Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep machine learning approach to develop a new asphalt pavement condition index
2020202 citationsHamed Majidifard, William G. Buttlar et al.profile →
Peers — A (Enhanced Table)
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Countries citing papers authored by William G. Buttlar
Since
Specialization
Citations
This map shows the geographic impact of William G. Buttlar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by William G. Buttlar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William G. Buttlar more than expected).
Fields of papers citing papers by William G. Buttlar
This network shows the impact of papers produced by William G. Buttlar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by William G. Buttlar. The network helps show where William G. Buttlar may publish in the future.
Co-authorship network of co-authors of William G. Buttlar
This figure shows the co-authorship network connecting the top 25 collaborators of William G. Buttlar.
A scholar is included among the top collaborators of William G. Buttlar based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with William G. Buttlar. William G. Buttlar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Buttlar, William G., et al.. (2019). Relating DC(T) Fracture Energy to Field Cracking Observations and Recommended Specification Thresholds for Performance-Engineered Mix Design. Transportation research circular.13 indexed citations
3.
McGovern, Megan E., et al.. (2015). Effectiveness of Rejuvenators on Aged Asphalt Concrete using Ultrasonic Non-collinear SubsurfaceWave Mixing. Materials Evaluation. 73(10). 1365–1376.4 indexed citations
4.
Buttlar, William G., et al.. (2015). Designing, producing, and constructing fine-graded hot mix asphalt on Illinois roadways.. IDEALS (University of Illinois Urbana-Champaign).1 indexed citations
5.
Buttlar, William G., et al.. (2013). Assessment of Emission Costs due to Maintenance and Rehabilitation to Reduce Pavement Roughness. Transportation Research Board 92nd Annual MeetingTransportation Research Board.
6.
Simpson, David, William G. Buttlar, & B J Dempsey. (2012). Best Practices for Bicycle Trail Pavement Construction and Maintenance in Illinois.1 indexed citations
7.
Buttlar, William G., Behzad Behnia, & Henrique Reis. (2011). An Acoustic Emission-Based Test to Determine Asphalt Binder and Mixture Embrittlement Temperature.7 indexed citations
8.
Behnia, Behzad, et al.. (2010). Determining the Embrittlement Temperature of Asphalt Binders Using an Acoustic Emission Approach.11 indexed citations
9.
Behnia, Behzad, Sarfraz Ahmed, Eshan Dave, & William G. Buttlar. (2010). Fracture Characterization of Asphalt Mixtures with Reclaimed Asphalt Pavement. International Journal of Pavement Research and Technology. 3(2). 72–78.7 indexed citations
10.
Al‐Qadi, Imad L., William G. Buttlar, Jongeun Baek, & Minkyum Kim. (2009). Cost-Effectiveness and Performance of Overlay Systems in Illinois Volume 1: Effectiveness Assessment of HMA Overlay Interlayer Systems Used to Retard Reflective Cracking. Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign).15 indexed citations
11.
Al‐Qadi, Imad L., William G. Buttlar, & Jongeun Baek. (2009). Cost-effectiveness and performance of overlay systems in Illinois. Volume 2, Guidelines for interlayer system selection decision when used in HMA overlays. Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign).5 indexed citations
Dave, Eshan, et al.. (2008). Integrated testing and modeling approach to develop accelerated testing sections for asphalt overlay-interlayer systems over PCC.1 indexed citations
14.
Buttlar, William G., et al.. (2007). Evaluation of Risk in End-Result Specifications for Asphalt Pavement Construction.2 indexed citations
15.
Buttlar, William G., Hyunwook Kim, & Michael P. Wagoner. (2006). Toward Realistic Heterogeneous Fracture Modeling of Asphalt MixtureUsing Disk-Shaped Compact Tension Test Based on Discontinuum Approach. Transportation Research Board 85th Annual MeetingTransportation Research Board.1 indexed citations
16.
Buttlar, William G., et al.. (2004). POTENTIAL APPLICATIONS OF THE HOLLOW CYLINDER TENSILE TESTER AS A SIMPLE PERFORMANCE TEST.1 indexed citations
17.
Buttlar, William G., et al.. (1999). UNDERSTANDING ASPHALT MASTIC BEHAVIOR THROUGH MICROMECHANICS (WITH DISCUSSION AND CLOSURE). Transportation Research Record Journal of the Transportation Research Board.13 indexed citations
18.
Buttlar, William G., Reynaldo Roque, & Namho Kim. (1996). Accurate asphalt mixture tensile strength. 163–172.10 indexed citations
19.
Buttlar, William G. & Reynaldo Roque. (1994). DEVELOPMENT AND EVALUATION OF THE STRATEGIC HIGHWAY RESEARCH PROGRAM MEASUREMENT AND ANALYSIS SYSTEM FOR INDIRECT TENSILE TESTING AT LOW TEMPERATURES. Transportation Research Record Journal of the Transportation Research Board.93 indexed citations
20.
Buttlar, William G., et al.. (1993). BEHAVIOR OF A BILAYER REINFORCED STRESSED TIMBER BRIDGE DECK UNDER STATIC AND DYNAMIC LOADS. Transportation Research Record Journal of the Transportation Research Board. 36–43.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
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Rankless may not fully capture the entirety of a scholar's output or impact.