建筑结构人工智能实验分析环境
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摘要
长期以来,在结构工程领域,为了精确分析各种工程结构的工作性能和反应,人们提出和引入了各种分析理论及方法,并与时俱进地不断改进,使有限元为代表的结构数值模拟分析技术日渐强大。但是,两个显而易见的问题一直在挑战目前的结构分析理论与方法:一是结构分析理论与方法,无论是经验公式还是被广泛应用的有限元分析方法,都是建立在一定的基本假设基础之上,这使得结构的数值模拟结果与结构的实际工作性能与反应之间具有天然的缺欠,在许多复杂工程结构分析中误差太大、甚至失效。二是长期积累的数量巨大的现有试验数据仅用于回归分析或检测数值模拟精度,而这些数据中所包含的大量关于结构工作性能与反应的宝贵信息没有被充分发掘出来并加以利用,无形中造成了巨大浪费。因此,若想避免由基本假设引进的误差,提高结构分析的精度和有效性,则需寻找能够直接从结构的实际工作行为/反应出发,预测新结构工作行为/反应的结构分析方法;而试验数据的充分利用,则需要发展行之有效的从现有试验数据进行知识挖掘的方法。
(1)为了解决上述问题,本文在实验数据和人工智能方法的“建筑结构人工智能实验分析环境(AIEESA)”的概念基础上,创建了相应的集成分析系统。“建筑结构人工智能实验分析环境”由人工智能技术(AITs)、数据挖掘技术、试验数据、结构构造的数字建模方法、以及一系列匹配结构类似性质和绘制结构行为/反应的匹配准则共同构成。试验数据经过数据挖掘处理,作为适合于AITs运算的数字模式。当一个新/未知结构模型进入“建筑结构人工结构智能实验分析环境”,该模型的行为或反应,如试验表达,能够基于现有的试验数据和现场测量数据绘制出来。在给出“建筑结构人工结构智能实验分析环境”的概念后,本文依次探讨了“建筑结构人工结智能实验分析环境”各个组成部分。
首先,提出了“建筑结构人工智能实验分析环境”数据库的组织方法:此数据库有三个组成部分:(1)结构行为,在本文中具体指横向荷载作用下砌体墙板的破坏模式与破坏荷载;(2)标准化的结构行为,为了集中反映横向荷载作用下砌体墙板破坏模式的特征,将构造类似的墙板破坏模式主要特征进行归纳提炼并剔除次生裂纹,得到标准化的墙板破坏模式,称为标准化的结构行为;(3)结构的反应,本文中结构反应指各级荷载作用下砌体墙板相应测点的位移值。这样,就构成了知识发现的原始信息源,而且便于将相应的数据挖掘技术引入“建筑结构人工智能实验分析环境”。
其次,本文研究了“建筑结构人工智能实验分析环境”中的两个数值模式:结构工作行为数值模式以及结构构造状态数值模式。在探讨结构破坏模式的数值描述方法过程中,引入了广义墙板的概念,从而丰富了相似度概念的特有内涵,并给出了一种定量的比较基础模型和新模型破坏模式的方法。在探讨结构构造状态数值模式过程中,提出了结构构造数值模式的两种方法:适于四边简支墙板的细胞自动机模型(CA)和基于有限元分析(FEA)的无量纲化法。后者以FEA分析所得区域位移的无量纲化结果作为数值模式,丰富了“建筑结构人工智能实验分析环境”结构行为建模的物理意义。
再者,研究了“建筑结构人工智能实验分析环境”的两个匹配准则:类似区域匹配准则和行为匹配准则。本文重点研究了类似区域匹配准则。在Zhou提出的匹配准则基础上提出了三种加权的匹配准则,并对这三种匹配准则在“建筑结构人工智能实验分析环境”中的应用效果进行比较,找出了结构构造条件不同时相应效果最佳的加权匹配准则。
本文还针对CA数值模型中,新模型的整体性质变异和局部性质及边界约束变异的建模方法进行了探讨。整体性质的变异可通过CA模型通过传递系数取值的变化来反映。建立SVM模型求得最优的传递系数取值范围,该范围一旦确定,则新模型的破坏模式即可通过“建筑结构人工智能实验分析环境”得到。并进一步研究了如何通过CA模型边界条件初始值的变化来反映由于边界条件和较大区域范围内的结构性质变异。
然后,建立起了三种基于墙板破坏模式预测其相应破坏荷载的神经网络模型, BP, RBF和RA神经网络模型,使得“建筑结构人工智能实验分析环境”具有了预测破坏荷载的功能。
最后,给出了一系列应用“建筑结构人工智能实验分析环境”预测结构行为的例子,并与相应试验数据进行了对比,验证了所建立起的“建筑结构人工智能实验分析环境”的有效性。
综上,本文建立的结构分析系统实现了直接从结构的实际工作行为/反应出发,预测新结构工作行为/反应;并且能够实现从现有试验数据挖掘出所蕴含的丰富知识,从而克服了传统结构分析技术的固有缺陷,初步建立起将挖掘出的知识用于新结构行为/反应的预测的“建筑结构人工智能实验分析环境”,为科研人员和结构设计人员提供了有力的结构分析工具。
For a long time, all sorts of analytical theories and methodologies were constantly proposed and improved, aiming at analyzing the performance/behavior and response of structures precisely. These reseach achievements have resulted in increasingly powerful capacities of numerical simulation techniques such as finite element analysis technique. However, two issues have been challengingthe current analytical theories and methodologies. One is that both empirical formula and widely used FEA methods are all established on many a hypothesis, which causes natural errors between numerical simulation and actual behavior/response of structure. In many cases, these hypotheses even lead to the invalidation of numerical simulation when a structure is complex. The other is that a large quantity of experimental data accumulated over a long period of time is only applied in regression analysis and precision estimation of structural numerical