Development of the AI-powered ideal-E* test

被引：0

作者：

Hu, Sheng ^{[1
]}

Zhou, Noah ^{[2
]}

Steger, Richard ^{[3
]}

Bausano, Jason ^{[3
]}

Mahmoud, Enad ^{[4
]}

Zhou, Fujie ^{[1
]}

机构：

[1] Texas A&M Transportat Inst, TAMU 3135, College Stn, TX 77843 USA

[2] Texas A&M Univ, College Stn, TX USA

[3] Ingevity, N Charleston, SC USA

[4] Texas Dept Transportat, Austin, TX USA

来源：

ROAD MATERIALS AND PAVEMENT DESIGN | 2025年

关键词：

Dynamic modulus; pavement design; artificial intelligence; ideal cracking test; DYNAMIC MODULUS; PREDICTION MODELS;

D O I：

10.1080/14680629.2025.2498633

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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.

引用

页数：25

共 28 条

[1] Asphalt concrete dynamic modulus prediction: Bayesian neural network approach
Asadi, Babak
Hajj, Ramez
Al-Qadi, Imad L.
[J]. INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2023, 24 (02)
[2] Bari J, 2006, J ASSOC ASPHALT PAV, V75, P381
[3] Evaluation of ANN-Based Dynamic Modulus Models of Asphalt Mixtures
Barugahare, Javilla
Amirkhanian, Armen N.
Xiao, Feipeng
Amirkhanian, Serji N.
[J]. JOURNAL OF MATERIALS IN CIVIL ENGINEERING, 2021, 33 (06)
[4] A machine learning study of the dynamic modulus of asphalt concretes: An application of M5P model tree algorithm
Behnood, Ali
Daneshvar, Dana
[J]. CONSTRUCTION AND BUILDING MATERIALS, 2020, 262
[5] Ceylan H., 2007, Intelligent Engineering Systems Through Artificial Neural Networks, V17, P117
[6] Chehab GR, 2003, JOURNAL OF THE ASSOCIATION OF ASPHALT PAVING TECHNOLOGISTS, VOL 72, P315
[7] Christensen D., 2015, Journal of Road Materials and Pavement Design, V16, P1, DOI [https://doi.org/10.1080/14680629.2015.1076996, DOI 10.1080/14680629.2015.1076996]
[8] Prediction of dynamic modulus of asphalt concrete using hybrid machine learning technique
Eleyedath, Abhary
Swamy, Aravind Krishna
[J]. INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2022, 23 (06) : 2083 - 2098
[9] Viscoelastic modeling and field validation of flexible pavements
Elseifi, MA
Al-Qadi, IL
Yoo, PJ
[J]. JOURNAL OF ENGINEERING MECHANICS, 2006, 132 (02) : 172 - 178
[10] Application of Artificial Neural Networks for Estimating Dynamic Modulus of Asphalt Concrete
Far, Maryam Sadat Sakhaei
Underwood, B. Shane
Ranjithan, S. Ranji
Kim, Y. Richard
Jackson, Newton
[J]. TRANSPORTATION RESEARCH RECORD, 2009, (2127) : 173 - 186

← 1 2 3 →