DEVELOPMENT OF PROGRAM ON NONLINEAR STRUCFURAL ANALYSIS USING NEURAL NETWORK

神经网络非线性结构分析程序的开发

• 批准号: 13555134
• 负责人: MAZDA Taiji
• 金额: $ 8.19万
• 依托单位: Kyushu University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-13555134/
• 关键词: neural network; non-linear; hysteretic behavior; dynamic response analysis; high damping rubber bearing; 非線形履歴; 免震支承

Generally, in formulating a spring-mass model for hysteric behavior of materials and members with inelastic characteristic, a mathematical model based on load-deformation experimental results is considered. The model must approximate the inelastic hysteresis of the material. However, assumption of material's behavior using mathematical models is crucial, since it may cause serious errors if inappropriate model is applied for a particular situation. This research describes multiple layered neural network to simulate the non-linear hysteretic behavior like Ramberg-Osgood model, modified bilinear model and Takeda model. In this study, based on the pattern recognition ability of neural network, non-linear hysteretic behavior was modeled by the network directly without replacing it with a mathematical model. The effectiveness and applicability of the network in numerical analysis were evaluated. Generalized multiple layered neural network to evaluate non-linear hysteretic curve was constructed. The network can recognize well the three types of hysteretic curve. The network is available as a subroutine of non-linear spring in dynamic response analysis.

在建立材料和具有非弹性特性的构件滞回行为的弹簧-质量模型时，通常会考虑基于荷载-变形试验结果的数学模型。该模型必须近似于材料的非弹性滞后。然而，使用数学模型来假设材料的行为是至关重要的，因为如果将不适当的模型应用于特定情况，可能会导致严重的误差。本研究采用多层神经网络来模拟结构的非线性滞回行为，如Ramberg-Osgood模型、修正双线性模型和武田模型。本研究基于神经网络的模式识别能力，将非线性滞回行为直接用神经网络来建模，而不用数学模型来代替。评价了该网络在数值分析中的有效性和适用性。构建了用于评价非线性滞回曲线的广义多层神经网络。该网络能够很好地识别三种滞回曲线。该网络可作为非线性弹簧子程序用于动力响应分析。

项目成果

A Study on Generalized Neural Network System for Recognizing Nonlinear Behaviour of Structures

识别结构非线性行为的广义神经网络系统研究

• 发表时间: 2002
• 期刊: Proceedings of The Third International Conference on Engineering Computational Technology (CD-ROM)Book No.85
• 作者: T.Mazda;H.Otsuka;W.Yabuki;M.Tsuruta
• 通讯作者: M.Tsuruta

ニューラルネットワークによるPC上部構造非線形挙動の認識

利用神经网络识别PC上部结构的非线性行为

• 发表时间: 2001
• 期刊: 第11回プレストレストコンクリートの発展に関するシンポジウム
• 作者: 矢葺 亘;大塚久哲;松田泰治
• 通讯作者: 松田泰治

矢葺 亘, 大塚久哲, 松田泰治: "ニューラルネットワークによるPC上部構造非線形挙動の認識"第11回プレストレストコンクリートの発展に関するシンポジウム. 675-678 (2001)

Wataru Yabuki、Hisatoshi Otsuka、Yasuharu Matsuda：“使用神经网络识别 PC 上部结构的非线性行为”第 11 届预应力混凝土进展研讨会 675-678 (2001)。

Construction of Generalized Neural Network System for Recognizing Several Non-linear Behaviors

识别多种非线性行为的广义神经网络系统的构建

• 发表时间: 2003
• 期刊: ASME PVP SeismicEngineering-2003 Vol.466
• 作者: T.Mazda;H.Otsuka;W.Yabuki;K.Iwamoto
• 通讯作者: K.Iwamoto

T.Mazda, H.Otsuka, W.Yabuki, K.Iwamoto: "Construction of generalized neural network system for recognizing several non-linear behaviors"ASME PVP-Vol.466, SEISMIC ENGINEERING 2003. Vol.466. 143-151 (2003)

T.Mazda、H.Otsuka、W.Yabuki、K.Iwamoto：“用于识别多种非线性行为的广义神经网络系统的构建”ASME PVP-Vol.466，SEISMIC ENGINEERING 2003。Vol.466。