许斌
大型复杂结构系统的监测与控制、新型传感技术;非线性动力结构系统识别;混凝土结构的动力本构;人工智能和智能计算。
个性化签名
- 姓名:许斌
- 目前身份:
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学术头衔:
博士生导师, 教育部“新世纪优秀人才支持计划”入选者
- 职称:-
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学科领域:
土木建筑结构
- 研究兴趣:大型复杂结构系统的监测与控制、新型传感技术;非线性动力结构系统识别;混凝土结构的动力本构;人工智能和智能计算。
许斌,湖南省芙蓉学者特聘教授、博士生导师。
主要学习经历:1988年9月-1992年6月,华中理工大学土木工程系工业与民用专业本科生,获工学学士学位;1992年9月-1995年6月,华中理工大学土木工程学院结构工程专业硕士生,获工学硕士学位;1998年1月-2001年3月,受日本文部省国费奖学金资助到日本国立茨城大学都市系统工程系攻读博士学位,获得工学博士学位。
主要工作经历:2001年4月-2003年3月,受日本学术振兴会(JSPS)科研奖励费资助,作为JSPS特别研究员(博士后)在日本茨城大学从事研究工作;2003年4月-2003年10月,作为日本文部科学省研究员,在日本茨城大学参加日本文部科学省重大项目研究工作;2003年11月-2005年6月,在美国密苏里大学从事研究工作;2005年起受聘为湖南省“芙蓉学者”,任湖南大学土木工程学院教授;2006年6月被批准为博士生导师;2007年受邀以Gledden高级访问学者身份到澳大利亚西澳大学开展合作研究;2008年受邀以访问教授身份到美国休斯敦大学开展合作研究。
主要研究方向:大型复杂结构系统的监测与控制、新型传感技术;非线性动力结构系统识别;混凝土结构的动力本构;人工智能和智能计算。
主持国家自然科学基金委员会资助面上项目一项、作为合作单位负责人参与国家自然科学基金委员会“重大工程的动力灾变”重大研究计划重点项目一项(排名第二)。主持教育部科学技术研究重点项目一项、霍英东教育基金会青年教师基金项目一项。主持湖南省自然科学基金委员会资助杰出青年基金项目一项、面上项目一项。入选湖南省芙蓉学者奖励计划、教育部新世纪优秀人才计划。
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成果数
3
【期刊论文】Direct identification of structural parameters from dynamic responses with neural networks
许斌, Bin Xu a, *, , Zhishen Wu b, Genda Chen a, Koichi Yokoyama b
Engineering Applications of Artificial Intelligence 17 (2004) 931-943,-0001,():
-1年11月30日
A novel neural network-based strategyis proposed and developed for the direct identification of structural parameters (stiffness and damping coefficients) from the time-domain dynamic responses of an object structure without any eigenvalue analysis and extraction and optimization process that is required in manyidentification algorithms for inverse problems. Two back-propagation neural networks are constructed to facilitate the process of parameter identifications. The first one, called mulator neural network, is to model the behavior of a reference structure that has the same overall dimension and topologyas the object structure to be identified. After having been properly trained with the dynamic responses of the reference structure under a given dynamic excitation, the emulator neural network can be used as a nonparametric model of the reference structure to forecast its dynamic response with sufficient accuracy. However, when the parameters of the reference structure are modified to form a so-called associated structure, the dynamic responses forecast by the network will differ from the simulated responses of the associated structure. Their difference can be assessed with a proposed root mean square (RMS) difference vector for both velocityand displacement responses. With the associated structural parameters and their corresponding RMS difference vectors, another network, called parametric evaluation neural network, can be trained. In this study, several 5-story frames are considered as example object structures with simulated displacement and velocitytime histories that mimic the measured dynamic responses in practice. The performance of the proposed strategyhas been demonstrated quite satisfactorily; the error for the estimation of each stiffness or damping coefficient is less than 10% even in the presence of 7% noise. Numerical simulations show that the accuracyof the identified parameters can be significantlyimproved byinjecting noise in the training patterns for the parametric evaluation neural network. The proposed strategyis extremelyefficient in computation and thus has potential of becoming a practical tool for near real time monitoring of civil infrastructures. r 2004 Elsevier Ltd. All rights reserved.
Neural networks, Identification, Damage detection, Dynamic responses, Parameters, Time domain, Root mean square difference vector
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【期刊论文】Identification of localized frame parameters using higher natural modes
许斌, Wei-Jian Yi a, , Yun Zhou a, * S. Kunnath b, a, Bin Xu a
Engineering Structures 30 (2008) 3082-3094,-0001,():
-1年11月30日
The use of sensitive higher modes in physical structural parameter identification of local members in a frame system is discussed in this paper. Analytical studies are supported by low-level vibration tests on a third-scale four-story reinforced concrete frame structure embedded in soil to represent a realistic foundation system. Preliminary numerical modal analysis are carried out to establish the sensitivity of higher modes to localized damage in the frame. Damage to one of the elements on the first story of the frame was simulated by adding a mass to the column and examining the vibration modes before and after the addition of the mass. Through forced vibration tests and selective placement of strain transducers, the Poly-reference least-squares complex frequency domain method (PolyMAX) is used to determine the existence of ‘highly sensitive higher modes’ (HSHMs). An evaluation of the identified higher modes further enables the identification of physical parameters of the target column element using a simple minimization scheme. Findings from the present study indicate that physical parameters of a local element in a frame structure can be identified effectively using HSHMs and that the higher modes of vibration are more sensitive to changes in local physical parameters than lower global modes. The identification of physical parameters as outlined in this study can be applied in structural damage detection.
Higher modes, Highly sensitive higher modes, Poly MAX method, Damage diagnosis, Structural parameter identification
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许斌, *许斌, , 卢平, 宋钢兵
工程力学,2009,26(9):87~93,-0001,():
-1年11月30日
提出了一种直接运用加速度响应测量的结构参数识别方法。该方法通过两个神经网络对结构刚度和阻尼比进行直接识别。通过结构运动微分方程的离散解,阐明了该方法的理论基础以及构建两个神经网络的依据。首先,依据对目标结构参数的初步估计,假定一个参考结构,构建一个神经网络来描述该参考结构的加速度响应时间序列之间的映射关系,即建立该参考结构的非参数模型。然后,定义加速度响应预测差值均方根向量作为结构参数识别指标,并构建一个参数识别用神经网络来描述该指标与结构参数之间的关系。最后,基于一个框架结构模型振动台试验的加速度响应时间序列实测值,运用以上两个神经网络,在假定结构质量已知的情况下,对该框架模型的刚度和阻尼比进行了识别,并与扫频试验的结果进行了比较。结果表明该方法的结构参数识别结果可靠,所提出的准实时结构参数直接识别方法可行。
结构参数, 识别, 加速度, 神经网络, 振动台实验, 刚度, 阻尼比, 时程
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