Pin Zhang 
- (2024): Seg2Tunnel: A hierarchical point cloud dataset and benchmarks for segmentation of segmental tunnel linings. In: Tunnelling and Underground Space Technology, v. 147 (Mai 2024).
- (2022): Success and challenges in predicting TBM penetration rate using recurrent neural networks. In: Tunnelling and Underground Space Technology, v. 130 (Dezember 2022).
- (2022): Physics-Informed Multifidelity Residual Neural Networks for Hydromechanical Modeling of Granular Soils and Foundation Considering Internal Erosion. In: Journal of Engineering Mechanics (ASCE), v. 148, n. 4 (April 2022).
- (2021): CNN-Based Intelligent Method for Identifying GSD of Granular Soils. In: International Journal of Geomechanics, v. 21, n. 12 (Dezember 2021).
- (2020): Analytical and Semi-Analytical Solutions for Describing Tunneling-Induced Transverse and Longitudinal Settlement Troughs. In: International Journal of Geomechanics, v. 20, n. 8 (August 2020).
- (2021): BiLSTM-Based Soil–Structure Interface Modeling. In: International Journal of Geomechanics, v. 21, n. 7 (Juli 2021).
- (2021): An AIoT-based system for real-time monitoring of tunnel construction. In: Tunnelling and Underground Space Technology, v. 109 (März 2021).
- (2019): Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods. In: Soils and Foundations, v. 59, n. 2 (April 2019).
- (2020): A critical evaluation of machine learning and deep learning in shield-ground interaction prediction. In: Tunnelling and Underground Space Technology, v. 106 (Dezember 2020).
- (2020): Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study. In: Tunnelling and Underground Space Technology, v. 99 (Mai 2020).
- (2020): Ground settlement induced by tunneling crossing interface of water-bearing mixed ground: A lesson from Changsha, China. In: Tunnelling and Underground Space Technology, v. 96 (Februar 2020).
- (2019): Prediction of shield tunneling-induced ground settlement using machine learning techniques. In: Frontiers of Structural and Civil Engineering, v. 13, n. 6 (Oktober 2019).
- (2019): Real-time analysis and regulation of EPB shield steering using Random Forest. In: Automation in Construction, v. 106 (Oktober 2019).
- (2018): Deformation and stress characteristics of existing twin tunnels induced by close-distance EPBS under-crossing. In: Tunnelling and Underground Space Technology, v. 82 (Dezember 2018).
Structurae kooperiert mit
