Development of the AI-powered ideal-E* test
Source abstract
Abstract
The dynamic modulus (E*) of asphalt mixtures is essential for Mechanistic-Empirical (ME) pavement designs but is seldom tested by Departments of Transportation (DOTs) due to the high cost and complexity of traditional E* tests. This study introduces an Artificial Intelligence (AI)-powered IDEAL-E* test that integrates IDEAL cracking tests at various temperatures, finite element analysis, and machine learning. This innovative approach addresses the limitations of existing models that struggle with evolving asphalt compositions. By simplifying the generation of E* data, it facilitates its use in AASHTO Pavement ME design software and is aligned with current practices with IDEAL cracking tests in DOTs’ QA and contractors’ QC labs.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Occupational Health and Safety Research · Risk and Safety Analysis · Medical Imaging and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Texas A&M Transportation Institute
Texas A&M University
Ingevity
Texas Department of Transportation
Funding
National Cooperative Highway Research Program
References (28)
Show source-provided references
1. 10.1080/10298436.2023.227056910.1080/10298436.2023.2270569 ↗
2. Bari, J., & Witczak, M. (2006). Development of a New revised version of the witczak E* predictive model for Hot Mix asphalt mixtures. Journal of Association of Asphalt Paving Technologists (AAPT), 75, 381–483.
3. 10.1061/(ASCE)MT.1943-5533.000372110.1061/(ASCE)MT.1943-5533.0003721 ↗
4. 10.1016/j.conbuildmat.2020.12054410.1016/j.conbuildmat.2020.120544 ↗
5. Ceylan, H., Kim, S., & Gopalakrishnan, K. (2007). Hot mix asphalt dynamic modulus prediction models using neural network approach. Intelligent Engineering Systems Through Artificial Neural Networks, 17, 117–124.
6. Chehab, G. R., Kim, Y. R., Schapery, R., Witczak, M., & Bonaquist, R. (2003). Characterization of asphalt concrete in uniaxial tension using a ViscoElastoPlastic model. Journal of AAPT, 72, 315–355.
7. 10.1080/14680629.2015.107699610.1080/14680629.2015.1076996 ↗
8. 10.1080/10298436.2020.184119110.1080/10298436.2020.1841191 ↗
9. 10.1061/(ASCE)0733-9399(2006)132:2(172)10.1061/(ASCE)0733-9399(2006)132:2(172) ↗
10. 10.3141/2127-2010.3141/2127-20 ↗
11. 10.3141/1860-0110.3141/1860-01 ↗
12. 10.1080/10298436.2020.186553310.1080/10298436.2020.1865533 ↗
13. 10.1016/j.conbuildmat.2020.11991210.1016/j.conbuildmat.2020.119912 ↗
14. 10.1139/L10-04010.1139/L10-040 ↗
15. 10.1016/j.conbuildmat.2021.12365510.1016/j.conbuildmat.2021.123655 ↗
16. 10.1080/14680629.2022.211206110.1080/14680629.2022.2112061 ↗
17. 10.1080/14680629.2024.237322210.1080/14680629.2024.2373222 ↗
18. 10.1080/14680629.2024.244447810.1080/14680629.2024.2444478 ↗
19. 10.3141/2057-1310.3141/2057-13 ↗
20. 10.1080/10298436.2016.113811310.1080/10298436.2016.1138113 ↗
21. 10.1016/j.measurement.2018.11.08110.1016/j.measurement.2018.11.081 ↗
22. 10.1016/j.conbuildmat.2020.11851310.1016/j.conbuildmat.2020.118513 ↗
23. 10.1080/14680629.2018.141871310.1080/14680629.2018.1418713 ↗
24. Mohammadi Golafshani, E., Behnood, A., & Karimi, M. M. (2021). Predicting the dynamic modulus of asphalt mixture using hybridized artificial neural network and grey wolf optimizer. International Journal of Pavement Engineering, 24(1), 1–11.
25. 10.1016/j.conbuildmat.2021.12358910.1016/j.conbuildmat.2021.123589 ↗
26. 10.1080/10298436.2023.225396410.1080/10298436.2023.2253964 ↗
27. 10.1016/j.conbuildmat.2014.11.01110.1016/j.conbuildmat.2014.11.011 ↗
28. 10.1061/(ASCE)MT.1943-5533.000054810.1061/(ASCE)MT.1943-5533.0000548 ↗