Regeneration of pavement surface textures using M‐sigmoid‐normalized generative adversarial networks
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QUAN LIU | M. Oeser | Jiale Lu | B. Pan | Pengfei Liu | Weixin Ren
[1] Y. Miao,et al. Data Augmentation and Intelligent Recognition in Pavement Texture Using a Deep Learning , 2022, IEEE Transactions on Intelligent Transportation Systems.
[2] Kelvin C. P. Wang,et al. Intelligent pixel‐level detection of multiple distresses and surface design features on asphalt pavements , 2022, Comput. Aided Civ. Infrastructure Eng..
[3] Xiongyao Xie,et al. Sparse‐sensing and superpixel‐based segmentation model for concrete cracks , 2022, Comput. Aided Civ. Infrastructure Eng..
[4] S. Caro,et al. Computational generation of multiphase asphalt nanostructures using random fields , 2022, Comput. Aided Civ. Infrastructure Eng..
[5] Ouming Xu,et al. Asphalt pavement macrotexture reconstruction from monocular image based on deep convolutional neural network , 2022, Comput. Aided Civ. Infrastructure Eng..
[6] Boqiang Xu,et al. A night pavement crack detection method based on image‐to‐image translation , 2022, Comput. Aided Civ. Infrastructure Eng..
[7] Jun Chen,et al. A novel U‐shaped encoder–decoder network with attention mechanism for detection and evaluation of road cracks at pixel level , 2022, Comput. Aided Civ. Infrastructure Eng..
[8] Wout Van Hauwermeiren,et al. A scalable, self‐supervised calibration and confounder removal model for opportunistic monitoring of road degradation , 2022, Comput. Aided Civ. Infrastructure Eng..
[9] Allen A. Zhang,et al. Integrated FFT and XGBoost framework to predict pavement skid resistance using automatic 3D texture measurement , 2021, Measurement.
[10] Dawei Wang,et al. Stress distributions in the textures of prefabricated pavement surface created with the assistance of 3D printing technology , 2021, International Journal of Pavement Engineering.
[11] Miao Yu,et al. The Effect of Pavement Texture on the Performance of Skid Resistance of Asphalt Pavement Based on the Hilbert-Huang Transform , 2021, Arabian Journal for Science and Engineering.
[12] Markus Oeser,et al. Stability prediction for asphalt mixture based on evolutional characterization of aggregate skeleton , 2021, Comput. Aided Civ. Infrastructure Eng..
[13] Zhifei Tan,et al. An efficient model for predicting the dynamic performance of fine aggregate matrix , 2021, Comput. Aided Civ. Infrastructure Eng..
[14] Yishun Li,et al. Cross‐scene pavement distress detection by a novel transfer learning framework , 2021, Comput. Aided Civ. Infrastructure Eng..
[15] Yanjun Qiu,et al. An improved differential box counting method to measure fractal dimensions for pavement surface skid resistance evaluation , 2021 .
[16] Yichang James Tsai,et al. Convolutional neural network for automated classification of jointed plain concrete pavement conditions , 2020, Comput. Aided Civ. Infrastructure Eng..
[17] Yoshihide Sekimoto,et al. Generative adversarial network for road damage detection , 2020, Comput. Aided Civ. Infrastructure Eng..
[18] Mohammed Bennamoun,et al. Deep Learning for 3D Point Clouds: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Zhaoyun Sun,et al. Virtual generation of pavement crack images based on improved deep convolutional generative adversarial network , 2021, Eng. Appl. Artif. Intell..
[20] João Paulo Papa,et al. Deep learning techniques for recommender systems based on collaborative filtering , 2020, Expert Syst. J. Knowl. Eng..
[21] Ian Goodfellow,et al. Generative adversarial networks , 2020, Commun. ACM.
[22] Kelvin C. P. Wang,et al. Friction-ResNets: Deep Residual Network Architecture for Pavement Skid Resistance Evaluation , 2020 .
[23] Hongduo Zhao,et al. Numerical analysis of hydroplaning behaviour by using a tire–water-film–runway model , 2020, International Journal of Pavement Engineering.
[24] Kelvin C. P. Wang,et al. Multiresolution analysis of three-dimensional (3D) surface texture for asphalt pavement friction estimation , 2020, International Journal of Pavement Engineering.
[25] Yuanyuan Wang,et al. Evaluation of Pavement Skid Resistance Using Surface Three-Dimensional Texture Data , 2020, Coatings.
[26] Vuong Minh Le,et al. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation , 2020, Sustainability.
[27] Hojjat Adeli,et al. A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals , 2019, Expert Syst. J. Knowl. Eng..
[28] Kelvin C. P. Wang,et al. Finite Element Method-Based Skid Resistance Simulation Using In-Situ 3D Pavement Surface Texture and Friction Data , 2019, Materials.
[29] Ali Aryo Bawono,et al. Skid resistance and surface water drainage performance of engineered cementitious composites for pavement applications , 2019, Cement and Concrete Composites.
[30] Hojjat Adeli,et al. A dynamic ensemble learning algorithm for neural networks , 2019, Neural Computing and Applications.
[31] Khalid M. Mosalam,et al. Deep leaf‐bootstrapping generative adversarial network for structural image data augmentation , 2019, Comput. Aided Civ. Infrastructure Eng..
[32] Weifeng Huang,et al. Bi-fractal feature of bi-Gaussian stratified surfaces , 2019, Tribology International.
[33] João Paulo Papa,et al. FEMa: a finite element machine for fast learning , 2019, Neural Computing and Applications.
