本文關(guān)鍵詞： 貝葉斯網(wǎng)絡(luò) 地震液化 性能指標(biāo) 概率預(yù)測(cè) 災(zāi)害評(píng)估 決策分析　出處：《大連理工大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：地震液化問(wèn)題的深入研究對(duì)于抗震減災(zāi)工程而言是極其重要的。目前已有的研究成果絕大部分是關(guān)于地震液化發(fā)生的預(yù)測(cè),而且已有的液化預(yù)測(cè)方法存在適用性窄,預(yù)測(cè)精度不高等缺陷,另外,對(duì)于液化后的災(zāi)害綜合預(yù)測(cè)研究鮮有,特別是對(duì)于液化的減災(zāi)決策問(wèn)題的研究。本文基于貝葉斯網(wǎng)絡(luò)方法,通過(guò)統(tǒng)計(jì)計(jì)量手段篩選地震液化的重要影響因素,建立了地震液化的貝葉斯網(wǎng)絡(luò)預(yù)測(cè)模型,隨后引入液化災(zāi)害變量和抗液化措施及損失,分別建立了地震液化的貝葉斯網(wǎng)絡(luò)液化災(zāi)害風(fēng)險(xiǎn)評(píng)估模型和液化減災(zāi)決策模型,擴(kuò)展的模型不僅可以對(duì)地震液化的發(fā)生及液化后的災(zāi)害做出快速、準(zhǔn)確的預(yù)測(cè)和評(píng)估,而且可以針對(duì)液化災(zāi)害程度及場(chǎng)地性質(zhì),分析得出最佳減災(zāi)決策方案,為工程抗震減災(zāi)提供科學(xué)依據(jù)。本文的主要研究有如下幾個(gè)方面：(1)基于統(tǒng)計(jì)計(jì)量手段和地震液化重要因素篩選原則,從眾多地震液化的影響因素中篩選出了12個(gè)重要因素：震中距、地震震級(jí)、峰值加速度、地震持續(xù)時(shí)間、土質(zhì)類別、細(xì)粒或粘粒含量、顆粒級(jí)配、相對(duì)密度、可液化層厚度、可液化層埋深、上覆有效應(yīng)力和地下水位,并采用解釋結(jié)構(gòu)模型方法對(duì)這12個(gè)重要影響因素建立了其層次結(jié)構(gòu)網(wǎng)絡(luò),分析了各重要影響因素間的層次強(qiáng)弱關(guān)系,為建立地震液化的貝葉斯網(wǎng)絡(luò)模型提供了基礎(chǔ)準(zhǔn)備。(2)選擇地震等級(jí)、震中距、標(biāo)準(zhǔn)貫入錘擊數(shù)、地下水位和砂土層埋深5個(gè)因素,基于解釋結(jié)構(gòu)模型法和K2算法分別建立了僅適用于自由場(chǎng)地的地震砂土液化判別的五因素主觀貝葉斯網(wǎng)絡(luò)模型、客觀貝葉斯網(wǎng)絡(luò)模型和混合貝葉斯網(wǎng)絡(luò)模型,選用總體精度(OA)、準(zhǔn)確率(Pre)、召回率(Rec)、F1值和ROC曲線下面積(AUC)五個(gè)性能指標(biāo),和其他常用的地震液化判別方法對(duì)比,驗(yàn)證了貝葉斯網(wǎng)絡(luò)模型的有效性和準(zhǔn)確性,其中地震砂土液化的混合貝葉斯網(wǎng)絡(luò)模型的預(yù)測(cè)性能最好。隨后探討了訓(xùn)練樣本的分類不均衡和抽樣偏差對(duì)地震液化概率預(yù)測(cè)模型的影響,發(fā)現(xiàn)訓(xùn)練樣本分類越不均衡、抽樣偏差越大,模型的回判性能越好,而預(yù)測(cè)性能卻越差,并給出了常用概率預(yù)測(cè)方法的最佳訓(xùn)練樣本分類比例范圍,以減小訓(xùn)練樣本分類比例選擇所帶來(lái)的模型誤差。另外,針對(duì)訓(xùn)練樣本存在嚴(yán)重分類不均衡或抽樣偏差的情況,探討了過(guò)采樣技術(shù)對(duì)在常用概率模型性能提升中的應(yīng)用。(3)在大量數(shù)據(jù)不完備和數(shù)據(jù)完備兩種情況下,基于篩選的12個(gè)重要影響因素,分別提出了基于解釋結(jié)構(gòu)模型和因果圖法建立地震液化多因素主觀貝葉斯網(wǎng)絡(luò)預(yù)測(cè)模型的方法及融合解釋結(jié)構(gòu)模型和K2算法建立地震液化多因素混合貝葉斯網(wǎng)絡(luò)預(yù)測(cè)模型的方法。采用K(K=5)折交叉試驗(yàn),選用五個(gè)性能評(píng)估指標(biāo),與其他液化概率預(yù)測(cè)模型的性能進(jìn)行綜合對(duì)比,驗(yàn)證了兩種方法所建的貝葉斯網(wǎng)路液化預(yù)測(cè)模型的準(zhǔn)確性和魯棒性,并對(duì)液化的12個(gè)影響因素進(jìn)行了敏感性分析和反演推理。所建立的新貝葉斯網(wǎng)絡(luò)模型擴(kuò)展并改善了前面五因素模型的適用范圍和預(yù)測(cè)精度,使其能適用于不同土質(zhì)類別、不同細(xì)粒含量的自由場(chǎng)地液化和有上部結(jié)構(gòu)物場(chǎng)地液化的更高精度預(yù)測(cè)。(4)在貝葉斯網(wǎng)路液化預(yù)測(cè)模型基礎(chǔ)上,引入地震液化的災(zāi)害指標(biāo),如液化潛能指數(shù)、噴砂冒水、地面裂縫、地表沉降和側(cè)向位移,建立了貝葉斯網(wǎng)路液化災(zāi)害風(fēng)險(xiǎn)評(píng)估模型,使其可以綜合評(píng)估液化后場(chǎng)地的災(zāi)害程度。與神經(jīng)網(wǎng)絡(luò)方法對(duì)比,驗(yàn)證了模型的精準(zhǔn)性和可靠性。隨后,引入抗液化措施和其效用及成本,擴(kuò)展了該模型,使其不僅可以預(yù)測(cè)液化及液化災(zāi)害,而且可以為液化減災(zāi)提供最佳決策支持。將地震液化減災(zāi)的決策模型應(yīng)用到人工島的抗液化決策中,采用數(shù)值模擬結(jié)果驗(yàn)證了模型的有效性,使其能為液化的防災(zāi)減災(zāi)提供科學(xué)依據(jù)。
[Abstract]:Study of earthquake liquefaction problems is very important for earthquake disaster mitigation projects. The existing research results mostly about prediction of liquefaction and liquefaction, prediction method has narrow applicability, the prediction accuracy is not high defect, in addition, for comprehensive prediction of post liquefaction disaster research rare, especially on the disaster reduction decision problem. In this paper the liquefaction method based on Bayesian network, by means of statistical measurement of seismic liquefaction of the important influence factors of screening, established a prediction model of Bayesian network for earthquake liquefaction, followed by liquefaction hazard variables and anti liquefaction measures and losses, the earthquake liquefaction disaster risk assessment model and Bayesian network liquefaction mitigation decision model of liquefaction established, extended model can not only make the earthquake liquefaction and post liquefaction disaster, accurately The prediction and assessment, and according to the nature of liquefaction hazard degree and site, analyze the best mitigation decision scheme, and provide scientific basis for earthquake disaster mitigation engineering. The main research of this paper are as follows: (1) statistical measurement method and seismic liquefaction is an important factor for screening based on the principle of selection from the many factors that influence the seismic liquefaction the 12 important factors: the earthquake magnitude, epicentral distance, peak