An efficient and robust method for predicting asphalt concrete dynamic modulus
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Hongren Gong | Baoshan Huang | Wei Hu | Yiren Sun | Pawel Polaczyk | Yuanshuai Dong | Bingye Han | Baoshan Huang | Pawel Polaczyk | Wei Hu | Hongren Gong | Yiren Sun | Yuanshuai Dong | Bingye Han
[1] Matthew W Witczak,et al. Development of a New Revised Version of the Witczak E* Predictive Model for Hot Mix Asphalt Mixtures (With Discussion) , 2006 .
[2] Sherif M. El-Badawy,et al. Performance of MEPDG Dynamic Modulus Predictive Models for Asphalt Concrete Mixtures: Local Calibration for Idaho , 2012 .
[3] Baoshan Huang,et al. Estimating Asphalt Concrete Modulus of Existing Flexible Pavements for Mechanistic-Empirical Rehabilitation Analyses , 2019, Journal of Materials in Civil Engineering.
[4] Hongren Gong,et al. Gradient Boosted Models for Enhancing Fatigue Cracking Prediction in Mechanistic-Empirical Pavement Design Guide , 2019, Journal of Transportation Engineering, Part B: Pavements.
[5] Jusang Lee,et al. Evaluating Dynamic Modulus for Indiana Mechanistic-Empirical Pavement Design Guide Practice , 2019 .
[6] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[7] Y. Richard Kim,et al. Development of Artificial Neural Network Predictive Models for Populating Dynamic Moduli of Long-Term Pavement Performance Sections , 2010 .
[8] S. Ranji Ranjithan,et al. Application of Artificial Neural Networks for Estimating Dynamic Modulus of Asphalt Concrete , 2009 .
[9] et al.,et al. Jupyter Notebooks - a publishing format for reproducible computational workflows , 2016, ELPUB.
[10] Sherif M. El-Badawy,et al. Evaluation of Witczak E* predictive models for the implementation of AASHTOWare-Pavement ME Design in the Kingdom of Saudi Arabia , 2014 .
[11] Xiaowen Zhao,et al. Using Artificial Neural Networks to Predict the Dynamic Modulus of Asphalt Mixtures Containing Recycled Asphalt Shingles , 2018 .
[12] N. Gibson,et al. A New Simplistic Model for Dynamic Modulus Predictions of Asphalt Paving Mixtures , 2006 .
[13] Mohamed M El-Basyouny,et al. SIMPLE PERFORMANCE TEST FOR SUPERPAVE MIX DESIGN , 2002 .
[14] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[15] Baoshan Huang,et al. A unified procedure for rapidly determining asphalt concrete discrete relaxation and retardation spectra , 2015 .
[16] Sesh Commuri,et al. Artificial Neural Network Modeling for Dynamic Modulus of Hot Mix Asphalt Using Aggregate Shape Properties , 2013 .
[17] Halil Ceylan,et al. Accuracy of Predictive Models for Dynamic Modulus of Hot-Mix Asphalt , 2009 .
[18] Derrick K. Rollins,et al. Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus , 2019 .
[19] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[20] S Yousefdoost,et al. Evaluation of dynamic modulus predictive models for typical Australian asphalt mixes , 2013 .
[21] Sherif El-Badawy,et al. Comparing Artificial Neural Networks with Regression Models for Hot-Mix Asphalt Dynamic Modulus Prediction , 2018, Journal of Materials in Civil Engineering.
[22] Qingli Dai,et al. Prediction of Dynamic Modulus and Phase Angle of Stone-Based Composites Using a Micromechanical Finite-Element Approach , 2010 .
[23] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[24] Sherif M. El-Badawy,et al. Using Artificial Neural Networks (ANNs) for Hot Mix Asphalt E* Predictions , 2016 .
[25] Xiang Shu,et al. Predicting Dynamic Modulus of Asphalt Mixtures with Differential Method , 2009 .
[26] R. Bonaquist,et al. Improved Hirsch model for estimating the modulus of hot-mix asphalt , 2015 .
[27] Rafiqul A. Tarefder,et al. Study of MEPDG Sensitivity Using Nonparametric Regression Procedures , 2014 .
[28] S. Keller,et al. Nonlinear least-squares data fitting in Excel spreadsheets , 2010, Nature Protocols.
[29] Sunghwan Kim,et al. Support Vector Machines Approach to HMA Stiffness Prediction , 2011 .
[30] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[31] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[33] Serji N. Amirkhanian,et al. Artificial Neural Network Approach to Estimating Stiffness Behavior of Rubberized Asphalt Concrete Containing Reclaimed Asphalt Pavement , 2009 .
[34] J. Friedman. Stochastic gradient boosting , 2002 .
[35] Yan Dong,et al. Road surface temperature prediction based on gradient extreme learning machine boosting , 2018, Comput. Ind..
[36] Matthew W Witczak,et al. Simple Performance Tests: Summary of Recommended Methods and Database , 2005 .
[37] Ali Behnood,et al. Estimation of the dynamic modulus of asphalt concretes using random forests algorithm , 2020, International Journal of Pavement Engineering.