软计算研究及其在桥梁健康监测与状态评估中的应用
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摘要
大型桥梁健康监测与状态评估的难点之一是如何有效的处理大量监测信息并对桥梁运营状态做出合理评估,主要体现在反问题的不适定性、组合优化的计算复杂性、评估的不确定性、信号的非平稳性等方面。在求解这些问题方面,以二元逻辑、线性系统等为基础的传统求解方法存在不足,因而迫切需要发展新的、有效、实用的方法。
软计算(Soft Computing, SC)是集成了人工神经网络、群智能优化及不确定性推理的一类方法,在数据反演、全局非线性优化、决策分析、并行计算等方面具有较大优势。本文在研究其基本理论的基础上,结合桥梁健康监测与状态评估,对其中主要的决策和识别问题进行了系统的探索研究。
主要工作和研究内容如下：
(1)引入了软计算方法基本概念和特征,重点介绍了群智能优化算法。基于所建立的Benchmark优化模型,对群智能优化算法的性能进行了对比数值实验分析。
(2)对传统BP神经网络进行了改进。引入自适应学习率和局部最优检测算子,增强了网络全局最优性,并提高了网络识别精度。基于此,建立了用于南京长江大桥桥墩—船撞击荷载识别网络,可对船舶撞击力、撞击角度和撞击位置进行识别。数值算例表明,所建网络具有良好的记忆、联想和抗干扰能力,在船舶撞击力识别方面具有很快的收敛速度和较高的识别精度。
(3)提出了一种新的针对传感器优化布置的“数集编码法”及其遗传算法的交叉和变异操作模式。将所提出的遗传算法结合人工免疫算法的克隆机制,增强了算法在单目标和多目标情况下对传感器位置进行全局优化的能力。以汀泗河特大桥1／8缩尺模型及南京长江大桥为对象,对其监测传感器位置进行单目标和多目标优化。优化结果表明,所提出的优化方法编码长度短、操作灵活、具有较强的全局收敛性。单目标情况下,最优测点具有累积性与分区集中性。多目标情况下,可得到具有多种布置方案的Pareto解,方便决策者进行方案比选。
(4)提出了一种利用三端点区间数层次分析法对桥梁进行状态评估的方法,建立了三端点区间数层次分析法一致性衡量指标体系以及权重向量的优化模型,设计了具有自适应惯性系数调整和强局部搜索机制的自适应微粒群-退火算法用于全局优化搜索,通过优化求解可得到一致性指标及其最优权重。将所提出的三端点区间数层次分析法用于南京长江大桥的状态评估中,评估结果表明,三端点区间数层次分析法灵活、简单可以综合考虑评估中的不确定性,所提出的判断矩阵一致性检验方法及平均随机一致性指标参照值具有较高的工程应用前景。
(5)提出了基于蚁群-混沌追踪算法的信号稀疏分解方法,该方法可用于参数识别和信号时频分析。算法效率方面,通过仿真信号对比表明,所提出算法的计算效率是传统匹配追踪算法的15倍。模态参数识别方面,对汀泗河特大桥1／8缩尺模型进行了实验并从实测信号中提取模态参数,试验结果与理论分析结果对比表明,所提出的方法稳定、有效且识别精度高。时频分析方面,对南京长江大桥火车过桥复杂信号进行时频分析并得到了自适应谱,通过与小波谱和WVD谱的分析结果对比分析表明,自适应谱具有较高的时频聚集性。
One of the challenges in health monitoring and assessment of large scale bridges is to process measured data effectively and make reliable evaluations for the service states. Specifically, the primary difficulties include ill-posedness of the inverse problem, the computation complexity of combinatorial optimization, uncertainties in evaluations, non-stationarity in signal, etc. It is critical to develop new methods that can solve the above problems effectively and practically with pressing needs, since the techniques based on the dualistic logistic, linear system and conventional numerical analysis have many deficiencies.
Soft computing (SC), integrating artificial neural networks (ANN), swarm intelligence optimization (SIO) and uncertainty reasoning (UR) has more advantages in data inversion, global nonlinear optimization, decision-making analysis, etc. Based on the studies of SC, decision making and parameter identification are systematically researched combining bridge health monitoring and assessment.
The main contents and details are as follows:
(1)The basic concepts and characteristics of SC methods are introduced. As the important member of SC, the methods of SIO are also discussed. Comparison and analysis on the methods of SIO by numerical experiments are conducted based on the proposed benchmark for optimization.
(2) Improve the conventional BP (back propagation) ANN by the proposed self-adaptive ratio and the operator for local optimum check with the purpose of enhancing the identification precision. Based on this improvement, the ANN is established to identify impact force during the ship-bridge collision on the pier of the Nanjing Yangtze River Bridge (NYRB). By the established ANN, the magnitude, direction and location of impact force during the collision can be identified effectively due to the ability of memory, associability and anti-jamming of the proposed ANN. The results of numeric studies show that the proposed ANN has rapid convergence speed and high precision for the identification of the impact force.
(3) A new coding approach named "number set coding" and the corresponding crossover and mutation operator in genetic algorithm (GA) for the optimization of senor locations are proposed. Combining with the clone mechanism in artificial immunity system (AIS), the methods aiming at optimizing the placement of sensors are developed which can optimize the placement of sensors globally for both single-objective and multi-objective optimization. With the proposed methods, the optimal placement of monitoring sensors for the 1/8 scale structure of Ting Si He River Bridge (TSHRB) and NYRB are obtained under the defined single-objective and multi-objective. The results show the flexiblility due to shorter code length of "number set coding" and strong global convergence of the proposed method. For single-objective criterion, the optimal locations of sensors obtained accumulatively and congregated in certain regions. For multi-objective criterion, The Pareto solution representing multi-scheme is the convenient to compare the plans of sensor placements for decision makers.
(4) The analytical hierarchy processing (AHP) with the three points interval number (TPIN) is developed to evaluate the state of bridges. The indexes system for consistency check and the optimization model for the optimal weight are established. In order to obtain the optimal weight and the corresponding index of consistency from the defined optimization model, a self-adaptive particle swarm optimization (PSO)-simulating annealing (SA) algorithm with self-adaptive adjustability for inertia coefficient in PSO and strong local search ability of SA is proposed. Then the proposed AHP with TPIN (TPIN-AHP) is employed to evaluate the steel truss of NYRB. The results show the TPIN-AHP is simple, flexible and able to consider the uncertainty synthetically. The method to check the consistency of judgment matrix with TPIN and the indexes of the reference numbers of mean random judgment matrix has promising engineering applications.
(5) The ACO (ant conolt optimization)-Chaos Pursuit algrithom is proposed to decompose signal sparsely with the application to parameter identification and time-frequency analysis. Based on the decomposition of the simulating signal, the result shows that the speed for signal decomposation of proposed algrithom is 15 times faster than the conventional Matching Pursuit (MP) algrithom in the aspect of efficiency of the proposed algrithom. For modal parameter identification, the experiment on the 1/8 scale structure of TSHRB is executed, and the results show the proposed method is stable, efficient and has high precision for identification by comparison with theoretic analysis. The self-adaptive spectra for time-frequency analysis of measured signal from NYRB monitoring system are obtained by sparse decomposition with the proposed method. By comparison with the wavelet spectra and the Wigner-ville distribution (WVD) spectra of the same signal, it shows that the self-adaptive spectra obtained by the proposed algorithm for sparse decomposing has good time-frequency aggregation.
软计算(Soft Computing, SC)是集成了人工神经网络、群智能优化及不确定性推理的一类方法,在数据反演、全局非线性优化、决策分析、并行计算等方面具有较大优势。本文在研究其基本理论的基础上,结合桥梁健康监测与状态评估,对其中主要的决策和识别问题进行了系统的探索研究。
主要工作和研究内容如下：
(1)引入了软计算方法基本概念和特征,重点介绍了群智能优化算法。基于所建立的Benchmark优化模型,对群智能优化算法的性能进行了对比数值实验分析。
(2)对传统BP神经网络进行了改进。引入自适应学习率和局部最优检测算子,增强了网络全局最优性,并提高了网络识别精度。基于此,建立了用于南京长江大桥桥墩—船撞击荷载识别网络,可对船舶撞击力、撞击角度和撞击位置进行识别。数值算例表明,所建网络具有良好的记忆、联想和抗干扰能力,在船舶撞击力识别方面具有很快的收敛速度和较高的识别精度。
(3)提出了一种新的针对传感器优化布置的“数集编码法”及其遗传算法的交叉和变异操作模式。将所提出的遗传算法结合人工免疫算法的克隆机制,增强了算法在单目标和多目标情况下对传感器位置进行全局优化的能力。以汀泗河特大桥1／8缩尺模型及南京长江大桥为对象,对其监测传感器位置进行单目标和多目标优化。优化结果表明,所提出的优化方法编码长度短、操作灵活、具有较强的全局收敛性。单目标情况下,最优测点具有累积性与分区集中性。多目标情况下,可得到具有多种布置方案的Pareto解,方便决策者进行方案比选。
(4)提出了一种利用三端点区间数层次分析法对桥梁进行状态评估的方法,建立了三端点区间数层次分析法一致性衡量指标体系以及权重向量的优化模型,设计了具有自适应惯性系数调整和强局部搜索机制的自适应微粒群-退火算法用于全局优化搜索,通过优化求解可得到一致性指标及其最优权重。将所提出的三端点区间数层次分析法用于南京长江大桥的状态评估中,评估结果表明,三端点区间数层次分析法灵活、简单可以综合考虑评估中的不确定性,所提出的判断矩阵一致性检验方法及平均随机一致性指标参照值具有较高的工程应用前景。
(5)提出了基于蚁群-混沌追踪算法的信号稀疏分解方法,该方法可用于参数识别和信号时频分析。算法效率方面,通过仿真信号对比表明,所提出算法的计算效率是传统匹配追踪算法的15倍。模态参数识别方面,对汀泗河特大桥1／8缩尺模型进行了实验并从实测信号中提取模态参数,试验结果与理论分析结果对比表明,所提出的方法稳定、有效且识别精度高。时频分析方面,对南京长江大桥火车过桥复杂信号进行时频分析并得到了自适应谱,通过与小波谱和WVD谱的分析结果对比分析表明,自适应谱具有较高的时频聚集性。
One of the challenges in health monitoring and assessment of large scale bridges is to process measured data effectively and make reliable evaluations for the service states. Specifically, the primary difficulties include ill-posedness of the inverse problem, the computation complexity of combinatorial optimization, uncertainties in evaluations, non-stationarity in signal, etc. It is critical to develop new methods that can solve the above problems effectively and practically with pressing needs, since the techniques based on the dualistic logistic, linear system and conventional numerical analysis have many deficiencies.
Soft computing (SC), integrating artificial neural networks (ANN), swarm intelligence optimization (SIO) and uncertainty reasoning (UR) has more advantages in data inversion, global nonlinear optimization, decision-making analysis, etc. Based on the studies of SC, decision making and parameter identification are systematically researched combining bridge health monitoring and assessment.
The main contents and details are as follows:
(1)The basic concepts and characteristics of SC methods are introduced. As the important member of SC, the methods of SIO are also discussed. Comparison and analysis on the methods of SIO by numerical experiments are conducted based on the proposed benchmark for optimization.
