帝國主義競爭算法的改進(jìn)及其在結(jié)構(gòu)識(shí)別中的應(yīng)用
發(fā)布時(shí)間：2018-09-04 14:21
【摘要】：在役工程結(jié)構(gòu)在惡劣自然環(huán)境、超負(fù)荷運(yùn)營以及材料疲勞老化等多重因素影響下存在嚴(yán)重的安全隱患。傳統(tǒng)的無損檢測(cè)和可靠性評(píng)估方法需要知道結(jié)構(gòu)缺陷的大致位置,且無法探測(cè)到結(jié)構(gòu)內(nèi)部缺陷,已經(jīng)不能滿足實(shí)用要求。為了彌補(bǔ)以上缺陷,近些年來該領(lǐng)域的學(xué)者們開展了諸多結(jié)構(gòu)健康監(jiān)測(cè)系統(tǒng)的研究。結(jié)構(gòu)識(shí)別(結(jié)構(gòu)參數(shù)識(shí)別和損傷識(shí)別)作為結(jié)構(gòu)健康監(jiān)測(cè)的核心內(nèi)容,其識(shí)別方法繁多,但至今為止仍沒有一個(gè)適用于實(shí)際工程的有效方法。本文對(duì)一種群體智能算法——帝國主義競爭算法(Imperialist Competitive Algorithm,ICA)進(jìn)行了研究,針對(duì)算法的局限性進(jìn)行改進(jìn),提出了尋優(yōu)能力更強(qiáng)的改進(jìn)算法,并將其引入到結(jié)構(gòu)模態(tài)參數(shù)識(shí)別和損傷識(shí)別中。主要內(nèi)容和研究及成果如下:(1)系統(tǒng)介紹了結(jié)構(gòu)模態(tài)參數(shù)識(shí)別和損傷識(shí)別的研究現(xiàn)狀、主要方法以及應(yīng)用背景,由此提出了將ICA應(yīng)用于結(jié)構(gòu)識(shí)別的研究思路和主要研究內(nèi)容;(2)詳細(xì)介紹了有關(guān)于IC A的背景、基本原理以及計(jì)算流程等,同時(shí)簡要闡述了ICA的主要應(yīng)用領(lǐng)域;(3)針對(duì)ICA實(shí)際運(yùn)用過程中會(huì)出現(xiàn)早熟收斂和陷入局部最優(yōu)的缺陷,引入PSO算法中全局最優(yōu)思想改進(jìn)同化方程,并利用小波變異替代殖民地革命過程中的隨機(jī)變異,發(fā)展出基于全局最優(yōu)思想的改進(jìn)帝國主義競爭算法——GBICA。通過標(biāo)準(zhǔn)測(cè)試函數(shù)測(cè)試結(jié)果可知,GBIC A相較于IC A在尋優(yōu)精度、尋優(yōu)能力和跳出局部最優(yōu)值三方面能力上均有較大提升;(4)鑒于已知激勵(lì)下的結(jié)構(gòu)模態(tài)參數(shù)識(shí)別在實(shí)際應(yīng)用中很難實(shí)現(xiàn),本文研究環(huán)境激勵(lì)下基于智能優(yōu)化算法的模態(tài)參數(shù)識(shí)別,將參數(shù)識(shí)別問題轉(zhuǎn)化為最優(yōu)化問題,一次性識(shí)別出結(jié)構(gòu)模態(tài)參數(shù)。通過數(shù)值模擬和實(shí)例分析可得:一方面,GBIC A相較于IC A在模態(tài)參數(shù)識(shí)別精度上有明顯提高。另一方面,在不同噪聲環(huán)境下,改進(jìn)后的GBICA識(shí)別結(jié)果更加穩(wěn)定,抗噪性能優(yōu)于ICA;(5)由廣義柔度靈敏度方法識(shí)別損傷的不足之處出發(fā),利用廣義柔度矩陣差構(gòu)建目標(biāo)函數(shù),結(jié)合優(yōu)化算法,將損傷識(shí)別問題轉(zhuǎn)化為最優(yōu)化問題。通過數(shù)值模型的損傷識(shí)別分析可得:一方面,無論是單處損傷還是多處損傷,ICA和GBICA兩種方法均能快速、準(zhǔn)確識(shí)別出結(jié)構(gòu)的損傷位置和損傷程度。另一方面,相較于ICA,GBICA在損傷程度識(shí)別精度上明顯提高,且在高噪聲環(huán)境下,改進(jìn)之后的GBICA穩(wěn)定性明顯優(yōu)于ICA,說明GBICA具有更強(qiáng)的魯棒性。
[Abstract]:Under the influence of many factors, such as harsh natural environment, overload operation and fatigue aging of materials, there are serious hidden dangers to the safety of in-service engineering structures. Traditional methods of nondestructive testing and reliability evaluation need to know the approximate location of structural defects and can not detect the internal defects of the structure, which can no longer meet the practical requirements. In recent years, scholars in this field have carried out a lot of research on structural health monitoring system. Structural identification (structural parameter identification and damage identification) as the core content of structural health monitoring, there are many identification methods, but up to now, there is still no effective method for practical engineering. In this paper, a colony intelligence algorithm-imperialist competition algorithm (Imperialist Competitive Algorithm,ICA) is studied. Aiming at the limitation of the algorithm, an improved algorithm with better searching ability is proposed. It is introduced into structural modal parameter identification and damage identification. The main contents and results are as follows: (1) the research status, main methods and application background of structural modal parameter identification and damage identification are introduced systematically. This paper puts forward the research idea and main research content of applying ICA to structure recognition. (2) the background, basic principle and calculation flow of IC A are introduced in detail. At the same time, the main application fields of ICA are briefly described. (3) aiming at the defects of premature convergence and falling into local optimum in the practical application of ICA, an improved assimilation equation is improved by introducing the global optimal idea in the PSO algorithm. By replacing the random variation in the colonial revolution with wavelet mutation, an improved imperialist competition algorithm based on the global optimal idea, GBICA, is developed. Through the test results of standard test function, we can see that the precision of IC A is better than that of IC A. (4) in view of the fact that structural modal parameter identification under known excitation is difficult to be realized in practical application, this paper studies modal parameter identification based on intelligent optimization algorithm under environment excitation. The parameter identification problem is transformed into an optimization problem, and the structural modal parameters are identified at one time. Through numerical simulation and example analysis, it can be concluded that, on the one hand, compared with IC A, the accuracy of modal parameter identification of GBICA is obviously improved. On the other hand, under different noise conditions, the improved GBICA recognition results are more stable, and the