simulation; thus, this results in enormous waste of abundant precious information about structural working performances/responses included in these data. Therefore,in order to avoid errors from hypothesis and enhance accuracy of structural analysis, it is needed to develop some new analytical methodologies that could predict structural behavior/response based on structural real behaviors/responses. For a full application of experimental data, more capable knowledge mining techniques should be developed .
In order to addressthe issues elaborated above, this study proposes a new concept of Artificial Intelligent“Experimental Environment for Structural Analysis (AIEESA)”and esteblishes the corresponding system. The AIEESA consists of AITs, data mining techniques and experimental data, along with numerically modeling methodologies of structural configuration and a series of criteria for matching similarity of structural properties and mapping/predicting structural behavior /response. The experimental data is subjected to a process of data mining and then treated as the numerical modes suitable for AIT functions. When a new/unseen structural model is placed in this AIEESA, the behavior or response of the model, like its experimental expression, could be mapped/predicted based on the existing experimental data or the data from the site measurements. Each part of AIEESA is investigated after concept of AIEESA is illuminated.
First of all, the data base in AIEESA is built as three parts: (1) Behavior of structure. Behavior of structure refers to failure patterns and failure loads of laterally loaded wall panels in this text; (2) Normalized behavior of structure. The normalization underlines themain features of failure patterns of laterally loadedwall panels with similar configurations and deletes some noise. (3) Structural response. Structural response in this text is the displacement values at the measuring points on experimental wall panels subjective toloading increment. Thus, original information resource of knowledge discovering is constituted in consideration of conveniently data mining.
Secondly, two numerical modeling techniques in AIEESA are studied in this dissertation: one is the numerical modeling technique of failure pattern and the other is the numerical nodeling technique of structural configuration. A new concept of generalized wall panel is put forward to developthe numerical modeling technique of failure pattern. This concept enriches the special content of the similarity level and provides a quantitative method to compare the failure patterns of both base and new models. For the numerical modeling technique of structiral configuration, a CA modeling technique for masonry panels constrained at four edges and a dimensionless technique based on the conventional FEA are raised. The dimensional technique contributes more physical meaning of the numerical modeling of structural behavior in AIEESA.