(2) Improve the conventional BP (back propagation) ANN by the proposed self-adaptive ratio and the operator for local optimum check with the purpose of enhancing the identification precision. Based on this improvement, the ANN is established to identify impact force during the ship-bridge collision on the pier of the Nanjing Yangtze River Bridge (NYRB). By the established ANN, the magnitude, direction and location of impact force during the collision can be identified effectively due to the ability of memory, associability and anti-jamming of the proposed ANN. The results of numeric studies show that the proposed ANN has rapid convergence speed and high precision for the identification of the impact force.
(3) A new coding approach named "number set coding" and the corresponding crossover and mutation operator in genetic algorithm (GA) for the optimization of senor locations are proposed. Combining with the clone mechanism in artificial immunity system (AIS), the methods aiming at optimizing the placement of sensors are developed which can optimize the placement of sensors globally for both single-objective and multi-objective optimization. With the proposed methods, the optimal placement of monitoring sensors for the 1/8 scale structure of Ting Si He River Bridge (TSHRB) and NYRB are obtained under the defined single-objective and multi-objective. The results show the flexiblility due to shorter code length of "number set coding" and strong global convergence of the proposed method. For single-objective criterion, the optimal locations of sensors obtained accumulatively and congregated in certain regions. For multi-objective criterion, The Pareto solution representing multi-scheme is the convenient to compare the plans of sensor placements for decision makers.
(4) The analytical hierarchy processing (AHP) with the three points interval number (TPIN) is developed to evaluate the state of bridges. The indexes system for consistency check and the optimization model for the optimal weight are established. In order to obtain the optimal weight and the corresponding index of consistency from the defined optimization model, a self-adaptive particle swarm optimization (PSO)-simulating annealing (SA) algorithm with self-adaptive adjustability for inertia coefficient in PSO and strong local search ability of SA is proposed. Then the proposed AHP with TPIN (TPIN-AHP) is employed to evaluate the steel truss of NYRB. The results show the TPIN-AHP is simple, flexible and able to consider the uncertainty synthetically. The method to check the consistency of judgment matrix with TPIN and the indexes of the reference numbers of mean random judgment matrix has promising engineering applications.
(5) The ACO (ant conolt optimization)-Chaos Pursuit algrithom is proposed to decompose signal sparsely with the application to parameter identification and time-frequency analysis. Based on the decomposition of the simulating signal, the result shows that the speed for signal decomposation of proposed algrithom is 15 times faster than the conventional Matching Pursuit (MP) algrithom in the aspect of efficiency of the proposed algrithom. For modal parameter identification, the experiment on the 1/8 scale structure of TSHRB is executed, and the results show the proposed method is stable, efficient and has high precision for identification by comparison with theoretic analysis. The self-adaptive spectra for time-frequency analysis of measured signal from NYRB monitoring system are obtained by sparse decomposition with the proposed method. By comparison with the wavelet spectra and the Wigner-ville distribution (WVD) spectra of the same signal, it shows that the self-adaptive spectra obtained by the proposed algorithm for sparse decomposing has good time-frequency aggregation.
引文
[1]欧进萍.重大工程结构的累计损伤与安全评定.走向21世纪的中国力学—中国科协第9次“青年科学家论坛”报告文集.北京：清华大学出版社,1996.179-189
[2]王柏生,倪一清,高赞明.青马大桥桥板结构损伤位置识别的数值模拟.土木工程学报,2001,34(3)：67-73
[3]李爱群,缪长青,李兆霞,等.润扬长江大桥结构健康监测系统研究.东南大学学报,2003,33(5)：544-548
[4]谢强,薛松涛.土木工程结构健康监测的研究状况与进展.中国科学基,2001,(5)：285-288
[5]Myroll F, Dibiagio E. Instrumentation for monitoring the Skarnsunder Cable-stayed Bridge. In:Jon Krodeborg Balkema, eds. Proceedings of the Third Symposium on Strait Crossing. Rotterdam:Symposium on Strait Crossing,1994.207-215
[6]Andersen E Y. Structural monitoring of the Great Belt East Bridge. In:Jon Krodeborg Balkema, eds. Proceedings of the Third Symposium on Strait Crossing. Rotterdam: Symposium on Strait Crossing,1994.54-62
[7]Curran P, Tilly G. Design and monitoring of the Flintshire Bridge. Structural Engineering International,1999,3:225-228
[8]邬晓光,徐祖恩.大型桥梁健康监测动态及发展趋势.长安大学学报(自然科学版),2003,23(1)：39-42
[9]Chueng M S, Tadros G S, Brown T G, et al. Field monitoring and research on performance of the Confederation Bridge. Canadian Journal of Civil Engineering, 1997,24 (6):951-962
[10]史家钧,兰海,郭志明.桥梁健康监测中的若干问题.见：中日结构减振及健康监测研讨会暨第三届中国结构抗振控制年会论文集.上海：同济大学出版社,2002.1-15
[11]Lau C K, Wong K Y, Ho K S. The Wind and Structural Health Monitoring System (WASHMS) for the three long-span cable-supported bridges in Hongkong. In: Proceedings of the fifteenth International Association for Bridge and Structural Engineering (IABSE). Copenhagen:IABSE,1996
[12]刘正光,麦惠培,黄启远,等.青马大桥的初步监测结果.见：中国土木工程学会桥梁及结构工程学会第十三届年会论文集.上海：同济大学出版社,1998. 730-740
[13]史家钧,邵志常.上海徐浦大桥结构状态监测系统.见：中国土木工程学会桥梁及结构工程学会第十三届年会论文集.上海：同济大学出版社,1998.779-784
[14]欧庆保,周世忠.江阴长江公路大桥上部结构的安全监测系统.见：龚晓南,编.中国土木工程学会第九届年会论文集——工程安全及耐久性.北京：中国水利水电出版社,2000.202-206
[15]Huang F L, He X H, Chen Z Q, et al. Structural safety monitoring for Nanjing Yangtze River Bridge. Journal of Central South University of Technology,2004,11 (3):332-335
[16]曾储惠.南京长江大桥健康监测和状态评估的研究：[硕士学位论文].长沙：中南大学,2004
[17]杨孚衡.芜湖长江大桥斜拉桥长期监测、安全评估及报警系统的研制.见：康熊,钱立新,编.铁道科学技术新进展——铁道科学研究院五十五周年论文集.北京：中国铁道出版社,2005.82-92
[18]苏木标,杜彦良,孙宝臣,等.芜湖长江大桥长期健康监测与报警系统研究.铁道学报,2007,29(2)：71-76
[19]Sun B N, Wu X P, Lou W J, et al. The real-time health monitoring system of Qianjiang 4th bridge. In:Chen Y M, eds. Proceedings of the International Seminar on Environmental Vibration:Prediction, Monitoring and Evaluation. Beijing:China Communication Press,2003.420-426
[20]郭健,陈勇,孙炳楠,等.钱江四桥健康监测系统中的关键性问题及损伤识别方法研究.见：中国土木工程学会桥梁及结构工程分会,编.第十七界全国桥梁学术会议论文集.北京：人民交通出版社,2006.671-679
[21]张启伟.大型桥梁健康监测概念与监测系统设计.同济大学学报,2001,29(1)：65-69
[22]郭健,顾正维,孙炳楠,等.基于小波分析的桥梁健康监测方法.工程力学,2006,23(12)：129-135
[23]郭嗣踪,陈刚.信息科学中的软计算方法.沈阳： 东北大学出版社,序5-7
[24]Zadeh LA. Soft Computing and Fuzzy Logic. IEEE Transactions on Software,1994, 11 (6):48-56
[25]Zadeh L A. Fuzzy logic, neural networks and soft computing. Communication of the ACM.1994,37 (3):77-84
[26]蔡自兴.人工智能及其应用(第三版).北京： 清华大学出版社,2004.8
[27]詹海刚.基于软计算的海洋水色遥感反演：[博士学位论文].青岛：青岛海洋大学,2001
[28]Yu Y, YuDJ, Cheng J S. A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration,2006,294:267-277
[29]Marcu T, Seliger B K, Stucher R. Design of fault detection for a hydraulic looper using dynamic neural networks. Control Engineering Practice,2008,16:192-213
[30]Guo Q J, Yu H B, Xu A D. A hybrid PSO-GD based intelligent method for machine diagnosis. Digital Signal Processing,2006,16:402-418
[31]Taplak H, Uzmay I, Yildirim S. An artificial neural network application to fault detection of a rotor bearing system. Industrial Lubrication and Tribology,2006,58 (1):32-44.
[32]Djarfour N, Aifa T, Baddari K, et al.Application of feedback connection artificial neural network to seismic data filtering. C. R. Geoscience,2008,340:335-344
[33]Koh C G, Chen Y F, Liaw C Y. A hybrid computational strategy for identification of structural parameters. Computers and Structures,2003,81:107-117
[34]Li L L, Wang L, Liu L H. An effective hybrid PSOSA strategy for optimization and its application to parameter estimation. Applied Mathematics and Computation, 2006,179:135-146
[35]Jaszkiewicz A. Genetic local search for multi-objective combinatorial optimization. European Journal of Operatioanal Research,2002,137:50-71
[36]Hossen A, Al-Wadahi F, Jervase J A. Classification of modulation signals using statistical signal characterization and artificial neural networks. Engineering Applications of Artificial Intelligence,2007,20:463-472
[37]Boryczka U. Finding groups in data:Cluster analysis with ants. Applied Soft Computing,2009,9:61-70
[38]Kuo R J, Lin S Y, Shih C W. Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan. Expert Systems with Applications,2007,33:794-808
[39]Martin J D, Morton Y T, Zhou Q H. Neural network development for the forecasting of upper atmosphere parameter distributions. Space Weather Advances in Space Research,2005,36 (12):2480-2485.
[40]郄志红.大坝安全监测资料正反分析的智能软计算方法及其应用：[博士学位论文].天津：天津大学,2005
[41]王光远.工程软设计理论.北京：科学出版社,1992.1-8
[42]王光远.工程结构系统软设计理论及应用.北京：国防工业出版社,1996.1-3
[43]中国科学技术培训中心.迎接交叉科学的时代.北京：光明日报社,1986.6
[44]阎石.结构振动智能控制的人工神经网络与模糊逻辑方法研究：[博士学位论文]. 大连：大连理工大学,2000
[45]刘建军.新型磁流变阻尼器及其智控制方法研究：[博士学位论文].天津：天津大学,2008
[46]周东.基坑支护工程遗传优化设计研究：[博士学位论文].广西：广西大学,2002
[47]易伟建,刘霞.遗传演化结构优化算法.工程力学,2004,21(3)：66-71
[48]淡丹辉.智能土木(桥梁)结构理论及其核心算法研究.[博士学位论文].成都：西南交通大学,2002
[49]Jeyasehar C A, Sumangala K. Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach. Computers and Structures,2006, 84:1709-1718
[50]Sahoo B, Maity D. Damage assessment of structures using hybrid neuro-genetic algorithm. Applied Soft Computing,2007,7:89-104
[51]Li Z X, Yang X M. Damage identification for beams using ANN based on statistical property of structural responses. Computers and Structures,2008,86:64-71
[52]孙宗光,高赞明,倪一清.基于神经网络的桥梁损伤位置识别.工程力学,2004,21(1)：42-47
[53]李国强,李杰.工程结构动力检测理论与应用.北京：科学出版社,2001.113-132
[54]Friswell M I, Penny J E T, Garvey S D. A combined genetic and eigensensitivity algorithm for the location of damage in structures. Computers and Structures,1998, 69:547-556
[55]张育智,李乔,单德山.SOFM网络在结构损伤位置识别中的应用.振动与冲击,2007,26(2)：160-163
[56]张育智.基于神经网络与数据融合的结构损伤识别理论研究：[博士学位论文].成都：西南交通大学,2007
[57]黄天立.结构系统和损伤识别的若干方法研究：[博士学位论文].上海：同济大学,2007
[58]吴大宏.基于遗传算法与神经网络的桥梁结构健康监测系统研究：[博士学位论文].成都：西南交通大学,2004
[59]李守巨.基于计算智能的岩土力学模型参数反演方法及其工程应用：[博士学位论文].大连：大连理工大学,2004
[60]杨成祥.材料非线性模型的进化识别方法研究：[博士学位论文].沈阳：东北大学,2001
[61]逯静洲,林皋.人工神经网络技术在混凝土本构模型中的应用.土木工程学报,2003,36(4)：38-42
[62]苏怀智,李季,吴中如.大坝及岩基物理力学参数优化反演分析研究.水利学报. 2007,增刊：129-134
[63]王建.大坝安全监控集成智能专家系统关键技术研究：[博士学位论文].南京：河海大学,2002
[64]何勇军,薛宝林.土石坝渗流的神经网络监控模型.河海大学学报(自然科学版),2001,29(2)：79-82
[65]林铁良,王君杰,陈艾荣,等.基于事故记录的船撞桥故障树建立.同济大学学报(自然科学版),2006,34(4)：467-471
[66]菜键,汪克来.桥梁受船舶碰撞的思考.安徽建筑工业学院学报(自然科学版)2008,16(1)：1-3,26
[67]Sipke E M, Aksel G F. Ship collision with bridges, review of accidents. Ship Collision Analysis. In:Gluver, Olsen eds. Ship collision analysis:proceedings of the International Symposium on advances in ship collision analysis. Rotterdam: Balkema,1998.3-11.
