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International Journal of Pavement Engineering Volume 21, 2020 - Issue 9
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Articles

Artificial neural network-based prediction of field permeability of hot mix asphalt pavement layers

M. K. NivedyaCivil and Environmental Engineering Department, Worcester Polytechnic Institute (WPI), Worcester, MA, USACorrespondence[email protected] [email protected]
&
Rajib B. MallickCivil and Environmental Engineering Department, Worcester Polytechnic Institute (WPI), Worcester, MA, USA
Pages 1057-1068 | Received 09 Nov 2017, Accepted 25 Aug 2018, Published online: 14 Sep 2018

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Sylvester Inkoom, John Sobanjo, Adrian Barbu & Xufeng Niu. (2019) Prediction of the crack condition of highway pavements using machine learning models. Structure and Infrastructure Engineering 15:7, pages 940-953.
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Articles from other publishers (10)

Leilei Chen, Gang Liu, Gengshen Pan & Zhendong Qian. (2023) Investigation on the pore water pressure in steel bridge deck pavement under the coupling effect of water and vehicle load. Construction and Building Materials 409, pages 134021.
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Xianqi Huang, Ruijin Ma, Hanyu Yang, Chi Feng & Kun Li. (2023) Comparative evaluation of data mining methods in predicting the water vapor permeability of cement-based materials. Building Simulation 16:6, pages 853-867.
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Tanvi Gupta & Rajesh Kumar. (2023) A novel feed-through Elman neural network for predicting the compressive and flexural strengths of eco-friendly jarosite mixed concrete: design, simulation and a comparative study. Soft Computing.
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Ashik Bellary & S. N. Suresha. (2022) ANN Model to Predict Joint Stiffness of White-topped Pavements Using Falling Weight Deflectometer (FWD) Data. International Journal of Pavement Research and Technology 16:2, pages 370-384.
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Abdulrahman Hamid, Hassan Baaj & Mohab El-Hakim. (2022) Predicting the Recovery and Nonrecoverable Compliance Behaviour of Asphalt Binders Using Artificial Neural Networks. Processes 10:12, pages 2633.
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M. A. Dalhat & Sami A. Osman. (2022) Artificial Neural Network Modeling of Theoretical Maximum Specific Gravity for Asphalt Concrete Mix. International Journal of Pavement Research and Technology.
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Yulong Zhao, Ke Zhang, Yao Zhang, Yaofei Luo & Shaoquan Wang. (2022) Prediction of air voids of asphalt layers by intelligent algorithm. Construction and Building Materials 317, pages 125908.
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Gourav Goel, S. N. Sachdeva & Mahesh Pal. (2021) Modelling of Tensile Strength Ratio of Bituminous Concrete Mixes Using Support Vector Machines and M5 Model Tree. International Journal of Pavement Research and Technology 15:1, pages 86-97.
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Shuyin Feng, Paul J. Vardanega, Maximilian James & Erdin Ibraim. (2021) Studying hydraulic conductivity of asphalt concrete using a database. Transportation Engineering 3, pages 100040.
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M. K. Nivedya & Rajib B. Mallick. (2020) A multi-structure multi-run range (MSMRR) approach for using machine learning with constrained data in pavement engineering. SN Applied Sciences 2:3.
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