[34] M. Hossain,et al. Evaluation of pavement surface texture at the network level , 2018, Nondestructive Testing and Evaluation.
[35] Hojjat Adeli,et al. Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes , 2018, Journal of Construction Engineering and Management.
[36] Jie Gao,et al. Convolutional Neural Network for Asphalt Pavement Surface Texture Analysis , 2018, Comput. Aided Civ. Infrastructure Eng..
[37] Zhen Zhang,et al. Characterization and identification of asphalt mixtures based on Convolutional Neural Network methods using X-ray scanning images , 2018, Construction and Building Materials.
[38] Charles Holzschuher,et al. Precision Assessment of the Florida Texture Meter in Hot Mix Asphalt , 2018, Journal of Transportation Engineering, Part B: Pavements.
[39] Mohammad Ali Khasawneh,et al. Macrotexture characterisation of laboratory-compacted hot-mix asphalt specimens using a new asphalt polishing machine , 2018 .
[40] Jacob Abernethy,et al. On Convergence and Stability of GANs , 2018 .
[41] Nanfeng Xiao,et al. Improved Boundary Equilibrium Generative Adversarial Networks , 2018, IEEE Access.
[42] Kelvin C. P. Wang,et al. Wavelet based macrotexture analysis for pavement friction prediction , 2018 .
[43] Kumar Anupam,et al. Finite Element Framework for the Computation of Runway Friction of Aircraft Tires , 2017 .
[44] Kelvin C. P. Wang,et al. Novel Macro- and Microtexture Indicators for Pavement Friction by Using High-Resolution Three-Dimensional Surface Data , 2017 .
[45] Hojjat Adeli,et al. NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization , 2017 .
[46] Hojjat Adeli,et al. A New Neural Dynamic Classification Algorithm , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[47] A. Tuononen,et al. Application of three-dimensional printing to pavement texture effects on rubber friction , 2017 .
[48] Hojjat Adeli,et al. Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete , 2017 .
[49] Grzegorz Ronowski,et al. Road texture influence on tyre rolling resistance , 2017 .
[50] E. D. de León Izeppi,et al. Enhancing Pavement Surface Macrotexture Characterization by Using the Effective Area for Water Evacuation , 2016 .
[51] Valeria Vignali,et al. The wear of Stone Mastic Asphalt due to slow speed high stress simulated laboratory trafficking , 2016 .
[52] Mohammad Hossein Rafiei,et al. A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units , 2016 .
[53] Mohammad Behroozi,et al. Simulation of tyre rolling resistance generated on uneven road , 2016 .
[54] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[55] Zoltan Rado,et al. Exploring the texture–friction relationship: from texture empirical decomposition to pavement friction , 2015 .
[56] A. Scarpas,et al. Development of a thermomechanical tyre–pavement interaction model , 2015 .
[57] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[58] De Chen,et al. Exploring the feasibility of evaluating asphalt pavement surface macro-texture using image-based texture analysis method , 2015 .
[59] Terhi K Pellinen,et al. Macro- and micro-texture evolution of road pavements and correlation with friction , 2015 .
[60] Kumar Anupam,et al. Study of Influence of Operating Parameters on Braking Friction and Rolling Resistance , 2015 .
[61] R Khedoe,et al. Application of fractal analysis for measuring the effects of rubber polishing on the friction of asphalt concrete mixtures , 2014 .
[62] Y. Miao,et al. Fractal and Multifractal Characteristics of 3D Asphalt Pavement Macrotexture , 2014 .
[63] Y. Liu,et al. Reduction of Tire-Pavement Noise by Porous Concrete Pavement , 2014 .
[64] Zoltan Rado,et al. An initial attempt to develop an empirical relation between texture and pavement friction using the HHT approach , 2014 .
[65] Kumar Anupam,et al. Safety Aspects of Wet Asphalt Pavement Surfaces through Field and Numerical Modeling Investigations , 2014 .
[66] Arash Rezaei,et al. Experimental-based model for predicting the skid resistance of asphalt pavements , 2013 .
[67] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[68] Nader Tabatabaee,et al. Characterization of Asphalt Pavement Surface Texture , 2012 .
[69] Mohamed Abdel-Aty,et al. Design and verification of a laser based device for pavement macrotexture measurement , 2011 .
[70] Mohamed Saleh,et al. Stereo-vision applications to reconstruct the 3D texture of pavement surface , 2011 .
[71] Stefano Manzoni,et al. Laser-triangulation device for in-line measurement of road texture at medium and high speed , 2010 .
[72] Tien Fang Fwa,et al. Modeling Skid Resistance of Commercial Trucks on Highways , 2010 .
[73] Julien Cesbron,et al. Experimental study of tyre/road contact forces in rolling conditions for noise prediction , 2009 .
[74] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[75] Ahmed Shalaby,et al. Mean Profile Depth of Pavement Surface Macrotexture Using Photometric Stereo Techniques , 2007 .
[76] B. Persson. Contact mechanics for randomly rough surfaces , 2006, cond-mat/0603807.
[77] Hideki Tachibana,et al. Definition of road roughness parameters for tire vibration noise control , 2005 .
[78] H Germany,et al. Rubber friction on wet and dry road surfaces: The sealing effect , 2005, cond-mat/0502495.
[79] G H Tsohos,et al. Consideration of Fractals Potential in Pavement Skid Resistance Evaluation , 2002 .
[80] O. Panagouli,et al. Fractal Evaluation of Pavement Skid Resistance Variations. I: Surface Wetting , 1998 .