[68]邵旭东,占雪芳,廖朝华,等.从美国阳光大道桥被撞重建看现有桥梁防撞风险评估.公路,2007,8：33-37
[69]戴彤宇,聂武,刘伟力.长江干线船撞桥事故分析.中国航海,2002,4：44-47
[70]戴彤宇.船撞桥及其风险分析：[博士学位论文].哈尔滨：哈尔滨工业大学,2002
[71]杜旭升.6-15九江大桥船撞事故引发的思考.中国海事,2007,9：33-35
[72]项海帆,范立础,王君杰.船撞桥设计理论的现状与需进一步研究的问题.同济大学学报,2002,30(4)：386-392
[73]Yun H, Nayeri R, Tasbihgoo F, et al. Monitoring the collision of a cargo ship with the Vincent Thomas Bridge. Structral Control and Health Monitoring,2008,15 (2): 183-206
[74]何旭辉.南京长江大桥结构健康监测及其关键技术研究：[博士学位论文].长沙：中南大学,2004
[75]耿波,王君杰,汪宏.桥梁船撞风险评估系统总体研究.土木工程学报,2007,40(5)：34-40
[76]刘小虎,杨文兵,王乘.大桥受船撞击灾害的计算机评价方法.工程力学,2000,17(8)：124-131
[77]魏培军,章梓茂.弹性动力学反问题的不适定性及其广义解,力学与实践,2001,23(3)：43-45
[78]邹振祝,冯文杰.弹性动力学反问题研究概况.石家庄铁道学院学报,2000,13(3),7-12
[79]杨慧珠,张高远,鲁小荣.动力学的反问题.固体力学发展趋势,北京： 北京理工大学出版社,1995.55-73
[80]智浩,文祥荣,缪龙秀,等.动态载荷的频域识别方法,北方交通大学学报,2000,24(4)：5-10
[81]唐秀近.动态力识别的时域方法.大连工学院学报,1987,26(4)：21-27
[82]Law S S, Chan T H T, Zeng Q H. Moving force identification:A time domain method, Journal of Sound and Vibration,1997,201 (1):1-22
[83]魏星原,宋斌,郑效忠.荷载识别的逆系统方法.振动、测试与诊断,1995,15(3)：35-43
[84]Kumpatis, Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks,1990,1 (1):4-27
[85]Cook A B, Fuller C R,0 Brien W F, et al. Artificial Networks in Detection for Predicting Nonlinear Dynamic Helicopter Loads, AIAA Journal,1994,32 (5), 2337-2344
[86]张方,朱德懋.基于神经网络模型的动荷载识别.振动工程学报,1997,10(2)：156-162
[87]徐宜桂,马西庚,史铁林,等.基于神经网络的动力学反解算法及其应用研究.机械工程学报,1998,34(4)：106-110
[88]瞿伟廉,王锦文.振动结构动态荷载识别综述.华中科技大学学报(城市科学版),2004,21(4)：1-4,8
[89]Cao X, Sugiyama Y, Mitsui Y. Application of artificial neural networks to load identification. Computers and Structures,1998,69:63-78
[90]金虎,楼文娟,陈勇.基于自适应BP神经网络的桥梁结构荷载识别.浙江大学学报(工学版),2005,39(10)：1596-1602
[91]吴大宏,赵人达.基于神经网络的混凝土桥梁荷载识别方法研究.中国铁道科学,2002,23(1)：25-28
[92]董会丽,郑世杰.基于RBF神经网络的复合材料层合壳荷载识别.工程力学,2008,25(3)：64-67
[93]李忠献,陈锋,王波.基于BP神经网络的桥上移动荷载分阶段识别方法.工程力学,2009,25(9)：85-92
[94]秦权.桥梁结构的健康监测.中国公路学报,2000,13(2)：37-42
[95]韩大建,谢峻.大跨度桥梁健康监测技术的近期研究进展.桥梁建设,2002,(6)：69-73.
[96]沈雁彬.基于动力特性的空间网格结构状态评估方法及检测系统研究：[博士学位论文].杭州：浙江大学,2007
[97]赵翔,李爱群,韩晓林.润扬大桥结构健康监测系统传感器测点布置.工业建筑,2005,35(1)：82-85
[98]李宏男,李东升.土木工程结构安全性评估、健康监测及诊断述评.地震工程与工程振动,2002,22(3)：82-90
[99]崔飞,袁万城,史家钧.传感器优化布设在桥梁健康监测中的应用.同济大学学报,1999,27(2)：165-169
[100]何浩祥.空间结构健康监测的理论与试验研究：[博士学位论文].北京：北京工业大学,2006
[101]Carne T G, Dohrmann C R. A modal test design strategy for modal correlation. In: Bathe K J eds. Proc 13th Intl Modal Analysis Conference. New York:Union College, Schenectady,1995.927-933
[102]Kammer D C, Tinker M L. Optimal placement of triaxial accelerometers for modal vibration tests. Mechanical Systems and Signal Processing,2004, (18):29-41
[103]Heo G, Wang HL, Satpathi D. Optimal transducer placement for health monitoring of long span bridge. Soil Dynamics and Earthquake Engineering,1997,16 (7-8): 495-502
[104]谢强, 薛松涛.结构健康监测传感器优化布置的混合算法.同济大学学报(自然科学版),2006,34(6)：726-731
[105]李戈, 秦权, 董聪.用遗传算法选择悬索桥监测系统中传感器的最优布点.工程力学,2000,17(1)：24-34
[106]Meo M, Zumpano G On the optimal sensor placement techniques for a bridge structure. Engineering Structures,2005,27 (10):1488-1497
[107]Z.N. Li, J. Tang, Q.S. Li. Optimal sensor locations for structural vibration measurements. Applied Acoustics 65 (2004) 807-818
[108]Heo G, Wang HL, Satpathi D. Optimal Transducer Placement for Health Monitoring of Long Span Bridge. Soil Dynamics and Earthquake Engineering,1997,16(7-8): 495-502
[109]刘斌,姚永丁,叶贵如.斜拉桥传感器优化布点的研究.工程力学,2005,22(5)：171-176
[110]魏民祥,闫桂荣,郭万林.基于信息嫡原理的结构振动传感器的布局.仪器仪表学报.2004,25(6)：802-804
[111]Papadimitriou C. Pareto optimal sensor locations for structural identification. Comput. Methods Appl. Mech. Engrg.2005,194:1655-1673
[112]张连振,黄侨,王潮海.基于多目标遗传算法的传感器优化布点研究.工程力学,2007,24(4)：168-172
[113]Sepulveda A E, schmit J L A. Optimal palcement of active elements in control augmented structural systhesis. AIAA Journal,1993,31 (10):1906-1915
[114]刘娟,黄维平.模态试验中传感器优化配置的逐步削减法.海洋工程,2004,122(11)：32-36
[115]刘娟,黄维平.传感器优化配置的修正逐步累积法.青岛海洋大学学报,2003,33(3)：476-482
[116]Hafkta R T, Adelman H M. Selection of actuator locations for static shape control of large space structures by heuristic integer programming. Computers and structures, 1985,20 (1-3):3506-3514
[117]黄维平,刘娟,李华军.基于遗传算法的传感器优化配置.工程力学,2005,22(1)：113-117
[118]李亚东.既有桥梁评估初探.桥梁建设,1997,(3)：18-21
[119]范剑锋.桥梁健康状态的智评估方法研究：[博士学位论文].武汉：武汉理工大学,2006
[120]兰海,史家钧.灰色关联分析与变权综合法在桥梁评估中的应用.同济大学学报,2001,29(1)：50-54
[121]项贻强,伍华成,翁沙羚.大跨度预应力混凝土斜拉桥的状态评估体系研究.中外公路,2005,25(5)：73-76
[122]任远,黄乔,林阳子.大跨度斜拉桥综合评估系统的研制与开发.南京航空航天大学学报,2007,39(4)：535-539
[123]刘沐宇,卢傲.基于GIS的钢管混凝土拱桥安全性评价系统.武汉理工大学学报(交通科学与工程版),2007,31(3)：442-445
[124]Guo T, Li A Q, Zhao D L, et al. Synthetical condition assesament of long span suspension bridge based on cbseness degree and FAHP. Journal of Southeast University (English Edition),2007,23 (2):261-265
[125]Huang Q, Lin Y Z, Ren Y. Comprehensive evaluation method for superstructures of prestressed concrete girder bridges. Journal of Southeast University (English Edition),2008,12 (1):64-68
[126]杨则英,曲建波,黄承逵.基于模糊综合评判和层次分析法的桥梁安全性评估.天津大学学报,2005,38(12)：1063-1067
[127]Wang Y M, Liu J, Taha M S E. An integrated AHP-DEA methodology for bridge risk assessment, Computers and Industrial Engineering,2008,54 (3):513-525
[128]Tee A B, Bowman M D, Sinha K C. A fuzzy mathematical approach for bridge condition evaluation, Journal of Civil Engineering systems,1988,5 (1):17-25
[129]淡丹辉,孙利民.Mamdani型模糊推理系统在桥梁状态评估中的应用.同济大学学报(自然科学版),2004,32(9)：1131-1135
[130]王根会,朱尚清.基于模糊系统的铁路钢板梁桥加固效果评估分析.铁道学报, 2007,29(3)：123-126
[131]郭琦,贺拴海,白云.基于神经网络的简支梁桥预应力衰减评估模型.长安大学学报(自然科学版),2007,27(6)：53-57
[132]范剑锋,袁海庆,彭自强.基于模型智能修正的桥梁健康状态评估.武汉理工大学学报,2008,30(4)：84-87
[133]韩大建,杨炳尧,颜全胜.用人工神经网络方法评估桥梁缺损状况.华南理工大学学报(自然科学版),2004,32(9)：72-75,96
[134]姚敏,张森.模糊一致矩阵及其在软科学中的应用.系统工程,1997,15(2)：54-57
[135]姚敏,黄燕君.模糊一致关系及其应用.电子科技大学学报,1997,26(6)：632-635
[136]姚敏,张森.模糊一致矩阵及其在决策分析中的应用.系统工程理论与实践,1998,(5)：78-81
[137]张吉军.模糊层次分析法(FAHP).模糊系统与数学,2000,14(2)：80-88
[138]吕跃进.基于模糊一致矩阵的模糊层次分析法的排序.模糊系统与数学,2002,16 (2)：79-85
[139]张吉军.模糊一致判断矩阵3种排序方法的比较研究.2003,25(11)：1370-1372
[140]徐泽水.AHP中两类标度的关系研究.系统工程理论与实践,1999,(7)：97-101
[141]冯向前,魏翠萍,李宗植,等.三角模糊数互反判断矩阵的一致性及排序研究.统计与决策,2007,(7)：27-28
[142]徐泽水.三角模糊数互补判断矩阵的一种排序方法.模糊系统与数学,2002,16(1)：47-50
[143]龚艳冰,陈森发.三角模糊数互补判断矩阵的一种排序方法.模糊系统与数学,2008,22(1)：127-131
[144]徐泽水,达庆利.区间数排序的可能度法及其应用.系统工程学报,2003,18(1)：67-70
[145]徐泽水,刘海峰.六类不确定型判断矩阵的相容性研究.模糊系统与数学,2003,17(2)：53-58
[146]冯向前,魏翠萍,李宗植.区间数互补判断矩阵一致性及其权重算法研究.数学的实践与认识,2007,37(19)：87-93
[147]朱建军,刘思峰,王(?)华.群决策中两类三端点区间数判断矩阵的集结方法.自动化学报,2007,33(3)：297-301
[148]朱建军宋传平,刘思峰等.一类三端点区间数判断矩阵的一致性及权重研究.系统工程学报,2008,23(1)：22-27
