Ju Huyan 
- (2024): Encoder–decoder with pyramid region attention for pixel‐level pavement crack recognition. In: Computer-Aided Civil and Infrastructure Engineering, v. 39, n. 10 (Januar 2024).
- (2023): An ensemble tree-based prediction of Marshall mix design parameters and resilient modulus in stabilized base materials. In: Construction and Building Materials, v. 401 (Oktober 2023).
- (2023): Evidential transformer for pavement distress segmentation. In: Computer-Aided Civil and Infrastructure Engineering, v. 38, n. 16 (3 Oktober 2023).
- (2023): Feature representation improved Faster R-CNN model for high efficiency pavement crack detection. In: Canadian Journal of Civil Engineering / Revue canadienne de génie civil, v. 50, n. 2 (Februar 2023).
- (2022): Novel Computer Tomography image enhancement deep neural networks for asphalt mixtures. In: Construction and Building Materials, v. 352 (Oktober 2022).
- (2022): Multi-scale feature fusion network for pixel-level pavement distress detection. In: Automation in Construction, v. 141 (September 2022).
- (2022): Pixelwise asphalt concrete pavement crack detection via deep learning‐based semantic segmentation method. In: Structural Control and Health Monitoring, v. 29, n. 8 (3 Juli 2022).
- (2021): New innovations in pavement materials and engineering: A review on pavement engineering research 2021. In: Journal of Traffic and Transportation Engineering (English Edition), v. 8, n. 6 (Dezember 2021).
- (2021): Predicting Marshall parameters of flexible pavement using support vector machine and genetic programming. In: Construction and Building Materials, v. 306 (November 2021).
- (2021): Assessment of importance-based machine learning feature selection methods for aggregate size distribution measurement in a 3D binocular vision system. In: Construction and Building Materials, v. 306 (November 2021).
- (2021): Crack Grid Detection and Calculation Based on Convolutional Neural Network. In: Canadian Journal of Civil Engineering / Revue canadienne de génie civil, v. 48, n. 9 (September 2021).
- (2022): Asphalt Pavement Friction Coefficient Prediction Method Based on Genetic-Algorithm-Improved Neural Network(GAI-NN) Model. In: Canadian Journal of Civil Engineering / Revue canadienne de génie civil, v. 49, n. 1 (Januar 2022).
- (2020): CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection. In: Structural Control and Health Monitoring, v. 27, n. 8 (7 Juli 2020).
- (2020): Image-Based Coarse-Aggregate Angularity Analysis and Evaluation. In: Journal of Materials in Civil Engineering (ASCE), v. 32, n. 6 (Juni 2020).
- (2020): Illumination Compensation Model with k-Means Algorithm for Detection of Pavement Surface Cracks with Shadow. In: Journal of Computing in Civil Engineering, v. 34, n. 1 (Januar 2020).
- (2019): Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network. In: Automation in Construction, v. 107 (November 2019).
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