anti-noise performance is better than that of ICA; (5). Based on the generalized flexibility sensitivity method, the objective function is constructed by using the generalized flexibility matrix difference. Combined with the optimization algorithm, the damage identification problem is transformed into an optimization problem. Through the damage identification analysis of the numerical model, it can be concluded that, on the one hand, both single and multiple damage can be quickly identified by ICA and GBICA, and the damage location and damage degree can be accurately identified. On the other hand, compared with ICA,GBICA, the accuracy of damage recognition is obviously improved, and in high noise environment, the improved GBICA stability is obviously better than that of ICA, which shows that GBICA has stronger robustness.
【學(xué)位授予單位】：蘇州科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TU317
[Abstract]:Under the influence of many factors, such as harsh natural environment, overload operation and fatigue aging of materials, there are serious hidden dangers to the safety of in-service engineering structures. Traditional methods of nondestructive testing and reliability evaluation need to know the approximate location of structural defects and can not detect the internal defects of the structure, which can no longer meet the practical requirements. In recent years, scholars in this field have carried out a lot of research on structural health monitoring system. Structural identification (structural parameter identification and damage identification) as the core content of structural health monitoring, there are many identification methods, but up to now, there is still no effective method for practical engineering. In this paper, a colony intelligence algorithm-imperialist competition algorithm (Imperialist Competitive Algorithm,ICA) is studied. Aiming at the limitation of the algorithm, an improved algorithm with better searching ability is proposed. It is introduced into structural modal parameter identification and damage identification. The main contents and results are as follows: (1) the research status, main methods and application background of structural modal parameter identification and damage identification are introduced systematically. This paper puts forward the research idea and main research content of applying ICA to structure recognition. (2) the background, basic principle and calculation flow of IC A are introduced in detail. At the same time, the main application fields of ICA are briefly described. (3) aiming at the defects of premature convergence and falling into local optimum in the practical application of ICA, an improved assimilation equation is improved by introducing the global optimal idea in the PSO algorithm. By replacing the random variation in the colonial revolution with wavelet mutation, an improved imperialist competition algorithm based on the global optimal idea, GBICA, is developed. Through the test results of standard test function, we can see that the precision of IC A is better than that of IC A. (4) in view of the fact that structural modal parameter identification under known excitation is difficult to be realized in practical application, this paper studies modal parameter identification based on intelligent optimization algorithm under environment excitation. The parameter identification problem is transformed into an optimization problem, and the structural modal parameters are identified at one time. Through numerical simulation and example analysis, it can be concluded that, on the one hand, compared with IC A, the accuracy of modal parameter identification of GBICA is obviously improved. On the other hand, under different noise conditions, the improved GBICA recognition results are more stable, and the anti-noise performance is better than that of ICA; (5). Based on the generalized flexibility sensitivity method, the objective function is constructed by using the generalized flexibility matrix difference. Combined with the optimization algorithm, the damage identification problem is transformed into an optimization problem. Through the damage identification analysis of the numerical model, it can be concluded that, on the one hand, both single and multiple damage can be quickly identified by ICA and GBICA, and the damage location and damage degree can be accurately identified. On the other hand, compared with ICA,GBICA, the accuracy of damage recognition is obviously improved, and in high noise environment, the improved GBICA stability is obviously better than that of ICA, which shows that GBICA has stronger robustness.
【學(xué)位授予單位】：蘇州科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TU317
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