Ju Huyan 
- : Encoder–decoder with pyramid region attention for pixel‐level pavement crack recognition. Dans: Computer-Aided Civil and Infrastructure Engineering.
- (2023): An ensemble tree-based prediction of Marshall mix design parameters and resilient modulus in stabilized base materials. Dans: Construction and Building Materials, v. 401 (octobre 2023).
- (2023): Evidential transformer for pavement distress segmentation. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 38, n. 16 (3 octobre 2023).
- (2023): Feature representation improved Faster R-CNN model for high efficiency pavement crack detection. Dans: Canadian Journal of Civil Engineering / Revue canadienne de génie civil, v. 50, n. 2 (février 2023).
- (2022): Novel Computer Tomography image enhancement deep neural networks for asphalt mixtures. Dans: Construction and Building Materials, v. 352 (octobre 2022).
- (2022): Multi-scale feature fusion network for pixel-level pavement distress detection. Dans: Automation in Construction, v. 141 (septembre 2022).
- (2022): Pixelwise asphalt concrete pavement crack detection via deep learning‐based semantic segmentation method. Dans: Structural Control and Health Monitoring, v. 29, n. 8 (3 juillet 2022).
- (2021): New innovations in pavement materials and engineering: A review on pavement engineering research 2021. Dans: Journal of Traffic and Transportation Engineering (English Edition), v. 8, n. 6 (décembre 2021).
- (2021): Predicting Marshall parameters of flexible pavement using support vector machine and genetic programming. Dans: Construction and Building Materials, v. 306 (novembre 2021).
- (2021): Assessment of importance-based machine learning feature selection methods for aggregate size distribution measurement in a 3D binocular vision system. Dans: Construction and Building Materials, v. 306 (novembre 2021).
- (2021): Crack Grid Detection and Calculation Based on Convolutional Neural Network. Dans: Canadian Journal of Civil Engineering / Revue canadienne de génie civil, v. 48, n. 9 (septembre 2021).
- (2022): Asphalt Pavement Friction Coefficient Prediction Method Based on Genetic-Algorithm-Improved Neural Network(GAI-NN) Model. Dans: Canadian Journal of Civil Engineering / Revue canadienne de génie civil, v. 49, n. 1 (janvier 2022).
- (2020): CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection. Dans: Structural Control and Health Monitoring, v. 27, n. 8 (7 juillet 2020).
- (2020): Image-Based Coarse-Aggregate Angularity Analysis and Evaluation. Dans: Journal of Materials in Civil Engineering (ASCE), v. 32, n. 6 (juin 2020).
- (2020): Illumination Compensation Model with k-Means Algorithm for Detection of Pavement Surface Cracks with Shadow. Dans: Journal of Computing in Civil Engineering, v. 34, n. 1 (janvier 2020).
- (2019): Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network. Dans: Automation in Construction, v. 107 (novembre 2019).
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