In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element m...In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element model.In the new method,the finite element model was replaced by the multi-output support vector regression machine(MSVR).The interval variables of the measured frequency were sampled by Latin hypercube sampling method.The samples of frequency were regarded as the inputs of the trained MSVR.The outputs of MSVR were the target values of design parameters.The steel structure of National Aquatic Center for Beijing Olympic Games was introduced as a case for finite element model updating.The results show that the proposed method can avoid solving the problem of complicated calculation.Both the estimated values and associated uncertainties of the structure parameters can be obtained by the method.The static and dynamic characteristics of the updated finite element model are in good agreement with the measured data.展开更多
In the steelmaking industry,enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities.Thus,effectively integrating various prod...In the steelmaking industry,enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities.Thus,effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain,reducing management costs and complexities.This paper proposes,for the first time,the integration of Vision-Language Model(VLM)and Large Language Model(LLM)technologies in the steel manufacturing domain,creating a novel steelmaking process management system.The system facilitates data collection,analysis,visualization,and intelligent dialogue for the steelmaking process.The VLM module provides textual descriptions for slab defect detection,while LLM technology supports the analysis of production data and intelligent question-answering.The feasibility,superiority,and effectiveness of the system are demonstrated through production data and comparative experiments.The system has significantly lowered costs and enhanced operational understanding,marking a critical step toward intelligent and cost-effective management in the steelmaking domain.展开更多
基金Project(50678052) supported by the National Natural Science Foundation of China
文摘In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element model.In the new method,the finite element model was replaced by the multi-output support vector regression machine(MSVR).The interval variables of the measured frequency were sampled by Latin hypercube sampling method.The samples of frequency were regarded as the inputs of the trained MSVR.The outputs of MSVR were the target values of design parameters.The steel structure of National Aquatic Center for Beijing Olympic Games was introduced as a case for finite element model updating.The results show that the proposed method can avoid solving the problem of complicated calculation.Both the estimated values and associated uncertainties of the structure parameters can be obtained by the method.The static and dynamic characteristics of the updated finite element model are in good agreement with the measured data.
文摘In the steelmaking industry,enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities.Thus,effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain,reducing management costs and complexities.This paper proposes,for the first time,the integration of Vision-Language Model(VLM)and Large Language Model(LLM)technologies in the steel manufacturing domain,creating a novel steelmaking process management system.The system facilitates data collection,analysis,visualization,and intelligent dialogue for the steelmaking process.The VLM module provides textual descriptions for slab defect detection,while LLM technology supports the analysis of production data and intelligent question-answering.The feasibility,superiority,and effectiveness of the system are demonstrated through production data and comparative experiments.The system has significantly lowered costs and enhanced operational understanding,marking a critical step toward intelligent and cost-effective management in the steelmaking domain.