[149]Zhou S Z, Liu S F, Zhu J J, etal. Choosing enterprise advertisement medium approach of three-point interval number comparison matrix method. In:Proceedings of 2007 IEEE International Conference on Gray Systems and Intelligence Service. 1412-1417
[150]Lu Z P, Tian F, Zhu J J. Decision-making Approach Based on Novel Linguistic Preference. In:Global Congress on Intelligent Systems, Intelligent Systems,2009. GCIS'09. WRI Global Congress on.343-346
[151]Tian F, Zhu J J, Ma B. Evaluation of Government Energy-Saving Policy Performance Base on Three-Point Interval Gray Number. In:Proceedings of 2007 IEEE International Conference on Gray Systems and Intelligence Service.1314-1316
[152]郭健,顾正维,孙炳楠,等.基于小波分析的桥梁健康监测方法.工程力学,2006,23(12)：129-135
[153]Cooley J W, Tukey J W. An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation,1965,19 (90):297-301
[154]谭为民,彭东林,郭小渝.三种典型信号处理方法浅析.测控技术,2003,22(6)：15-16
[155]张贤达,保铮.非平稳信号分析与处理.北京：国防工业出版社,2001.12-14
[156]Gabor D. Theory of communication. Journal of IEEE,1946,93:429-457
[157]Meyer Y. Wavelets.-algorithms and applications. Philadelphia.-Society for Industrial and Applied Mathematics,1993
[158]Daubechies I. Orthonormal base of compactly supported wavelet. Communication on Pure and Applied Mathematics,1988, XII:909-996
[159]Daubechies I. The wavelet transform, time frequency localization and signal analysis. IEEE Transactions on Information Theory,1990,36 (5):961-1005
[160]Mallat S. A theory for multiresolusion signal decomposition:the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989,25 (11):674-693
[161]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series A,1998,454:903-995
[162]Huang N E, Shen Z, Long S R. A new view of nonlinear water waves:the Hilbert spectrum. Annual Review of Fluid Mechanics,1999,31:417-457
[163]丁康,陈健林,苏向荣.平稳和非平稳振动信号的若干处理方法的研究.振动工程学报,2003.16(1)：1-10
[164]钟佑明,秦树人,汤宝平.Hilbert-Huang变换中的理论研究.振动与冲击,2002,21(4)：13-18
[165]程军圣,于德介,杨宇.基于支持矢量回归机的Hilbert-Huang变换端点效应问题的处理方法.机械工程学报,2006,42(4)：23-31
[166]赵进平.异常事件对EMD方法的影响及其解决方法研究.青岛海洋大学学报,2001,31(6)：805-814
[167]钟佑明,金涛,秦树人.希尔伯特倾髦变换中的一种新包络线算法.数据采集与处理,2005,20(1)：13-17
[168]Classen T C M, Mecklenbrauker W F G. The Wigner Distribution-part I. Philips Journal of Research,1980,35:217-250
[169]Classen T C M, Mecklenbrauker W F G. The Wigner Distribution-part II. Philips Journal of Research,1980,35:276-300
[170]Classen T C M, Mecklenbrauker W F G. The Wigner Distribution-part III. Philips Journal of Research,1980,35:372-389
[171]Mark W D. Spectral analysis of the convolution and filtering of non-stationary stochastic process. Journal of Sound and Vibration,1970,11 (1):19-63
[172]丁康,陈健林,苏向荣.平稳和非平稳振动信号的若干处理方法及发展.振动工程学报,2003,16(1)：1-10
[173]邹红星,周小波,李衍达.时频分析：回溯与前瞻.电子学报,2000,9：78-84
[174]Coifman R R, Wickerhauser M V. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory,1992,38 (2):713-718
[175]Mallat S, Zhang Z. Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing,1993,41 (12):3397-3415
[176]Daubechies I. The wavelet transform, time-frequency localization and signal analysis, IEEE Transactions on Information Theory,1990,36 (5):961-1005
[177]Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Review,2001,43 (1):129-159
[178]Qian S E, Chen D P. Signal representation using adaptive normalized Gaussian functions. Signal Processing,1994,36 (1):1-11
[179]Bultan A. A four-parameter atomic decomposition of chirplets, IEEE Transactions on Signal Processing,1999,47 (3):731-744
[180]Zou H, Chen Y, Zhu J, et al. Steady-motion-based Dopplerlet transform application to the estimation of range and speed of a moving sound source, IEEE Journal of Oceanic Engineering,2004,29 (3):887-905
[181]Feng Z P, Chu F L. Application of atomic decomposition to gear damage detection. Journal of Sound and Vibration,2007,302 (1-2):138-151
[182]Yang H Y, Mathew J, Ma L. Fault diagnosis of rolling element bearings using basis pursuit. Mechanical Systems and Signal Processing,2005,19(2):341-356
[183]孟庆丰, 范虹, 王祺.匹配追踪信号分解与往复机械故障特征提取技术研究.西安交通大学报,2001,35(7)：696-699
[184]朱明,金炜东,普运伟.基于Chirplet原子的雷达辐射源信号特征提取.红外与毫米波学报,2007,26(4)：302-306
[185]徐鹏.信号的稀疏分解及其在脑电信号处理中的应用研究：[博士学位论文].成都：电子科技大学,2006
[186]姜玉亭.基于稀疏分解的图像去噪.西南交通大学硕士学位.成都：西南交通大学.2005
[187]张跃飞.基于稀疏分解的图像压缩.西南交通大学硕士学位.成都：西南交通大学.2006
[188]Spanos P D, Giaralis A, Politis N P. Time-frequency representation of earthquake accelerograms and inelastic structural response records using the adaptive chirplet decomposition and empirical mode decomposition. Soil Dynamics and Earthquake Engineering,2007,27 (7):675-689
[189]Wang Y. Seismic time-frequency spectral decomposition by matching pursuit. Geophysics,2007,13 (72):13-20
[190]Natarajan B K. Sparse approximate solutions to linear systems. SIAM Journal on Computing,1995,24 (2):227-234
[191]Davis G M, Mallat S, Avellaneda M. Greedy adaptive approximations. Constructive approximation.1997,13 (1):57-98
[192]Adelino R, Silva F D. Atomic decomposition with evolutionary pursuit. Digital Signal Processing,2003,13 (2):317-337
[193]高瑞,徐华楠,胡钢.基于GA和过完备原子库划分的MP信号稀疏分解算法.科学技术与工程,2008,8(4)：914-916,920
[194]尹忠科,王建英,Vandergheynst P.基于GA和原子特性的信号稀疏分解.铁道学报,2005,27(3)：58-61
[195]徐鹏,尧德中.一种引入退火降温阈值函数约束的改进MP稀疏分解方法.电子与信息学报,2007,29(7)：1645-1648
[196]Aarts E, Van H K. et al. A unifying approach on heuristic search. Annals of Opearation Research,1995,55:81-99
[197]王凌,刘波.微粒群优化与调度算法.北京：清华大学出版社,2008.15-33
[198]McCulloch W S, Pitts W H. A logical calculus of the ideas immanent in nervous activity. Bulletin Mathematical Biophysics,1943, (5):115-133
[199]Hebb D O. The organization of behavior:A neuro psychological theory. New York: Wiley,1949.1-12
[200]Rosenblatt F. Principles of neurodynamics:Perceptrons and the theory of brain mechanisms. New York:Spartan,1962.3-20
[201]周明,孙树栋.遗传算法原理及应用.北京：国防工业出版社,1999.59
[202]Kennedy J, Eberhart R. Particle swarm optimization.In: Proceedings of IEEE International Conference on Neural Networks. New York:IEEE,1995.1942-1948
[203]Gallad A E, Hawary M E, Sallaam A, et al. Enhancing the particle swarm optimizer via proper parameters selection. In:Proceedings of the 2002 IEEE Canadian Conference on Electrical and Engineering. New York:IEEE,2002.792-797
[204]Shi Y, Eberhart R. A modified particle swarm optimizer. In:Proceedings of IEEE International Conference on Evolutionary Computation. New York:IEEE,1998. 69-73
[205]Clerk M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trasactions on Evolutionary Computation. 2002,6 (1):58-73
[206]Fan H Y, Shi Y H. Study of vmax of the particle swarm optimization algorithom. Purdue School of Engineering and Technology. INPUI 2001
[207]彭宇,彭喜元,刘兆庆.微粒群算法参数效能的统计分析.电子学报,2004,32(2)：209-213.
