報(bào)告人: Nata?a Tri?ovi?, 教授，貝爾格萊德大學(xué)，塞爾維亞
報(bào)告人簡(jiǎn)介：塞爾維亞貝爾格萊德大學(xué)機(jī)械工程學(xué)院教授，主要從事理論與應(yīng)用力學(xué)、工程結(jié)構(gòu)動(dòng)力學(xué)與振動(dòng)、工程結(jié)構(gòu)可靠性分析等的研究，是CEEPUS (Central European Exchange Program for University Studies 中歐高校學(xué)生交流計(jì)劃) 貝爾格萊德大學(xué)分部的負(fù)責(zé)人。分別擔(dān)任2019-2024歐盟COST (European Cooperation in Science and Technology) CA18203計(jì)劃和2022-2026年COST CA21106計(jì)劃的管理委員會(huì)成員。是捷克工程大學(xué)力學(xué)工程學(xué)院客座教授（2009），波西尼亞巴尼亞盧卡大學(xué)力學(xué)工程學(xué)院客座教授（2010，2012），斯洛伐克科技大學(xué)力學(xué)工程學(xué)院客座教授，美國(guó)萊斯大學(xué)MEMS學(xué)院客座教授（2012-2014），EUREKA 計(jì)劃 和ESPRIT 項(xiàng)目成員，塞爾維亞結(jié)構(gòu)完整性與生命協(xié)會(huì)成員，塞爾維亞力學(xué)學(xué)會(huì)秘書長(zhǎng)在學(xué)術(shù)方面，發(fā)表學(xué)術(shù)論文60多篇，Google Scholar引用327次，H指數(shù)為11。國(guó)際化工作包括主持2013-2015年中國(guó)-塞爾維亞政府間科技合作項(xiàng)目一項(xiàng)；2015-2017年中國(guó)-塞爾維亞政府間例會(huì)項(xiàng)目一項(xiàng)；在中國(guó)西安電子科技大學(xué)大學(xué)雙創(chuàng)周活動(dòng)中主講三次全英文課程"Theory of Oscillations" 、"An Introduction to Structural Optimization and Reliability"、"Foundation of ordinary differential equation".
邀請(qǐng)人：李偉
報(bào)告地點(diǎn)：騰訊會(huì)議 #428-488-261
報(bào)告時(shí)間：28號(hào)從13點(diǎn)開始，每人兩小時(shí)
報(bào)告題目1：Effect of a Breathing Crack on the Random Vibrations of a Beam
摘要：This study investigates the influence of a breathing crack on the random vibrations of a beam, with special attention to geometrical non-linearities arising from the crack behavior. The system is modeled as bilinear due to the alternating opening and closing of the crack during vibration. Random excitations are considered to simulate realistic loading conditions, complementing common harmonic excitations typically observed in rotating machinery with unbalances. The results provide insights into the dynamic response of cracked structures under stochastic loading, which is crucial for structural health monitoring and early damage detection.
報(bào)告題目2：Towards Intelligent Reanalysis in Structural Dynamics
摘要：Predictive reanalysis enables efficient updating of structural responses after minor changes in geometry, material properties, or boundary conditions, without full re-computation. Initially based on finite element methods (FEM), these techniques now increasingly rely on Artificial Intelligence (AI), such as neural networks and ensemble models, to improve speed and adaptability in dynamic analysis. This presentation reviews key developments in predictive reanalysis with a focus on AI integration. A numerical study on a cantilever beam illustrates how changes in cross-section affect natural frequencies, comparing AI-based predictions with classical formulas. Results confirm the potential of AI models for fast, reliable assessment of structural modifications, supporting early-stage design and maintenance. Current challenges and future directions, including data quality, model interpretability, and real-time integration, are also discussed.
