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1.
Influence of measurement errors on structural damage identification using artificial neural networks
The effect of measurement errors on structural damage identification using artificial neural networks (ANN) was investigated
in this study. By using back-propagation (BP) networks with proper input vectors, numerical simulation tests for damage detection
on a six-storey frame were conducted with measurement errors in deterministic as well as probabilistic senses. The identifiability
using ANN for damage location and extent was studied for the cases of measurement errors with different degrees. The results
showed that there exists a critical level of measurement error beyond which the probability of correct identification is sharply
decreased. The identifiability using the neural networks in the presence of modeling and measurement errors is finally verified
using experimental data on a two-storey steel frame.
Project supported by Hong Kong Polytechnic University. 相似文献
2.
INTRODUCTION Damage detection of structures is very importantfor ensuring and evaluating the safety of structuresystems during their lifetime. The approaches de-veloped in this field may be generally classified intodynamic identification approach using dynamic testdata and static identification approach using static testdata (Wang et al., 2001). The dynamic identificationapproach has been highly developed, although severalinherent drawbacks and problems handicap the fullutilization o… 相似文献
3.
ＩＮＴＲＯＤＵＣＴＩＯＮＩｎｅｘｃａｖａｔｉｏｎ ,ｎｏｒｍａｌａｎａｌｙｓｉｓｉｓｎｏｔｇｏｏｄｅｎｏｕｇｈｔｏｍｅｅｔｅｎｇｉｎｅｅｒｉｎｇｎｅｅｄｓｄｕｅｔｏｔｈｅｕｎ ｃｅｒｔａｉｎｔｙｏｆｆｏｒｃｅｓａｐｐｌｉｅｄｏｎｂｒａｃｅｓｔｒｕｃｔｕｒｅｓ,ｓｏｉｌｃｈａｒａｃｔｅｒｉｓｔｉｃｓ,ａｎｄｓｏｉｌｍｏｄｅｌｕｓｅｄ .Ｔｏｇｕａｒａｎｔｅｅｔｈａｔｔｈｅｃｏｎｓｔｒｕｃｔｉｏｎｐｒｏｃｅｓｓｃａｎｂｅｓｍｏｏｔｈｌｙｐｅｒｆｏｒｍｅｄ ,ｍｅａｓｕｒｅｍｅｎｔｓｉｎｓｉｔｕａｒｅｕｓｕａ… 相似文献
4.
提出了基于人工神经网络（ＡｒｔｉｆｉｃｉａｌＮｅｕｒａｌＮｅｔｗｏｒｋｓ）对动力结构进行系统辨识的方法，即应用人工神经网络预测结构地震响应．采用ＢＰ算法的前馈网络（简称ＢＰ网络）对剪切模型结构进行系统辨识．首先用实际地震波及相应的模拟地震响应训练本文提出的ＢＰ网络，然后用“已学会”的ＢＰ网络预测其它地震波激励下的结构地震响应．还讨论了网络拓扑结构、输入单元数等对网络学习和预测的影响．通过本文可以发现，合适的人工神经网络结构能准确地辨识结构动力特性和预测结构动力响应 相似文献
5.
针对结构物在地震作用下的灾害评估问题,提出了一种基于刚度退化概念的框架结构整体与层间损伤指标.该指标通过结构静力弹塑性分析方法进行计算,利用塑性铰考虑结构的地震损伤.同时,根据能力谱方法建立了该损伤指标与抗震设防等级的关系.然后,将建议指标应用于2个3层钢筋混凝土框架结构,并与其他损伤指标进行了对比.结果表明:建议的损伤指标偏于安全,且对静力弹塑性分析的水平荷载模式不敏感;层间损伤指标能够清晰地反映各楼层的损伤情况,从而判断薄弱层的位置.最后,通过统计分析给出了结构不同性能水准与损伤指标的对应关系,为基于性能的框架结构抗震评估提供参考. 相似文献
6.
