Ankang Ji 
- (2025): Unsupervised seepage segmentation pipeline based on point cloud projection with large vision model. In: Tunnelling and Underground Space Technology, v. 158 (April 2025).
- (2025): Vision-Based Body Pose Estimation of Excavator Using a Transformer-Based Deep-Learning Model. In: Journal of Computing in Civil Engineering, v. 39, n. 2 (März 2025).
- (2024): Dual hierarchical attention-enhanced transfer learning for semantic segmentation of point clouds in building scene understanding. In: Automation in Construction, v. 168 (Dezember 2024).
- (2024): Real-time prediction of TBM penetration rates using a transformer-based ensemble deep learning model. In: Automation in Construction, v. 168 (Dezember 2024).
- : Schedule-cost optimization in high-rise buildings considering uncertainty. In: Engineering, Construction and Architectural Management.
- (2024): Integrating Evolutionary Game and System Dynamics for Multi-player Safety Regulation of Major Infrastructure Projects in China. In: Journal of Civil Engineering and Management, v. 30, n. 4 (15 April 2024).
- : A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification. In: Engineering, Construction and Architectural Management.
- (2023): Dual attention-based deep learning network for multi-class object semantic segmentation of tunnel point clouds. In: Automation in Construction, v. 156 (Dezember 2023).
- (2023): Scientometric Analysis of Pavement Maintenance: a Twenty-year Review. In: Journal of Civil Engineering and Management, v. 29, n. 5 (5 April 2023).
- (2023): Deep learning for large-scale point cloud segmentation in tunnels considering causal inference. In: Automation in Construction, v. 152 (August 2023).
- (2023): Attention-enhanced sampling point cloud network (ASPCNet) for efficient 3D tunnel semantic segmentation. In: Automation in Construction, v. 146 (Februar 2023).
- (2023): Semi-supervised learning-based point cloud network for segmentation of 3D tunnel scenes. In: Automation in Construction, v. 146 (Februar 2023).
- (2022): UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels. In: Automation in Construction, v. 142 (Oktober 2022).
- (2022): Sewer defect detection from 3D point clouds using a transformer-based deep learning model. In: Automation in Construction, v. 136 (April 2022).
- (2022): An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds. In: Automation in Construction, v. 137 (Mai 2022).
- (2021): Development strategy for prefabricated construction projects: a tripartite evolutionary game based on prospect theory. In: Engineering, Construction and Architectural Management, v. 30, n. 1 (August 2021).
- (2022): Large-scale 3D point-cloud semantic segmentation of urban and rural scenes using data volume decomposition coupled with pipeline parallelism. In: Automation in Construction, v. 133 (Januar 2022).
- (2021): Game theory–based bilevel model for multiplayer pavement maintenance management. In: Automation in Construction, v. 129 (September 2021).
- (2021): Image‐based road crack risk‐informed assessment using a convolutional neural network and an unmanned aerial vehicle. In: Structural Control and Health Monitoring, v. 28, n. 7 (2 Juni 2021).
- (2020): An Integrated Multi-Objectives Optimization Approach on Modelling Pavement Maintenance Strategies for Pavement Sustainability. In: Journal of Civil Engineering and Management, v. 26, n. 8 (5 November 2020).
- (2020): An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement. In: Automation in Construction, v. 114 (Juni 2020).
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