[208]Eberhert R C, Shi Y H. Comparing inertia weights and constriction factors in particle swarm optimization. In:Proceedings of IEEE International Congress on Evolutionary Computation. New York:IEEE,2000.84-88
[209]Eberhert R C, Shi Y H. A modified particle swarm optimizer. In:Proceedings of the IEEE International Conference on Evolutionary Computation. New York:IEEE,1998. 69-73
[210]Eberhert R C, Shi Y H. Empirical study of particle swarm optimization. In: Proceedings of the Congress on Evolutionary Computation. New York:IEEE,1999. 1954-1950
[211]Suganthan P N. Particle swarm optimizer with neighbourhood operator. In: Proceedings of the Congress on Evolutionary Computation.1958-1962
[212]Colorni A, Dorigo M, Maniezzo V, et al. Distributed optimization by ant colonies. In:Prooeedings of European Conference on artificial Life. Paris:Elsevier,1991. 134-142
[213]Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents. IEEE Transaction on Sytems, Man, and Cybernetics-Part B, 1996,26 (1):29-41
[214]Bonabeau E, Dorigo M, Theraulaz G. Inspiration for Optimization from Social Insect Behavior. Nature,2000,406 (6):39-42
[215]杨剑峰.蚁群算法原理及应用：[博士学位论文].杭州：浙江大学,2007
[216]陈慰峰.医学免疫学.北京：人民卫生出版社,2001.1-3
[217]林学颜,张玲.现代细胞与分子免疫学.北京：科学出版社,2000.3-5
[218]Jerne N K. Towards a Network Theory of the Immune System. Annual Immunology, 1974,125 (1-2):373-389
[219]Farmer J D, Kauffman S A, Packard N H, et al. Adaptive dynamic networks as models for the immune system and autocatalytic sets. Annals of the New York Academy of Sciences,1987,504 (1):118-131
[220]孙宁.人工免疫优化算法及其应用研究：[博士学位论文].哈尔滨：哈尔滨工业大学,2006
[221]Goldberg D E, Segrest P. Finite Markov chain analysis of genetic algorithms. In: Grefenstette J J, eds. Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application. Hillsdale,NJ,USA:Erlbaum Associates,1987.1-8
[222]Rudolph G. Convergence analysis of canonical genetic algorithms. IEEE transactions on Neural Networks,1994,5 (1):96-101
[223]雷开友.粒子群算法及其应用研究：[博士学位论文].重庆：西南大学,2006
[224]王俊年.微粒群算法及其在锌电解整流供电系统优化中的应用研究：[博士学位论文].长沙：中南大学,2006
[225]Gutjahr W J. A graph-based ant system and its convergence. Future generation computer systems,2000,16 (9):873-888
[226]Gutjahr W J. ACO algorithms with guaranteed convergence to the optimal solution. Information Processing Letters,2002,82 (3):145-153
[227]Stiltzle T, Dorigo M. A short convergence proof for a class of ant colony optmization algorithms. IEEE Transactions on Evolutionary Computation,2002,16 (4): 358-365
[228]刘若辰,杜海峰,焦李成.一种免疫单克隆策略算法.电子学报,2004,32(11)：1880-1884
[229]段海滨,马冠军,王道波,等.一种求解连续空间优化问题的改进蚁群算法.系统仿真学报.2007,19(5)：974-977
[230]陈烨.用于连续函数优化的蚁群算法.四川大学学报(工程科学版),2004,36(6)：117-120
[231]李守巨,刘迎曦,孙慧玲.基于蚁群算法的含水层参数识别方法.岩石力学,2005,26(7)：1049-1052
[232]Macready W G, Wolpert D H. The no free lunch Theorem for optimizations. IEEE Transaction on Evolutionary Computation,1997,1 (1):67-82
[233]王攀.科技创新中的方法集成及其范例——软计算方法集成.中国软科学,2007,(1)： 139-143
[234]王凌.智能优化算法及其应用.北京：清华大学出版社,2001.130-142
[235]韩冰.基于智能软计算的视频镜头分割算法研究：[博士学位论文].西安：西安电子科技大学,2006
[236]刘建成,顾永宁.船桥碰撞力学问题研究现状及非线性有限元仿真.船舶工程,2002,(5)：4-9
[237]黄丹.船舶碰撞桥梁概率公式分析.公路交通技术,2007,5：63-65
[238]Minorsky V U. An Analysis of Ship Collisions with Reference to Protection of Nuclear Power Plants. Journal of Ship Research,1959,3 (1):1-4
[239]Derucher K N. Bridge pile damage upon vessel impact. Computers and Structures,1984,18 (5):931-935
[240]Pedersen P T, Valsgard S, Olsen D, et al. Ship Impacts:Bow Collisions. International Journal of Impact Engineering,1993,13 (2):163-187
[241]梁文娟,金允龙,陈高增.船舶与桥墩碰撞力计算及桥墩防撞.第十四届全国桥梁学术会议论文集,同济大学出版社,2000.566-571.
[242]Wang G, Ohtsubo H, Arita K. Inner dynamics of side collision to bridge pipers. Ship Collision Analysis. Procedings of the International Symposium on Advances in Ship Collision Analysis,1998,53-60
[243]Reckling K A. Mechanics of minor ship collisions. International Journal of Impact Engineering,1983,1 (3):281-299
[244]杨渡军.桥梁的防撞保护系统及其设计.北京：人民交通出版社,1990.98-111
[245]赵劲松.船—桥碰撞与南京长江大桥的防碰问题.大连海运学院学报,1992,18(1)：77-81
[246]《南京长江大桥抗振加固技术》编委会.南京长江大桥抗振加固技术.北京：中国铁道出版社,1998.23-30
[247]王民族,康宏远.受船舶撞击的桥墩桩基设计方法.中国市政工程,1996(2)：54-61
[248]刘斌,姚永丁,叶贵如.斜拉桥传感器优化布点的研究.工程力学,2005,22(5)：171-176
[249]何浩祥,闰维明,张爱林.面向结构健康监测的传感器数量及位置优化研究.振
动与冲击,2008,27(9)：131-134,188
[250]Carne T G, Dohmann C R. A modal test design strategy for modal correlation. In: Proceedings of the 13th International Modal Analysis Conference. New York:Union College, Schenectady,1995:927-933
[251]马向东.桥梁结构健康监测系统传感器优化布置：[硕士学位论文].成都：西南交通大学,2007
[252]田东平,迟洪钦.混合遗传算法与模拟退火法.计算机工程与应用,2006,(22)：63-65
[253]谢晓锋,张文俊,杨之廉.微粒群算法综述.2003,3：129-134
[254]段晓东,王存睿,刘向东.粒子群算法及其应用.辽宁大学出版社.沈阳,2007：31
[255]王凌.智能优化算法及其应用.清华大学出版社.北京2001,35
[256]郭彤.大跨度悬索桥状态评估实用方法研究与应用：[博士学位论文].南京：东南大学,2005
[257]高颖楠.基于广义距离的大型桥梁状态评估研究：[硕士学位论文].长沙：中南大学,2008
[258]袁颖.桥梁结构损伤识别方法的相关问题研究：[博士学位论文].大连：大连理工大学,2005
[259]谢峻.基于振动的桥梁结构损伤识别方法研究.[博士学位论文].广州：华南理工大学,2003
[260]孙鸿敏,李宏男.土木工程结构健康监测研究进展.防灾减灾工程学报,2003,9,23(3)：92-98
[261]邵君.基于MP的信号稀疏分解算法研究：[硕士学位论文].成都：西南交通大学,2006
[262]马世伟.非平稳信号的参数自适应时频表示及其应用的研究：[博士学位论文].上海：上海大学,2000
[263]Yin Q Y, Qian S E, Feng A G. A Fast Refinement for Adaptive Gaussian Chirplet Decomposition. IEEE Transactions on signal processing, 2002, (50)6:1298-1306
[264]Chen S, Donoho D L, Saunders M, Atomic decomposition by basis pursuit. SI AM Journal on Scientific Computing,1998,20 (1):33-61
[265]尹忠科,王建英,邵君.基于原子库结构特性的信号稀疏分解.西南交通大学学报,2005,40,(2)：173-178
[266]何旭辉, 陈政清, 黄方林,等.南京长江大桥动力特性研究.桥梁建设.2003,4：23-29
[2]王柏生,倪一清,高赞明.青马大桥桥板结构损伤位置识别的数值模拟.土木工程学报,2001,34(3)：67-73
[3]李爱群,缪长青,李兆霞,等.润扬长江大桥结构健康监测系统研究.东南大学学报,2003,33(5)：544-548
[4]谢强,薛松涛.土木工程结构健康监测的研究状况与进展.中国科学基,2001,(5)：285-288
[5]Myroll F, Dibiagio E. Instrumentation for monitoring the Skarnsunder Cable-stayed Bridge. In:Jon Krodeborg Balkema, eds. Proceedings of the Third Symposium on Strait Crossing. Rotterdam:Symposium on Strait Crossing,1994.207-215
[6]Andersen E Y. Structural monitoring of the Great Belt East Bridge. In:Jon Krodeborg Balkema, eds. Proceedings of the Third Symposium on Strait Crossing. Rotterdam: Symposium on Strait Crossing,1994.54-62
[7]Curran P, Tilly G. Design and monitoring of the Flintshire Bridge. Structural Engineering International,1999,3:225-228
[8]邬晓光,徐祖恩.大型桥梁健康监测动态及发展趋势.长安大学学报(自然科学版),2003,23(1)：39-42
[9]Chueng M S, Tadros G S, Brown T G, et al. Field monitoring and research on performance of the Confederation Bridge. Canadian Journal of Civil Engineering, 1997,24 (6):951-962
[10]史家钧,兰海,郭志明.桥梁健康监测中的若干问题.见：中日结构减振及健康监测研讨会暨第三届中国结构抗振控制年会论文集.上海：同济大学出版社,2002.1-15
[11]Lau C K, Wong K Y, Ho K S. The Wind and Structural Health Monitoring System (WASHMS) for the three long-span cable-supported bridges in Hongkong. In: Proceedings of the fifteenth International Association for Bridge and Structural Engineering (IABSE). Copenhagen:IABSE,1996
[12]刘正光,麦惠培,黄启远,等.青马大桥的初步监测结果.见：中国土木工程学会桥梁及结构工程学会第十三届年会论文集.上海：同济大学出版社,1998. 730-740
[13]史家钧,邵志常.上海徐浦大桥结构状态监测系统.见：中国土木工程学会桥梁及结构工程学会第十三届年会论文集.上海：同济大学出版社,1998.779-784
[14]欧庆保,周世忠.江阴长江公路大桥上部结构的安全监测系统.见：龚晓南,编.中国土木工程学会第九届年会论文集——工程安全及耐久性.北京：中国水利水电出版社,2000.202-206
[15]Huang F L, He X H, Chen Z Q, et al. Structural safety monitoring for Nanjing Yangtze River Bridge. Journal of Central South University of Technology,2004,11 (3):332-335
[16]曾储惠.南京长江大桥健康监测和状态评估的研究：[硕士学位论文].长沙：中南大学,2004
[17]杨孚衡.芜湖长江大桥斜拉桥长期监测、安全评估及报警系统的研制.见：康熊,钱立新,编.铁道科学技术新进展——铁道科学研究院五十五周年论文集.北京：中国铁道出版社,2005.82-92
[18]苏木标,杜彦良,孙宝臣,等.芜湖长江大桥长期健康监测与报警系统研究.铁道学报,2007,29(2)：71-76
[19]Sun B N, Wu X P, Lou W J, et al. The real-time health monitoring system of Qianjiang 4th bridge. In:Chen Y M, eds. Proceedings of the International Seminar on Environmental Vibration:Prediction, Monitoring and Evaluation. Beijing:China Communication Press,2003.420-426
[20]郭健,陈勇,孙炳楠,等.钱江四桥健康监测系统中的关键性问题及损伤识别方法研究.见：中国土木工程学会桥梁及结构工程分会,编.第十七界全国桥梁学术会议论文集.北京：人民交通出版社,2006.671-679
[21]张启伟.大型桥梁健康监测概念与监测系统设计.同济大学学报,2001,29(1)：65-69
[22]郭健,顾正维,孙炳楠,等.基于小波分析的桥梁健康监测方法.工程力学,2006,23(12)：129-135
[23]郭嗣踪,陈刚.信息科学中的软计算方法.沈阳： 东北大学出版社,序5-7
[24]Zadeh LA. Soft Computing and Fuzzy Logic. IEEE Transactions on Software,1994, 11 (6):48-56
[25]Zadeh L A. Fuzzy logic, neural networks and soft computing. Communication of the ACM.1994,37 (3):77-84
[26]蔡自兴.人工智能及其应用(第三版).北京： 清华大学出版社,2004.8
[27]詹海刚.基于软计算的海洋水色遥感反演：[博士学位论文].青岛：青岛海洋大学,2001
[28]Yu Y, YuDJ, Cheng J S. A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration,2006,294:267-277
[29]Marcu T, Seliger B K, Stucher R. Design of fault detection for a hydraulic looper using dynamic neural networks. Control Engineering Practice,2008,16:192-213
[30]Guo Q J, Yu H B, Xu A D. A hybrid PSO-GD based intelligent method for machine diagnosis. Digital Signal Processing,2006,16:402-418
[31]Taplak H, Uzmay I, Yildirim S. An artificial neural network application to fault detection of a rotor bearing system. Industrial Lubrication and Tribology,2006,58 (1):32-44.
