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International Journal of Pavement Engineering Volume 22, 2021 - Issue 2
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Articles

Neural networks for fatigue cracking prediction using outputs from pavement mechanistic-empirical design

Hongren Gonga Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA
,
Yiren Sunb School of Transportation and Logistics, Dalian University of Technology, Dalian, China
,
Wei Hua Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USAORCID Iconhttps://orcid.org/0000-0002-2637-5463
ORCID Icon &
Baoshan Huanga Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA;c Department of Transportation, Tongji University, Shanghai, ChinaCorrespondence[email protected]
Pages 162-172 | Received 24 Oct 2018, Accepted 04 Feb 2019, Published online: 17 Feb 2019

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Osama Abu Daoud, Omar Albatayneh, Lars Forslof & Khaled Ksaibati. (2022) Validating the practicality of utilising an image classifier developed using TensorFlow framework in collecting corrugation data from gravel roads. International Journal of Pavement Engineering 23:11, pages 3797-3808.
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Feng Xiao, Xinyu Chen, Jianchuan Cheng, Shunxin Yang & Yang Ma. (2022) Establishment of probabilistic prediction models for pavement deterioration based on Bayesian neural network. International Journal of Pavement Engineering 0:0, pages 1-16.
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Articles from other publishers (19)

Weimin Song, Yuxin Fan, Hao Wu & Liang Zhou. (2024) I-II mixed fracture characterization of hot mix asphalt under tensile test using notched semi-circular specimens. Theoretical and Applied Fracture Mechanics 129, pages 104200.
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Manzhe Xiao, Rong Luo, Yu Chen & Xinmin Ge. (2023) Prediction model of asphalt pavement functional and structural performance using PSO-BPNN algorithm. Construction and Building Materials 407, pages 133534.
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Haisheng Zhao, Shiping Cui, Zhen Li, Shaobin Wang, Lin Wang, Wensheng Zhang, Chunhua Su, Peiyu Zhang & Shijie Ma. (2023) Study on the Influence Factors of the Dynamic Property of the Polyurethane Mixture with Dense Gradation. Coatings 13:8, pages 1465.
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Haisheng Zhao, Wenbin Gao, Shiping Cui, Wensheng Zhang, Shijie Ma, Baoji Miao, Chunhua Su, Peiyu Zhang, Xiaoyan Wang & Jincheng Wei. (2023) Study on the Dynamic Performance of PU and SBS-Modified Asphalt Mixtures with Dense Gradation. Coatings 13:7, pages 1143.
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Haisheng Zhao, Xiufen Wang, Shiping Cui, Bin Jiang, Shijie Ma, Wensheng Zhang, Peiyu Zhang, Xiaoyan Wang, Jincheng Wei & Shan Liu. (2023) Study on the Phase Angle Master Curve of the Polyurethane Mixture with Dense Gradation. Coatings 13:5, pages 909.
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Yang Song, Yizhuang David Wang, Xianbiao Hu & Jenny Liu. (2022) An Efficient and Explainable Ensemble Learning Model for Asphalt Pavement Condition Prediction Based on LTPP Dataset. IEEE Transactions on Intelligent Transportation Systems 23:11, pages 22084-22093.
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Yong Deng & Xianming Shi. (2022) An Accurate, Reproducible and Robust Model to Predict the Rutting of Asphalt Pavement: Neural Networks Coupled With Particle Swarm Optimization. IEEE Transactions on Intelligent Transportation Systems 23:11, pages 22063-22072.
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Qiao Dong, Xueqin Chen, Shi Dong & Fujian Ni. (2022) Data Analysis in Pavement Engineering: An Overview. IEEE Transactions on Intelligent Transportation Systems 23:11, pages 22020-22039.
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Jian Liu, Fangyu Liu, Hongren Gong, Ebenezer O. Fanijo & Linbing Wang. (2022) Improving asphalt mix design by predicting alligator cracking and longitudinal cracking based on machine learning and dimensionality reduction techniques. Construction and Building Materials 354, pages 129162.
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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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Siddharth Suman, Kishan Dwivedi, Samanvay Anand & Himanshu Pathak. (2022) XFEM–ANN approach to predict the fatigue performance of a composite patch repaired aluminium panel. Composites Part C: Open Access 9, pages 100326.
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Osama Abu Daoud & Khaled Ksaibati. (2022) Artificial neural network-based roughness prediction models for gravel roads considering land use. Innovative Infrastructure Solutions 7:3.
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Jiale Li, Zhishuai Zhang, Xuefei Wang & Weixi Yan. (2022) Intelligent decision-making model in preventive maintenance of asphalt pavement based on PSO-GRU neural network. Advanced Engineering Informatics 51, pages 101525.
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Yu Chen & Robert L. Lytton. (2021) Exploratory analysis of LTPP faulting data using statistical techniques. Construction and Building Materials 309, pages 125025.
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Xi Jiang, Miaomiao Zhang, Rui Xiao, Pawel Polaczyk, Yun Bai & Baoshan Huang. (2021) An investigation of structural responses of inverted pavements by numerical approaches considering nonlinear stress-dependent properties of unbound aggregate layer. Construction and Building Materials 303, pages 124505.
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Mingyang Gong, Yiren Sun & Jingyun Chen. (2021) Mechanical response analysis of asphalt pavement on curved concrete bridge deck using a mesostructure-based multi-scale method. Construction and Building Materials 285, pages 122858.
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Lizette Tello-Cifuentes, Marcela Aguirre-Sánchez, Jean P. Díaz-Paz & Francisco Hernández. (2021) Evaluación de daños en pavimento flexible usando fotogrametría terrestre y redes neuronales. TecnoLógicas 24:50, pages e1686.
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Omar Albatayneh, Lars ForslöfKhaled Ksaibati. (2020) Image Retraining Using TensorFlow Implementation of the Pretrained Inception-v3 Model for Evaluating Gravel Road Dust. Journal of Infrastructure Systems 26:2.
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Hassan Divandari. (2019) Predict of Asphalt Rutting Potential Based on IDT and Validation with ANN. Journal of Applied Engineering Sciences 9:2, pages 131-138.
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