ) size and its compression with crack propagation. Mixtures containing five distinct asphalt binders were

Performance Evaluation of Asphalt Mixtures with Reclaimed Asphalt Pavement and Recycled Asphalt Shingles in Missouri

Published: 02/03/2019

By: Behnam Jahangiri, Hamed Majidifard, James Meister, and William G. Buttlar

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This study investigates the performance of eighteen different dense-graded asphalt mixtures paved in Missouri. The sections contain a wide range of reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS), and different types of additives. The large number of sections investigated and the associated breadth of asphalt mixtures tested provided a robust data set to

New machine learning-based prediction models for fracture energy of asphalt mixtures

Published: 11/26/2018

By: Hamed Majidifard, Behnam Jahangiri, William G. Buttlar, Amir H. Alavi

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This paper presents innovative machine learning methods called gene expression programming (GEP) and hybrid artificial neural network/simulated annealing (ANN/SA) to predict the fracture energy of asphalt mixture specimens. The GEP and ANN/SA models are developed using an experimental database including a number of disk-shaped compact tension (DC(T)) test results for fracture energy. The fracture energy

Spiral cracking pattern in asphalt materials

Published: 11/21/2016

By: Behzad Behnia, William G. Buttlar, Henrique Reis

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The present work for the first time reports observations and modeling to characterize three-dimensional spiral-shaped fracture patterns in a bi-axially stressed layer of asphalt material bonded to an aluminum substrate. Five different asphalt materials with different fracture characteristics are investigated. A logarithmic spiral model was found to mathematically represent the 3D helix-shaped cracks observed. A

Field assessment of oxidative aging in asphalt concrete pavements with unknown acoustic properties

Published: 04/26/2016

By: Megan E. McGovern, William G. Buttlar, Henrique Reis

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Because in the field asphalt concrete acoustic properties (i.e., velocities and corresponding attenuations) are unknown, a modified version of the non-collinear wave mixing method is proposed to evaluate oxidative aging levels. Longitudinal transducers mounted on angle wedges are employed to launch subsurface dilatational waves to allow evaluation when there is only access to one side

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