[32]Djarfour N, Aifa T, Baddari K, et al.Application of feedback connection artificial neural network to seismic data filtering. C. R. Geoscience,2008,340:335-344
[33]Koh C G, Chen Y F, Liaw C Y. A hybrid computational strategy for identification of structural parameters. Computers and Structures,2003,81:107-117
[34]Li L L, Wang L, Liu L H. An effective hybrid PSOSA strategy for optimization and its application to parameter estimation. Applied Mathematics and Computation, 2006,179:135-146
[35]Jaszkiewicz A. Genetic local search for multi-objective combinatorial optimization. European Journal of Operatioanal Research,2002,137:50-71
[36]Hossen A, Al-Wadahi F, Jervase J A. Classification of modulation signals using statistical signal characterization and artificial neural networks. Engineering Applications of Artificial Intelligence,2007,20:463-472
[37]Boryczka U. Finding groups in data:Cluster analysis with ants. Applied Soft Computing,2009,9:61-70
[38]Kuo R J, Lin S Y, Shih C W. Mining association rules through integration of clustering analysis and ant colony system for health insurance database in Taiwan. Expert Systems with Applications,2007,33:794-808
[39]Martin J D, Morton Y T, Zhou Q H. Neural network development for the forecasting of upper atmosphere parameter distributions. Space Weather Advances in Space Research,2005,36 (12):2480-2485.
[40]郄志红.大坝安全监测资料正反分析的智能软计算方法及其应用：[博士学位论文].天津：天津大学,2005
[41]王光远.工程软设计理论.北京：科学出版社,1992.1-8
[42]王光远.工程结构系统软设计理论及应用.北京：国防工业出版社,1996.1-3
[43]中国科学技术培训中心.迎接交叉科学的时代.北京：光明日报社,1986.6
[44]阎石.结构振动智能控制的人工神经网络与模糊逻辑方法研究：[博士学位论文]. 大连：大连理工大学,2000
[45]刘建军.新型磁流变阻尼器及其智控制方法研究：[博士学位论文].天津：天津大学,2008
[46]周东.基坑支护工程遗传优化设计研究：[博士学位论文].广西：广西大学,2002
[47]易伟建,刘霞.遗传演化结构优化算法.工程力学,2004,21(3)：66-71
[48]淡丹辉.智能土木(桥梁)结构理论及其核心算法研究.[博士学位论文].成都：西南交通大学,2002
[49]Jeyasehar C A, Sumangala K. Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach. Computers and Structures,2006, 84:1709-1718
[50]Sahoo B, Maity D. Damage assessment of structures using hybrid neuro-genetic algorithm. Applied Soft Computing,2007,7:89-104
[51]Li Z X, Yang X M. Damage identification for beams using ANN based on statistical property of structural responses. Computers and Structures,2008,86:64-71
[52]孙宗光,高赞明,倪一清.基于神经网络的桥梁损伤位置识别.工程力学,2004,21(1)：42-47
[53]李国强,李杰.工程结构动力检测理论与应用.北京：科学出版社,2001.113-132
[54]Friswell M I, Penny J E T, Garvey S D. A combined genetic and eigensensitivity algorithm for the location of damage in structures. Computers and Structures,1998, 69:547-556
[55]张育智,李乔,单德山.SOFM网络在结构损伤位置识别中的应用.振动与冲击,2007,26(2)：160-163
[56]张育智.基于神经网络与数据融合的结构损伤识别理论研究：[博士学位论文].成都：西南交通大学,2007
[57]黄天立.结构系统和损伤识别的若干方法研究：[博士学位论文].上海：同济大学,2007
[58]吴大宏.基于遗传算法与神经网络的桥梁结构健康监测系统研究：[博士学位论文].成都：西南交通大学,2004
[59]李守巨.基于计算智能的岩土力学模型参数反演方法及其工程应用：[博士学位论文].大连：大连理工大学,2004
[60]杨成祥.材料非线性模型的进化识别方法研究：[博士学位论文].沈阳：东北大学,2001
[61]逯静洲,林皋.人工神经网络技术在混凝土本构模型中的应用.土木工程学报,2003,36(4)：38-42
[62]苏怀智,李季,吴中如.大坝及岩基物理力学参数优化反演分析研究.水利学报. 2007,增刊：129-134
[63]王建.大坝安全监控集成智能专家系统关键技术研究：[博士学位论文].南京：河海大学,2002
[64]何勇军,薛宝林.土石坝渗流的神经网络监控模型.河海大学学报(自然科学版),2001,29(2)：79-82
[65]林铁良,王君杰,陈艾荣,等.基于事故记录的船撞桥故障树建立.同济大学学报(自然科学版),2006,34(4)：467-471
[66]菜键,汪克来.桥梁受船舶碰撞的思考.安徽建筑工业学院学报(自然科学版)2008,16(1)：1-3,26
[67]Sipke E M, Aksel G F. Ship collision with bridges, review of accidents. Ship Collision Analysis. In:Gluver, Olsen eds. Ship collision analysis:proceedings of the International Symposium on advances in ship collision analysis. Rotterdam: Balkema,1998.3-11.
[68]邵旭东,占雪芳,廖朝华,等.从美国阳光大道桥被撞重建看现有桥梁防撞风险评估.公路,2007,8：33-37
[69]戴彤宇,聂武,刘伟力.长江干线船撞桥事故分析.中国航海,2002,4：44-47
[70]戴彤宇.船撞桥及其风险分析：[博士学位论文].哈尔滨：哈尔滨工业大学,2002
[71]杜旭升.6-15九江大桥船撞事故引发的思考.中国海事,2007,9：33-35
[72]项海帆,范立础,王君杰.船撞桥设计理论的现状与需进一步研究的问题.同济大学学报,2002,30(4)：386-392
[73]Yun H, Nayeri R, Tasbihgoo F, et al. Monitoring the collision of a cargo ship with the Vincent Thomas Bridge. Structral Control and Health Monitoring,2008,15 (2): 183-206
[74]何旭辉.南京长江大桥结构健康监测及其关键技术研究：[博士学位论文].长沙：中南大学,2004
[75]耿波,王君杰,汪宏.桥梁船撞风险评估系统总体研究.土木工程学报,2007,40(5)：34-40
[76]刘小虎,杨文兵,王乘.大桥受船撞击灾害的计算机评价方法.工程力学,2000,17(8)：124-131
[77]魏培军,章梓茂.弹性动力学反问题的不适定性及其广义解,力学与实践,2001,23(3)：43-45
[78]邹振祝,冯文杰.弹性动力学反问题研究概况.石家庄铁道学院学报,2000,13(3),7-12
[79]杨慧珠,张高远,鲁小荣.动力学的反问题.固体力学发展趋势,北京： 北京理工大学出版社,1995.55-73
[80]智浩,文祥荣,缪龙秀,等.动态载荷的频域识别方法,北方交通大学学报,2000,24(4)：5-10
[81]唐秀近.动态力识别的时域方法.大连工学院学报,1987,26(4)：21-27
[82]Law S S, Chan T H T, Zeng Q H. Moving force identification:A time domain method, Journal of Sound and Vibration,1997,201 (1):1-22
[83]魏星原,宋斌,郑效忠.荷载识别的逆系统方法.振动、测试与诊断,1995,15(3)：35-43
[84]Kumpatis, Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks,1990,1 (1):4-27
[85]Cook A B, Fuller C R,0 Brien W F, et al. Artificial Networks in Detection for Predicting Nonlinear Dynamic Helicopter Loads, AIAA Journal,1994,32 (5), 2337-2344
[86]张方,朱德懋.基于神经网络模型的动荷载识别.振动工程学报,1997,10(2)：156-162
[87]徐宜桂,马西庚,史铁林,等.基于神经网络的动力学反解算法及其应用研究.机械工程学报,1998,34(4)：106-110
[88]瞿伟廉,王锦文.振动结构动态荷载识别综述.华中科技大学学报(城市科学版),2004,21(4)：1-4,8
[89]Cao X, Sugiyama Y, Mitsui Y. Application of artificial neural networks to load identification. Computers and Structures,1998,69:63-78
[90]金虎,楼文娟,陈勇.基于自适应BP神经网络的桥梁结构荷载识别.浙江大学学报(工学版),2005,39(10)：1596-1602
[91]吴大宏,赵人达.基于神经网络的混凝土桥梁荷载识别方法研究.中国铁道科学,2002,23(1)：25-28
[92]董会丽,郑世杰.基于RBF神经网络的复合材料层合壳荷载识别.工程力学,2008,25(3)：64-67
[93]李忠献,陈锋,王波.基于BP神经网络的桥上移动荷载分阶段识别方法.工程力学,2009,25(9)：85-92
[94]秦权.桥梁结构的健康监测.中国公路学报,2000,13(2)：37-42
[95]韩大建,谢峻.大跨度桥梁健康监测技术的近期研究进展.桥梁建设,2002,(6)：69-73.