acceleration, duration of earthquake, soil, or fine clay content, particle size, relative density, liquefiable layer thickness, liquefaction depth, effective overburden stress and underground water level, and the factors that influence the establishment of 12 important the hierarchical structure of network by using interpretative structural modeling method, analyses the factors influence the strength of the relationship between, for the establishment of a Bayesian network model of seismic liquefaction provides the basis for the choice (2). The earthquake epicenter, grade, SPT, underground water and sand layer depth of 5 factors, five factors of seismic liquefaction subjective Bias network model is only suitable for the free field established interpretive structure model and K2 algorithm respectively based on objective Bias network model and Bias mixed network model selection the overall accuracy (OA), accuracy (Pre), the recall rate (Rec), F1 value and the area under the ROC curve (AUC) five performance indexes, and other commonly used seismic liquefaction methods to verify the effectiveness of Bias network model and the accuracy of the prediction performance of hybrid Bias network model of sand liquefaction the best. Then it discusses the classification of training samples is not balanced and the sampling bias prediction model of influence on seismic liquefaction probability, we find that the training sample classification is more uneven, the greater the sampling bias, discriminant performance model The better, and the prediction performance is worse, and the best training sample classification ratio range prediction method commonly used to model the probability of error brought by reducing training samples classification scale selection. In addition, the training samples are seriously unbalanced classification or sampling bias conditions, discusses the application of over sampling technique to improve performance the commonly used probability model. (3) in a large number of incomplete data and complete data for two cases, 12 important factors are put forward based on screening, interpretative structural model and causal graph method to establish seismic liquefaction multi factor forecasting method and subjective Bayesian network fusion prediction model to explain the structure model and K2 based seismic liquefaction hybrid Bayesian network based on multi factor. Using K (K=5) fold cross test, using five performance evaluation index, and other prediction model of probability of liquefaction Can carry out comprehensive comparison, Bias network prediction model to verify the liquefaction of two methods the accuracy and robustness, and the 12 factors influencing liquefaction sensitivity analysis was carried out and the inverse reasoning. The new Bias network model to expand and improve the application range and the first five factors model prediction accuracy, the suitable for different categories of soil, the effect of fines content on the liquefaction free and have higher accuracy of site liquefaction prediction of upper structure. (4) based on the prediction model of liquefied Bias network, introducing the disaster index of seismic liquefaction, such as liquefaction potential index, sand at the water, ground crack, surface subsidence and lateral displacement. The risk of liquefaction hazard assessment model of network Bias, which can make the comprehensive evaluation after liquefaction site degree of the disaster. Compared with the neural network method to validate the model accuracy and Depending on the nature. Then, introducing the anti liquefaction measures and its utility and cost, the model is extended, so that it can not only predict the liquefaction and disaster, but also can provide the best decision support for disaster reduction. The application of liquefaction decision model of earthquake liquefaction mitigation to liquefaction decision artificial island, the numerical simulation results verify the model, which can provide scientific basis for disaster prevention and mitigation of liquefaction.

【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TU435
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本文編號(hào)：1502021