Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea...Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.展开更多
Future-oriented Science&Technology(S&T)Strategies trigger the innovative developments of advanced materials,providing an envision to the significant progress of leading-/cutting-edge science,engineering,and te...Future-oriented Science&Technology(S&T)Strategies trigger the innovative developments of advanced materials,providing an envision to the significant progress of leading-/cutting-edge science,engineering,and technologies for the next few decades.Motivated by Made in China 2025 and New Material Power Strategy by 2035,several key viewpoints about automated research workflows for accelerated discovery and smart manufacturing of advanced materials in terms of AI for Science and main respective of big data,database,standards,and ecosys-tems are discussed.Referring to classical toolkits at various spatial and temporal scales,AI-based toolkits and AI-enabled computations for material design are compared,highlighting the dominant role of the AI agent paradigm.Our recent developed ProME platform together with its functions is introduced briefly.A case study of AI agent assistant welding is presented,which is consisted of the large language model,auto-coding via AI agent,image processing,image mosaic,and machine learning for welding defect detection.Finally,more duties are called to educate the next generation workforce with creative minds and skills.It is believed that the transformation of knowledge-enabled data-driven integrated computational material engineering era to AI^(+) era promotes the transformation of smart design and manufacturing paradigm from“designing the materials”to“designing with materials.”展开更多
文摘Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.
基金funded by the National Basic Scientific Research Project of China(No.JCKY2020607B003)the Joint Strategy Research&Consulting Project supported by the Chinese Academy of Engineering and National Natural Science Foundation of China(No.2022-ZCQ-03).
文摘Future-oriented Science&Technology(S&T)Strategies trigger the innovative developments of advanced materials,providing an envision to the significant progress of leading-/cutting-edge science,engineering,and technologies for the next few decades.Motivated by Made in China 2025 and New Material Power Strategy by 2035,several key viewpoints about automated research workflows for accelerated discovery and smart manufacturing of advanced materials in terms of AI for Science and main respective of big data,database,standards,and ecosys-tems are discussed.Referring to classical toolkits at various spatial and temporal scales,AI-based toolkits and AI-enabled computations for material design are compared,highlighting the dominant role of the AI agent paradigm.Our recent developed ProME platform together with its functions is introduced briefly.A case study of AI agent assistant welding is presented,which is consisted of the large language model,auto-coding via AI agent,image processing,image mosaic,and machine learning for welding defect detection.Finally,more duties are called to educate the next generation workforce with creative minds and skills.It is believed that the transformation of knowledge-enabled data-driven integrated computational material engineering era to AI^(+) era promotes the transformation of smart design and manufacturing paradigm from“designing the materials”to“designing with materials.”