[96]沈雁彬.基于动力特性的空间网格结构状态评估方法及检测系统研究：[博士学位论文].杭州：浙江大学,2007
[97]赵翔,李爱群,韩晓林.润扬大桥结构健康监测系统传感器测点布置.工业建筑,2005,35(1)：82-85
[98]李宏男,李东升.土木工程结构安全性评估、健康监测及诊断述评.地震工程与工程振动,2002,22(3)：82-90
[99]崔飞,袁万城,史家钧.传感器优化布设在桥梁健康监测中的应用.同济大学学报,1999,27(2)：165-169
[100]何浩祥.空间结构健康监测的理论与试验研究：[博士学位论文].北京：北京工业大学,2006
[101]Carne T G, Dohrmann C R. A modal test design strategy for modal correlation. In: Bathe K J eds. Proc 13th Intl Modal Analysis Conference. New York:Union College, Schenectady,1995.927-933
[102]Kammer D C, Tinker M L. Optimal placement of triaxial accelerometers for modal vibration tests. Mechanical Systems and Signal Processing,2004, (18):29-41
[103]Heo G, Wang HL, Satpathi D. Optimal transducer placement for health monitoring of long span bridge. Soil Dynamics and Earthquake Engineering,1997,16 (7-8): 495-502
[104]谢强, 薛松涛.结构健康监测传感器优化布置的混合算法.同济大学学报(自然科学版),2006,34(6)：726-731
[105]李戈, 秦权, 董聪.用遗传算法选择悬索桥监测系统中传感器的最优布点.工程力学,2000,17(1)：24-34
[106]Meo M, Zumpano G On the optimal sensor placement techniques for a bridge structure. Engineering Structures,2005,27 (10):1488-1497
[107]Z.N. Li, J. Tang, Q.S. Li. Optimal sensor locations for structural vibration measurements. Applied Acoustics 65 (2004) 807-818
[108]Heo G, Wang HL, Satpathi D. Optimal Transducer Placement for Health Monitoring of Long Span Bridge. Soil Dynamics and Earthquake Engineering,1997,16(7-8): 495-502
[109]刘斌,姚永丁,叶贵如.斜拉桥传感器优化布点的研究.工程力学,2005,22(5)：171-176
[110]魏民祥,闫桂荣,郭万林.基于信息嫡原理的结构振动传感器的布局.仪器仪表学报.2004,25(6)：802-804
[111]Papadimitriou C. Pareto optimal sensor locations for structural identification. Comput. Methods Appl. Mech. Engrg.2005,194:1655-1673
[112]张连振,黄侨,王潮海.基于多目标遗传算法的传感器优化布点研究.工程力学,2007,24(4)：168-172
[113]Sepulveda A E, schmit J L A. Optimal palcement of active elements in control augmented structural systhesis. AIAA Journal,1993,31 (10):1906-1915
[114]刘娟,黄维平.模态试验中传感器优化配置的逐步削减法.海洋工程,2004,122(11)：32-36
[115]刘娟,黄维平.传感器优化配置的修正逐步累积法.青岛海洋大学学报,2003,33(3)：476-482
[116]Hafkta R T, Adelman H M. Selection of actuator locations for static shape control of large space structures by heuristic integer programming. Computers and structures, 1985,20 (1-3):3506-3514
[117]黄维平,刘娟,李华军.基于遗传算法的传感器优化配置.工程力学,2005,22(1)：113-117
[118]李亚东.既有桥梁评估初探.桥梁建设,1997,(3)：18-21
[119]范剑锋.桥梁健康状态的智评估方法研究：[博士学位论文].武汉：武汉理工大学,2006
[120]兰海,史家钧.灰色关联分析与变权综合法在桥梁评估中的应用.同济大学学报,2001,29(1)：50-54
[121]项贻强,伍华成,翁沙羚.大跨度预应力混凝土斜拉桥的状态评估体系研究.中外公路,2005,25(5)：73-76
[122]任远,黄乔,林阳子.大跨度斜拉桥综合评估系统的研制与开发.南京航空航天大学学报,2007,39(4)：535-539
[123]刘沐宇,卢傲.基于GIS的钢管混凝土拱桥安全性评价系统.武汉理工大学学报(交通科学与工程版),2007,31(3)：442-445
[124]Guo T, Li A Q, Zhao D L, et al. Synthetical condition assesament of long span suspension bridge based on cbseness degree and FAHP. Journal of Southeast University (English Edition),2007,23 (2):261-265
[125]Huang Q, Lin Y Z, Ren Y. Comprehensive evaluation method for superstructures of prestressed concrete girder bridges. Journal of Southeast University (English Edition),2008,12 (1):64-68
[126]杨则英,曲建波,黄承逵.基于模糊综合评判和层次分析法的桥梁安全性评估.天津大学学报,2005,38(12)：1063-1067
[127]Wang Y M, Liu J, Taha M S E. An integrated AHP-DEA methodology for bridge risk assessment, Computers and Industrial Engineering,2008,54 (3):513-525
[128]Tee A B, Bowman M D, Sinha K C. A fuzzy mathematical approach for bridge condition evaluation, Journal of Civil Engineering systems,1988,5 (1):17-25
[129]淡丹辉,孙利民.Mamdani型模糊推理系统在桥梁状态评估中的应用.同济大学学报(自然科学版),2004,32(9)：1131-1135
[130]王根会,朱尚清.基于模糊系统的铁路钢板梁桥加固效果评估分析.铁道学报, 2007,29(3)：123-126
[131]郭琦,贺拴海,白云.基于神经网络的简支梁桥预应力衰减评估模型.长安大学学报(自然科学版),2007,27(6)：53-57
[132]范剑锋,袁海庆,彭自强.基于模型智能修正的桥梁健康状态评估.武汉理工大学学报,2008,30(4)：84-87
[133]韩大建,杨炳尧,颜全胜.用人工神经网络方法评估桥梁缺损状况.华南理工大学学报(自然科学版),2004,32(9)：72-75,96
[134]姚敏,张森.模糊一致矩阵及其在软科学中的应用.系统工程,1997,15(2)：54-57
[135]姚敏,黄燕君.模糊一致关系及其应用.电子科技大学学报,1997,26(6)：632-635
[136]姚敏,张森.模糊一致矩阵及其在决策分析中的应用.系统工程理论与实践,1998,(5)：78-81
[137]张吉军.模糊层次分析法(FAHP).模糊系统与数学,2000,14(2)：80-88
[138]吕跃进.基于模糊一致矩阵的模糊层次分析法的排序.模糊系统与数学,2002,16 (2)：79-85
[139]张吉军.模糊一致判断矩阵3种排序方法的比较研究.2003,25(11)：1370-1372
[140]徐泽水.AHP中两类标度的关系研究.系统工程理论与实践,1999,(7)：97-101
[141]冯向前,魏翠萍,李宗植,等.三角模糊数互反判断矩阵的一致性及排序研究.统计与决策,2007,(7)：27-28
[142]徐泽水.三角模糊数互补判断矩阵的一种排序方法.模糊系统与数学,2002,16(1)：47-50
[143]龚艳冰,陈森发.三角模糊数互补判断矩阵的一种排序方法.模糊系统与数学,2008,22(1)：127-131
[144]徐泽水,达庆利.区间数排序的可能度法及其应用.系统工程学报,2003,18(1)：67-70
[145]徐泽水,刘海峰.六类不确定型判断矩阵的相容性研究.模糊系统与数学,2003,17(2)：53-58
[146]冯向前,魏翠萍,李宗植.区间数互补判断矩阵一致性及其权重算法研究.数学的实践与认识,2007,37(19)：87-93
[147]朱建军,刘思峰,王(?)华.群决策中两类三端点区间数判断矩阵的集结方法.自动化学报,2007,33(3)：297-301
[148]朱建军宋传平,刘思峰等.一类三端点区间数判断矩阵的一致性及权重研究.系统工程学报,2008,23(1)：22-27
[149]Zhou S Z, Liu S F, Zhu J J, etal. Choosing enterprise advertisement medium approach of three-point interval number comparison matrix method. In:Proceedings of 2007 IEEE International Conference on Gray Systems and Intelligence Service. 1412-1417
[150]Lu Z P, Tian F, Zhu J J. Decision-making Approach Based on Novel Linguistic Preference. In:Global Congress on Intelligent Systems, Intelligent Systems,2009. GCIS'09. WRI Global Congress on.343-346
[151]Tian F, Zhu J J, Ma B. Evaluation of Government Energy-Saving Policy Performance Base on Three-Point Interval Gray Number. In:Proceedings of 2007 IEEE International Conference on Gray Systems and Intelligence Service.1314-1316
[152]郭健,顾正维,孙炳楠,等.基于小波分析的桥梁健康监测方法.工程力学,2006,23(12)：129-135
[153]Cooley J W, Tukey J W. An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation,1965,19 (90):297-301
[154]谭为民,彭东林,郭小渝.三种典型信号处理方法浅析.测控技术,2003,22(6)：15-16
[155]张贤达,保铮.非平稳信号分析与处理.北京：国防工业出版社,2001.12-14
[156]Gabor D. Theory of communication. Journal of IEEE,1946,93:429-457
[157]Meyer Y. Wavelets.-algorithms and applications. Philadelphia.-Society for Industrial and Applied Mathematics,1993
[158]Daubechies I. Orthonormal base of compactly supported wavelet. Communication on Pure and Applied Mathematics,1988, XII:909-996
[159]Daubechies I. The wavelet transform, time frequency localization and signal analysis. IEEE Transactions on Information Theory,1990,36 (5):961-1005
[160]Mallat S. A theory for multiresolusion signal decomposition:the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989,25 (11):674-693
[161]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series A,1998,454:903-995
[162]Huang N E, Shen Z, Long S R. A new view of nonlinear water waves:the Hilbert spectrum. Annual Review of Fluid Mechanics,1999,31:417-457
[163]丁康,陈健林,苏向荣.平稳和非平稳振动信号的若干处理方法的研究.振动工程学报,2003.16(1)：1-10
[164]钟佑明,秦树人,汤宝平.Hilbert-Huang变换中的理论研究.振动与冲击,2002,21(4)：13-18
[165]程军圣,于德介,杨宇.基于支持矢量回归机的Hilbert-Huang变换端点效应问题的处理方法.机械工程学报,2006,42(4)：23-31
[166]赵进平.异常事件对EMD方法的影响及其解决方法研究.青岛海洋大学学报,2001,31(6)：805-814
[167]钟佑明,金涛,秦树人.希尔伯特倾髦变换中的一种新包络线算法.数据采集与处理,2005,20(1)：13-17
[168]Classen T C M, Mecklenbrauker W F G. The Wigner Distribution-part I. Philips Journal of Research,1980,35:217-250
[169]Classen T C M, Mecklenbrauker W F G. The Wigner Distribution-part II. Philips Journal of Research,1980,35:276-300
[170]Classen T C M, Mecklenbrauker W F G. The Wigner Distribution-part III. Philips Journal of Research,1980,35:372-389
[171]Mark W D. Spectral analysis of the convolution and filtering of non-stationary stochastic process. Journal of Sound and Vibration,1970,11 (1):19-63
[172]丁康,陈健林,苏向荣.平稳和非平稳振动信号的若干处理方法及发展.振动工程学报,2003,16(1)：1-10
[173]邹红星,周小波,李衍达.时频分析：回溯与前瞻.电子学报,2000,9：78-84
[174]Coifman R R, Wickerhauser M V. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory,1992,38 (2):713-718
[175]Mallat S, Zhang Z. Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing,1993,41 (12):3397-3415
[176]Daubechies I. The wavelet transform, time-frequency localization and signal analysis, IEEE Transactions on Information Theory,1990,36 (5):961-1005
[177]Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Review,2001,43 (1):129-159
[178]Qian S E, Chen D P. Signal representation using adaptive normalized Gaussian functions. Signal Processing,1994,36 (1):1-11
[179]Bultan A. A four-parameter atomic decomposition of chirplets, IEEE Transactions on Signal Processing,1999,47 (3):731-744
[180]Zou H, Chen Y, Zhu J, et al. Steady-motion-based Dopplerlet transform application to the estimation of range and speed of a moving sound source, IEEE Journal of Oceanic Engineering,2004,29 (3):887-905
[181]Feng Z P, Chu F L. Application of atomic decomposition to gear damage detection. Journal of Sound and Vibration,2007,302 (1-2):138-151
[182]Yang H Y, Mathew J, Ma L. Fault diagnosis of rolling element bearings using basis pursuit. Mechanical Systems and Signal Processing,2005,19(2):341-356
[183]孟庆丰, 范虹, 王祺.匹配追踪信号分解与往复机械故障特征提取技术研究.西安交通大学报,2001,35(7)：696-699
[184]朱明,金炜东,普运伟.基于Chirplet原子的雷达辐射源信号特征提取.红外与毫米波学报,2007,26(4)：302-306
[185]徐鹏.信号的稀疏分解及其在脑电信号处理中的应用研究：[博士学位论文].成都：电子科技大学,2006
[186]姜玉亭.基于稀疏分解的图像去噪.西南交通大学硕士学位.成都：西南交通大学.2005
[187]张跃飞.基于稀疏分解的图像压缩.西南交通大学硕士学位.成都：西南交通大学.2006
[188]Spanos P D, Giaralis A, Politis N P. Time-frequency representation of earthquake accelerograms and inelastic structural response records using the adaptive chirplet decomposition and empirical mode decomposition. Soil Dynamics and Earthquake Engineering,2007,27 (7):675-689
[189]Wang Y. Seismic time-frequency spectral decomposition by matching pursuit. Geophysics,2007,13 (72):13-20
[190]Natarajan B K. Sparse approximate solutions to linear systems. SIAM Journal on Computing,1995,24 (2):227-234
[191]Davis G M, Mallat S, Avellaneda M. Greedy adaptive approximations. Constructive approximation.1997,13 (1):57-98
[192]Adelino R, Silva F D. Atomic decomposition with evolutionary pursuit. Digital Signal Processing,2003,13 (2):317-337
[193]高瑞,徐华楠,胡钢.基于GA和过完备原子库划分的MP信号稀疏分解算法.科学技术与工程,2008,8(4)：914-916,920
[194]尹忠科,王建英,Vandergheynst P.基于GA和原子特性的信号稀疏分解.铁道学报,2005,27(3)：58-61
[195]徐鹏,尧德中.一种引入退火降温阈值函数约束的改进MP稀疏分解方法.电子与信息学报,2007,29(7)：1645-1648
[196]Aarts E, Van H K. et al. A unifying approach on heuristic search. Annals of Opearation Research,1995,55:81-99
[197]王凌,刘波.微粒群优化与调度算法.北京：清华大学出版社,2008.15-33
[198]McCulloch W S, Pitts W H. A logical calculus of the ideas immanent in nervous activity. Bulletin Mathematical Biophysics,1943, (5):115-133
[199]Hebb D O. The organization of behavior:A neuro psychological theory. New York: Wiley,1949.1-12
[200]Rosenblatt F. Principles of neurodynamics:Perceptrons and the theory of brain mechanisms. New York:Spartan,1962.3-20
[201]周明,孙树栋.遗传算法原理及应用.北京：国防工业出版社,1999.59
[202]Kennedy J, Eberhart R. Particle swarm optimization.In: Proceedings of IEEE International Conference on Neural Networks. New York:IEEE,1995.1942-1948
[203]Gallad A E, Hawary M E, Sallaam A, et al. Enhancing the particle swarm optimizer via proper parameters selection. In:Proceedings of the 2002 IEEE Canadian Conference on Electrical and Engineering. New York:IEEE,2002.792-797
[204]Shi Y, Eberhart R. A modified particle swarm optimizer. In:Proceedings of IEEE International Conference on Evolutionary Computation. New York:IEEE,1998. 69-73
[205]Clerk M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trasactions on Evolutionary Computation. 2002,6 (1):58-73
[206]Fan H Y, Shi Y H. Study of vmax of the particle swarm optimization algorithom. Purdue School of Engineering and Technology. INPUI 2001
[207]彭宇,彭喜元,刘兆庆.微粒群算法参数效能的统计分析.电子学报,2004,32(2)：209-213.
