Yongjia Xu 
- (2024): Drone Photogrammetry-based Wind Field Simulation for Climate Adaptation in Urban Environments. In: Sustainable Cities and Society, v. 117 (Dezember 2024).
- (2024): An asymmetric pinching damaged hysteresis model for glubam members: Parameter identification and model comparison. In: Computers & Structures, v. 305 (Dezember 2024).
- (2024): Glubam roof truss with riveted glubam connections adopting thin-walled steel tube: Experiment, modeling, and model-updating. In: Journal of Building Engineering, v. 96 (November 2024).
- (2024): Cyclic behavior of laminated bio-based connections with slotted-in steel plates: Genetic algorithm, deterministic neural network-based model parameter identification, and uncertainty quantification. In: Engineering Structures, v. 310 (Juli 2024).
- (2024): Bidirectional graphics-based digital twin framework for quantifying seismic damage of structures using deep learning networks. In: Structural Health Monitoring, v. 24, n. 1 (Februar 2024).
- (2023): Bio‐based laminated truss structures with bolted steel connections: Experiment, modeling, and model‐updating. In: Earthquake Engineering and Structural Dynamics, v. 53, n. 2 (November 2023).
- (2023): Hysteretic behavior simulation based on pyramid neural network: Principle, network architecture, case study and explanation. In: Advances in Structural Engineering, v. 26, n. 13 (Juli 2023).
- (2023): A deep ensemble learning-driven method for the intelligent construction of structural hysteresis models. In: Computers & Structures, v. 286 (Oktober 2023).
- (2022): Advanced corrective training strategy for surrogating complex hysteretic behavior. In: Structures, v. 41 (Juli 2022).
- (2021): Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning. Vorgetragen bei: IABSE Congress: Resilient technologies for sustainable infrastructure, Christchurch, New Zealand, 3-5 February 2021.
- (2021): A deep learning approach to rapid regional post‐event seismic damage assessment using time‐frequency distributions of ground motions. In: Earthquake Engineering and Structural Dynamics, v. 50, n. 6 (Mai 2021).
- (2021): Real‐time regional seismic damage assessment framework based on long short‐term memory neural network. In: Computer-Aided Civil and Infrastructure Engineering, v. 36, n. 4 (Februar 2021).
- (2021): Real-Time Seismic Damage Prediction and Comparison of Various Ground Motion Intensity Measures Based on Machine Learning. In: Journal of Earthquake Engineering, v. 26, n. 8 (März 2021).
- (2019): A prediction method of building seismic loss based on BIM and FEMA P-58. In: Automation in Construction, v. 102 (Juni 2019).
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