Thirdly, criterion forboth similar zone matching and behavior matching are investgated. Three weighted criteria for similar zone matching are proposed, and a comparison is made between applications effects of three criteria to find out the optimal criterion for panels with different configuration.
Then, modeling methods of both global property variation and property variation with CA technique are studied. On one hand, global property variation could be described by transition coefficient in CA model. Meanwhile, a SVM model is built up to obtained optimal value of transition coefficient. Once the range of transition coefficient is determined, failure pattern of new model could be predictedd with AIEESA. On the other hand, this study also put forward that varying of initial value in CA model could reflects property variation properly.
Moreover, three sorts of neutral networks: BP, RBF and RA are established to predict failure loads account of corresponding failure patterns, which enable function to predict failure loadsof AIEESA.
Finally, a comparison is made between the a series of implementing results of AIEESA and corresponding experimental behavior/response to verify validation of AIEESA.
In sum, the analytical system AIEESA built up in this study realizes predicting structural behavior/response directly from existing real structural behavior/response; and it also achieves mining out plenty knowledge contained in now existing experimental data. Therefore, the AIEESAnot only overcomes inherent defect of conventional analyzing technologies and methodologies, but alsoprovides a new way to mine out knowledge contained in existing experimental data and apply to analysis of new structures. Consequently, AIEESA provides researchers and engineers an effective structural analysis tool.
(1)为了解决上述问题,本文在实验数据和人工智能方法的“建筑结构人工智能实验分析环境(AIEESA)”的概念基础上,创建了相应的集成分析系统。“建筑结构人工智能实验分析环境”由人工智能技术(AITs)、数据挖掘技术、试验数据、结构构造的数字建模方法、以及一系列匹配结构类似性质和绘制结构行为/反应的匹配准则共同构成。试验数据经过数据挖掘处理,作为适合于AITs运算的数字模式。当一个新/未知结构模型进入“建筑结构人工结构智能实验分析环境”,该模型的行为或反应,如试验表达,能够基于现有的试验数据和现场测量数据绘制出来。在给出“建筑结构人工结构智能实验分析环境”的概念后,本文依次探讨了“建筑结构人工结智能实验分析环境”各个组成部分。
首先,提出了“建筑结构人工智能实验分析环境”数据库的组织方法:此数据库有三个组成部分:(1)结构行为,在本文中具体指横向荷载作用下砌体墙板的破坏模式与破坏荷载;(2)标准化的结构行为,为了集中反映横向荷载作用下砌体墙板破坏模式的特征,将构造类似的墙板破坏模式主要特征进行归纳提炼并剔除次生裂纹,得到标准化的墙板破坏模式,称为标准化的结构行为;(3)结构的反应,本文中结构反应指各级荷载作用下砌体墙板相应测点的位移值。这样,就构成了知识发现的原始信息源,而且便于将相应的数据挖掘技术引入“建筑结构人工智能实验分析环境”。
其次,本文研究了“建筑结构人工智能实验分析环境”中的两个数值模式:结构工作行为数值模式以及结构构造状态数值模式。在探讨结构破坏模式的数值描述方法过程中,引入了广义墙板的概念,从而丰富了相似度概念的特有内涵,并给出了一种定量的比较基础模型和新模型破坏模式的方法。在探讨结构构造状态数值模式过程中,提出了结构构造数值模式的两种方法:适于四边简支墙板的细胞自动机模型(CA)和基于有限元分析(FEA)的无量纲化法。后者以FEA分析所得区域位移的无量纲化结果作为数值模式,丰富了“建筑结构人工智能实验分析环境”结构行为建模的物理意义。
再者,研究了“建筑结构人工智能实验分析环境”的两个匹配准则:类似区域匹配准则和行为匹配准则。本文重点研究了类似区域匹配准则。在Zhou提出的匹配准则基础上提出了三种加权的匹配准则,并对这三种匹配准则在“建筑结构人工智能实验分析环境”中的应用效果进行比较,找出了结构构造条件不同时相应效果最佳的加权匹配准则。
本文还针对CA数值模型中,新模型的整体性质变异和局部性质及边界约束变异的建模方法进行了探讨。整体性质的变异可通过CA模型通过传递系数取值的变化来反映。建立SVM模型求得最优的传递系数取值范围,该范围一旦确定,则新模型的破坏模式即可通过“建筑结构人工智能实验分析环境”得到。并进一步研究了如何通过CA模型边界条件初始值的变化来反映由于边界条件和较大区域范围内的结构性质变异。
然后,建立起了三种基于墙板破坏模式预测其相应破坏荷载的神经网络模型, BP, RBF和RA神经网络模型,使得“建筑结构人工智能实验分析环境”具有了预测破坏荷载的功能。
最后,给出了一系列应用“建筑结构人工智能实验分析环境”预测结构行为的例子,并与相应试验数据进行了对比,验证了所建立起的“建筑结构人工智能实验分析环境”的有效性。
综上,本文建立的结构分析系统实现了直接从结构的实际工作行为/反应出发,预测新结构工作行为/反应;并且能够实现从现有试验数据挖掘出所蕴含的丰富知识,从而克服了传统结构分析技术的固有缺陷,初步建立起将挖掘出的知识用于新结构行为/反应的预测的“建筑结构人工智能实验分析环境”,为科研人员和结构设计人员提供了有力的结构分析工具。
