Shaojun Zhu, Doctor of Engineering, Shanghai High-Level Scholar, Shanghai Pujiang Scholar, Assistant Professor and Distinguished Research Fellow of Department of Structural Engineering, College of Civil Engineering, Tongji University. He also serves as a council member of the Engineering Fire Protection Technology Branch of the China Society of Civil Engineering and a member of the Structural Fire Resistance Committee of the Earthquake Resistance and Disaster Prevention Branch of the Architectural Society of China. He majors in the research on smart firefighting and smart structural design. Specific research topics include real-time early warning of fire-induced collapse of large steel structures, shape and topology optimization of skeleton structures, and reinforcement learning-driven automatic design of structures. He hosted 1 project sponsored by the National Science Foundation of China, 1 project sponsored by the Shanghai Pujiang Program, and 2 secondary projects sponsored by the National Key Research and Development Program of China, participated in the revision of 1 standard, published 68 journal papers indexed by SCI, published 16 Chinese journal papers indexed by EI, and got 3 patents granted. During his Ph.D. period, he was awarded by the National Scholarship of China 3 times, and he visited Kyoto University as a guest research associate for 2 years funded by the Chinese Scholarship Council (supervised by Prof. Makoto Ohsaki). The achievements won the First Prize for Scientific and Technological Progress from the China Steel Structure Society and the First Prize for Science and Technology from the China Railway Society. His E-mail is: zhushaojun@tongji.edu.cn
Last update of this page: February 14th, 2026
Based on virtual interaction forces, the structural topology constraint is considered, and the constrained stochastic imperfection modal method is proposed for single-layer reticulated shells with controllable computational cost and reasonable accuracy.
Through parametric analysis, all collapse modes and mechanisms of planar steel truss structures under arbitrary conditions are summarized, and an early-warning method for fire-induced collapse is proposed based on the evolution laws of key physical parameters.
Using deep graph neural network-based reinforcement learning to quantitatively evaluate critical elements for progressive collapse resistance of frame structures.
Autonomous topology generation for 2D trusses using reinforcement learning and graph embedding, followed by topology optimization based on ground structures.
A quantitative metric for evaluating assembly convenience of aluminum alloy free-form reticulated shells is proposed, along with a form-finding method considering joint rigidity.
Combining supervised learning with the improved consistent imperfection method to predict the stability capacity of imperfect spatial grid structures.
A shape optimization method using genetic algorithms that significantly improves the nonlinear buckling capacity without affecting macro-geometric features.
Note: * denotes corresponding author