- 摘 要
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(1 北京工業(yè)大學空間結(jié)構(gòu)研究中心，北京 100124；2 北京工業(yè)大學城市與工程安全減災省部共建教育部重點實驗室，北京 100124)
［摘要］為了提高診斷效率與診斷結(jié)果的可靠性，根據(jù)空間網(wǎng)格結(jié)構(gòu)的構(gòu)成特點提出了面向子結(jié)構(gòu)的損傷定位方法，即根據(jù)網(wǎng)格結(jié)構(gòu)的組成規(guī)律，將其細分成子結(jié)構(gòu)，采用概率神經(jīng)網(wǎng)絡識別損傷可能發(fā)生的子結(jié)構(gòu)。以某單層柱面網(wǎng)殼試驗模型為例進行模擬損傷的定位研究，論述了訓練樣本確定的優(yōu)先準則，為提高損傷診斷的準確率采取了并集策略。計算結(jié)果表明，面向子結(jié)構(gòu)的初步損傷定位法對大型網(wǎng)格結(jié)構(gòu)進行損傷定位是可行的，為現(xiàn)有的面向節(jié)點的損傷定位提供了有效的補充，且該方法具有一定的工程實用價值。
［關(guān)鍵詞］空間網(wǎng)格結(jié)構(gòu); 單層柱面網(wǎng)殼; 面向子結(jié)構(gòu); 損傷定位; 概率神經(jīng)網(wǎng)絡
中圖分類號：TU393.3 文獻標識碼：A 文章編號：1002-848X(2014)06-0079-06
Substructure-oriented damage localization method of the spatial lattice structure
Liu Caiwei1, Zhang Yigang1, 2, Wu Jinzhi1
(1 Spatial Structures Research Center, Beijing University of Technology, Beijing 100124, China; 2 Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124, China)
Abstract: In order to improve the damage diagnosis efficiency and reliability of damage diagnosis results, substructure-oriented damage localization method was proposed based on the characteristics of spatial lattice structures. The lattice structure was subdivided into substructures based on the formation rule of lattice structure and probabilistic neural network was used to locate the substructures that were possible to be damaged. Take a single-layer cylindrical reticulated shell test model as an example, simulated damage localization study was carried out. The priority criteria of training samples were discussed and union strategy was taken in order to improve the diagnostic accuracy. The results show that the substructure-oriented preliminary damage localization to detect the defect position in the large lattice structure is feasible and the method has a certain practical value, providing an effective supplement for substructureoriented damage localization method.
Keywords: spatial lattice structure; single-layer latticed cylindrical shell; substructure-oriented; damage localization; probabilistic neural network
*國家自然科學基金資助項目（51278009）。
作者簡介：劉才瑋，博士研究生，Email:03150053@163.com。
參考文獻
［1］BIJAN SAMALI, ULRIKE DACKERMANN, JIANCHUN LI. Location and severity identification of notch-type damage in a two-storey steel framed structure utilizing frequency response functions and artificial neural network［J］. Advances in Structural Engineering, 2012, 15 (5):743-757.
［2］張毅剛,吳金志,梁鑫.面向節(jié)點的網(wǎng)格結(jié)構(gòu)損傷定位方法［J］.北京工業(yè)大學學報, 2004,30(2): 171-175.
［3］JIANG SHAOFEI, ZHANG CHUNMING, YAO JUAN. Eigen-level data fusion model by integrating rough set and probabilistic neural network for structural damage detection［J］. Advances in Structural Engineering, 2011, 14 (2):333-350.
［4］張育智,李喬,單德山.復雜結(jié)構(gòu)損傷識別的廣義子結(jié)構(gòu)法［J］.西南交通大學學報, 2009,44(2): 160-165.
［5］LAM H F, KO J M, WONG C W. Localization of damaged structural connections based on experimental modal and sensitivity analysis［J］. Journal of Sound and Vibration, 1998, 210 (1):91-115.
［6］LI HUIJUN,LIU CHUNGUANG，JIA LINGLING. Reliability and sensitivity analysis of double-layer spherical latticed shell［J］. International Journal of Space Structures, 2011, 26(1): 19-29.
［7］吳金志. 基于動力檢測的網(wǎng)格結(jié)構(gòu)損傷識別研究［D］. 北京：北京工業(yè)大學, 2005.
［8］LI JIANCHUN, DACKERMANN U,XU YOULIN,et al. Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles［J］. Structural Control and Health Monitoring, 2011, 18(2): 207-226.