讨论了基于BackStepping方法,载体位置与姿态均不受控制的双臂空间机器人跟踪惯性空间期望轨迹的控制问题．首先基于拉格朗日第二类方法,结合系统动量（动量矩）守恒关系,推导得到了系统动力学方程,并转化为系统状态方程．基于Backstepping方法,针对具有不确定性的双臂空间机器人系统,设计了鲁棒自适应神经网络控制规律,保证了具有不确定性的双臂空间机器人系统末端手爪在惯性空间跟踪期望轨迹的控制．仿真实验证明了该方法的有效性． 相似文献
7.
Thomas F. Rienzo Kuriakose K. Athappilly 《Decision Sciences Journal of Innovative Education》2012,10(4):515-520
Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of the following ANN parameters: weights, learning rates, threshold functions, and transformation functions. The spreadsheet ANN model project is given early in the semester, just after ANN is introduced. Students can see effects of ANN parameters as they make changes to spreadsheet model inputs, greatly enhancing discussion of ANN processes. After working with the spreadsheet model, students have expressed an appreciation for decisions based on patterns of historic data, and they like the ability to peek “behind the curtain” at processes of predictive software packages. 相似文献
8.
针对服役结构状态评估问题提出了基于虚功误差估计算子的统计分析方法.首先定义虚功误差来表达实际结构与参数化分析模型之间的差别,然后采用改进的牛顿算法推导分析模型结构识别算法.为了探讨在有测量误差的情况下算法的性能,引用Monte Carlo方法模拟测量数据,对测量数据的误差与识别结果的影响进行了详细的分析比较.根据识别结果确定它的概率分布,通过假设试验对服役结构状态评估进行统计分析.最后应用双跨五层刚架结构进行了大量数值模拟,计算结果显示了所提方法的有效性. 相似文献
9.
1 Introduction Direct methanol fuel cell ( DMFC) is desirable toserve as the power systemfor portable devices such ascellular phones , portable computers ,etc. due to thetheoretically high energy density and the liquid fuelused that can be stored and tran… 相似文献
10.
Too many sensors and data information in structural health monitoring system raise the problem of how to realize multi-sensor information fusion. An experiment on a three-story frame structure was conducted to obtain vibration test data in 36 damage cases. A coupling neural network (NN) based on multi-sensor information fusion is proposed to achieve identification of damage occurrence, damage localization and damage quantification, respectively. First, wavelet packet transform (WPT) is used to extract features of vibration test data from structure with different damage extent. Then, data fusion is conducted by assembling feature vectors of different type sensors. Finally, three sets of coupling NN are constructed to implement decision fusion and damage identification. The results of experimental study proved the validity and feasibility of the proposed methodology. 相似文献
11.
Xu Ri-qing 《浙江大学学报(A卷英文版)》2000,1(4):408-413
This paper presents a new method (GA-ANN) developed by combining genetic algorithm (GA) and artificial neural networks (ANN)
for determining parameters of soils and retaining walls of deep excavation. This method has the advantages of nonlinear projection
of neural networks, networks reasoning, prediction and good overall characteristics. it was first used for back analysis of
the problem of mechanics parameters for excavation. Case studies showed that the GA-ANN method is effective and practical
for back analysis of determining parameters.
Project supported by NSFC(5973860) and National Civil Defence fund of China 相似文献
12.
Artificial neural networks (ANNs) have been widely used to solve a number of problems to which analytical solutions are difficult to obtain using traditional mathematical approaches. Such problems exist also in the analysis of industrial robots. This paper presents an overview of ANN applications to robot kinematics, dynamics, control, trajectory and path planning, and sensing. Reasons for using or not using ANNs to industrial robots are explained as well. 相似文献
13.
WU Xiao-juan ZHU Xin-jian CAO Guang-yi TU Heng-yong 《浙江大学学报(A卷英文版)》2007,8(9):1505-1509
The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model. 相似文献
14.
Kim-seng Chia Herlina Abdul Rahim Ruzairi Abdul Rahim 《Journal of Zhejiang University. Science. B》2012,13(2):145-151
Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate
the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper
is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR)
(a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400–1000 nm) spectra in the non-destructive soluble
solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data.
Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount
were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons
were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden
neurons outperforms that of PCR. 相似文献
15.
Calculation method of ship collision force on bridge using artificial neural network 总被引：1，自引：0，他引：1
Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method （FEM） which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network （ANN）. The radial basis function neural network （RBFNN） employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation effi- ciency in application using MATLAB software. 相似文献
16.
1 Ｉｎｔｒｏｄｕｃｔｉｏｎ Ｉｎｒｅｃｅｎｔｙｅａｒｓ,ｍａｎｙｎｅｗｐｏｗｅｒｓｗｉｔｃｈｉｎｇｓｅｍｉｃｏｎｄｕｃｔｏｒｓｈａｖｅｅｍｅｒｇｅｄｄｕｅｔｏｔｈｅｒａｐｉｄｄｅｖｅｌｏｐｍｅｎｔｏｆｐｏｗｅｒｅｌｅｃｔｒｏｎｉｃｓ.Ｉｔｉｓｎｏｔｓｔｒａｎｇｅｔｈａｔｔｈｅｍｏｄｅｌｏｆａｎｅｗｌｙｄｅｖｅｌｏｐｅｄｄｅｖｉｃｅｃａｎｎｏｔｂｅｆｏｕｎｄｉｎｔｈｅｌｉｂｒａｒｙｏｆａｃｉｒｃｕｉｔｓｉｍｕｌａｔｏｒ.Ｔｈｅｕｓｅｒｓｈａｖｅｔｏｅｓｔａｂｌｉｓｈｔｈｅｓｉｍｕｌａｔｉｏｎｍｏｄｅ… 相似文献
17.
Artificial neural network modeling of water quality of the Yangtze River system: a case study in reaches crossing the city of Chongqing 总被引：2，自引：0，他引：2
An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network （ANN） models, a feed-forward back-propagation （BP） model and a radial basis function （RBF） model, to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing, P. R. China. Our models used the historical monitoring data of biological oxygen demand, dissolved oxygen, ammonia, oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models, the RBF model calculates with a smaller mean error, but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models. 相似文献
18.
用人工神经网络预测时用水量的方法 总被引：4，自引：0，他引：4
根据城市时段用水量序列季节性、趋势性及随机扰动性的特点 ,利用人工神经网络方法 ,建立了时间水量短期预报模型 .选取不同的隐层结点数 ,采用相同的输入样本及预测数据进行训练和预测 ,并通过比较其相对误差的大小 ,确定了神经网络的结构 ,并应用 Matlab语言进行了具体的建模和预报 .实例考核证明 ,该方法与常用的时间序列三角函数分析法相比 ,具有预测误差小、计算速度快等特点 ,可满足供水系统调度运行的实际需要 相似文献
19.
本文提出了一种基于模糊方向线索特征 (fuzzydirectionallineelementfeature,FDLEF)与人工神经网络 (artificialneuralnetworks,ANN)相结合的手写体汉字识别方法 (FDLEF -ANN) ,解决了单一FDLEF方法对相似字识别率低的问题 .这种方法分两级识别 ,先由FDLEF识别模块进行识别 ,将识别结果送至选择器 ,若识别结果不属于预定义的相似字集合簇 ,则该结果即为最终识别结果 ,否则 ,将其送至人工神经网络识别模块进行相似字的识别 .本方法既保留了原FDLEF方法的优点又提高了对相似字的识别率 ,FDLEF -ANN系统对相似字的识别率由 78 0 9%提高到 82 97% . 相似文献
20.
Arturo Garcia-Perez Juan P. Amezquita-Sanchez Aurelio Dominguez-Gonzalez Ramin Sedaghati Roque Osornio-Rios Rene J. Romero-Troncoso 《浙江大学学报(A卷英文版)》2013,14(9):615-630
Structural health monitoring （SHM） is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on struc- tures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decom- position （EMD） method are combined with artificial neural networks （ANNs） for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures. 相似文献