[208]Eberhert R C, Shi Y H. Comparing inertia weights and constriction factors in particle swarm optimization. In:Proceedings of IEEE International Congress on Evolutionary Computation. New York:IEEE,2000.84-88
[209]Eberhert R C, Shi Y H. A modified particle swarm optimizer. In:Proceedings of the IEEE International Conference on Evolutionary Computation. New York:IEEE,1998. 69-73
[210]Eberhert R C, Shi Y H. Empirical study of particle swarm optimization. In: Proceedings of the Congress on Evolutionary Computation. New York:IEEE,1999. 1954-1950
[211]Suganthan P N. Particle swarm optimizer with neighbourhood operator. In: Proceedings of the Congress on Evolutionary Computation.1958-1962
[212]Colorni A, Dorigo M, Maniezzo V, et al. Distributed optimization by ant colonies. In:Prooeedings of European Conference on artificial Life. Paris:Elsevier,1991. 134-142
[213]Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents. IEEE Transaction on Sytems, Man, and Cybernetics-Part B, 1996,26 (1):29-41
[214]Bonabeau E, Dorigo M, Theraulaz G. Inspiration for Optimization from Social Insect Behavior. Nature,2000,406 (6):39-42
[215]杨剑峰.蚁群算法原理及应用：[博士学位论文].杭州：浙江大学,2007
[216]陈慰峰.医学免疫学.北京：人民卫生出版社,2001.1-3
[217]林学颜,张玲.现代细胞与分子免疫学.北京：科学出版社,2000.3-5
[218]Jerne N K. Towards a Network Theory of the Immune System. Annual Immunology, 1974,125 (1-2):373-389
[219]Farmer J D, Kauffman S A, Packard N H, et al. Adaptive dynamic networks as models for the immune system and autocatalytic sets. Annals of the New York Academy of Sciences,1987,504 (1):118-131
[220]孙宁.人工免疫优化算法及其应用研究：[博士学位论文].哈尔滨：哈尔滨工业大学,2006
[221]Goldberg D E, Segrest P. Finite Markov chain analysis of genetic algorithms. In: Grefenstette J J, eds. Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application. Hillsdale,NJ,USA:Erlbaum Associates,1987.1-8
[222]Rudolph G. Convergence analysis of canonical genetic algorithms. IEEE transactions on Neural Networks,1994,5 (1):96-101
[223]雷开友.粒子群算法及其应用研究：[博士学位论文].重庆：西南大学,2006
[224]王俊年.微粒群算法及其在锌电解整流供电系统优化中的应用研究：[博士学位论文].长沙：中南大学,2006
[225]Gutjahr W J. A graph-based ant system and its convergence. Future generation computer systems,2000,16 (9):873-888
[226]Gutjahr W J. ACO algorithms with guaranteed convergence to the optimal solution. Information Processing Letters,2002,82 (3):145-153
[227]Stiltzle T, Dorigo M. A short convergence proof for a class of ant colony optmization algorithms. IEEE Transactions on Evolutionary Computation,2002,16 (4): 358-365
[228]刘若辰,杜海峰,焦李成.一种免疫单克隆策略算法.电子学报,2004,32(11)：1880-1884
[229]段海滨,马冠军,王道波,等.一种求解连续空间优化问题的改进蚁群算法.系统仿真学报.2007,19(5)：974-977
[230]陈烨.用于连续函数优化的蚁群算法.四川大学学报(工程科学版),2004,36(6)：117-120
[231]李守巨,刘迎曦,孙慧玲.基于蚁群算法的含水层参数识别方法.岩石力学,2005,26(7)：1049-1052
[232]Macready W G, Wolpert D H. The no free lunch Theorem for optimizations. IEEE Transaction on Evolutionary Computation,1997,1 (1):67-82
[233]王攀.科技创新中的方法集成及其范例——软计算方法集成.中国软科学,2007,(1)： 139-143
[234]王凌.智能优化算法及其应用.北京：清华大学出版社,2001.130-142
[235]韩冰.基于智能软计算的视频镜头分割算法研究：[博士学位论文].西安：西安电子科技大学,2006
[236]刘建成,顾永宁.船桥碰撞力学问题研究现状及非线性有限元仿真.船舶工程,2002,(5)：4-9
[237]黄丹.船舶碰撞桥梁概率公式分析.公路交通技术,2007,5：63-65
[238]Minorsky V U. An Analysis of Ship Collisions with Reference to Protection of Nuclear Power Plants. Journal of Ship Research,1959,3 (1):1-4
[239]Derucher K N. Bridge pile damage upon vessel impact. Computers and Structures,1984,18 (5):931-935
[240]Pedersen P T, Valsgard S, Olsen D, et al. Ship Impacts:Bow Collisions. International Journal of Impact Engineering,1993,13 (2):163-187
[241]梁文娟,金允龙,陈高增.船舶与桥墩碰撞力计算及桥墩防撞.第十四届全国桥梁学术会议论文集,同济大学出版社,2000.566-571.
[242]Wang G, Ohtsubo H, Arita K. Inner dynamics of side collision to bridge pipers. Ship Collision Analysis. Procedings of the International Symposium on Advances in Ship Collision Analysis,1998,53-60
[243]Reckling K A. Mechanics of minor ship collisions. International Journal of Impact Engineering,1983,1 (3):281-299
[244]杨渡军.桥梁的防撞保护系统及其设计.北京：人民交通出版社,1990.98-111
[245]赵劲松.船—桥碰撞与南京长江大桥的防碰问题.大连海运学院学报,1992,18(1)：77-81
[246]《南京长江大桥抗振加固技术》编委会.南京长江大桥抗振加固技术.北京：中国铁道出版社,1998.23-30
[247]王民族,康宏远.受船舶撞击的桥墩桩基设计方法.中国市政工程,1996(2)：54-61
[248]刘斌,姚永丁,叶贵如.斜拉桥传感器优化布点的研究.工程力学,2005,22(5)：171-176
[249]何浩祥,闰维明,张爱林.面向结构健康监测的传感器数量及位置优化研究.振
动与冲击,2008,27(9)：131-134,188
[250]Carne T G, Dohmann C R. A modal test design strategy for modal correlation. In: Proceedings of the 13th International Modal Analysis Conference. New York:Union College, Schenectady,1995:927-933
[251]马向东.桥梁结构健康监测系统传感器优化布置：[硕士学位论文].成都：西南交通大学,2007
[252]田东平,迟洪钦.混合遗传算法与模拟退火法.计算机工程与应用,2006,(22)：63-65
[253]谢晓锋,张文俊,杨之廉.微粒群算法综述.2003,3：129-134
[254]段晓东,王存睿,刘向东.粒子群算法及其应用.辽宁大学出版社.沈阳,2007：31
[255]王凌.智能优化算法及其应用.清华大学出版社.北京2001,35
[256]郭彤.大跨度悬索桥状态评估实用方法研究与应用：[博士学位论文].南京：东南大学,2005
[257]高颖楠.基于广义距离的大型桥梁状态评估研究：[硕士学位论文].长沙：中南大学,2008
[258]袁颖.桥梁结构损伤识别方法的相关问题研究：[博士学位论文].大连：大连理工大学,2005
[259]谢峻.基于振动的桥梁结构损伤识别方法研究.[博士学位论文].广州：华南理工大学,2003
[260]孙鸿敏,李宏男.土木工程结构健康监测研究进展.防灾减灾工程学报,2003,9,23(3)：92-98
[261]邵君.基于MP的信号稀疏分解算法研究：[硕士学位论文].成都：西南交通大学,2006
[262]马世伟.非平稳信号的参数自适应时频表示及其应用的研究：[博士学位论文].上海：上海大学,2000
[263]Yin Q Y, Qian S E, Feng A G. A Fast Refinement for Adaptive Gaussian Chirplet Decomposition. IEEE Transactions on signal processing, 2002, (50)6:1298-1306
[264]Chen S, Donoho D L, Saunders M, Atomic decomposition by basis pursuit. SI AM Journal on Scientific Computing,1998,20 (1):33-61
[265]尹忠科,王建英,邵君.基于原子库结构特性的信号稀疏分解.西南交通大学学报,2005,40,(2)：173-178
[266]何旭辉, 陈政清, 黄方林,等.南京长江大桥动力特性研究.桥梁建设.2003,4：23-29