For a long time, all sorts of analytical theories and methodologies were constantly proposed and improved, aiming at analyzing the performance/behavior and response of structures precisely. These reseach achievements have resulted in increasingly powerful capacities of numerical simulation techniques such as finite element analysis technique. However, two issues have been challengingthe current analytical theories and methodologies. One is that both empirical formula and widely used FEA methods are all established on many a hypothesis, which causes natural errors between numerical simulation and actual behavior/response of structure. In many cases, these hypotheses even lead to the invalidation of numerical simulation when a structure is complex. The other is that a large quantity of experimental data accumulated over a long period of time is only applied in regression analysis and precision estimation of structural numerical simulation; thus, this results in enormous waste of abundant precious information about structural working performances/responses included in these data. Therefore,in order to avoid errors from hypothesis and enhance accuracy of structural analysis, it is needed to develop some new analytical methodologies that could predict structural behavior/response based on structural real behaviors/responses. For a full application of experimental data, more capable knowledge mining techniques should be developed .
In order to addressthe issues elaborated above, this study proposes a new concept of Artificial Intelligent“Experimental Environment for Structural Analysis (AIEESA)”and esteblishes the corresponding system. The AIEESA consists of AITs, data mining techniques and experimental data, along with numerically modeling methodologies of structural configuration and a series of criteria for matching similarity of structural properties and mapping/predicting structural behavior /response. The experimental data is subjected to a process of data mining and then treated as the numerical modes suitable for AIT functions. When a new/unseen structural model is placed in this AIEESA, the behavior or response of the model, like its experimental expression, could be mapped/predicted based on the existing experimental data or the data from the site measurements. Each part of AIEESA is investigated after concept of AIEESA is illuminated.
First of all, the data base in AIEESA is built as three parts: (1) Behavior of structure. Behavior of structure refers to failure patterns and failure loads of laterally loaded wall panels in this text; (2) Normalized behavior of structure. The normalization underlines themain features of failure patterns of laterally loadedwall panels with similar configurations and deletes some noise. (3) Structural response. Structural response in this text is the displacement values at the measuring points on experimental wall panels subjective toloading increment. Thus, original information resource of knowledge discovering is constituted in consideration of conveniently data mining.
Secondly, two numerical modeling techniques in AIEESA are studied in this dissertation: one is the numerical modeling technique of failure pattern and the other is the numerical nodeling technique of structural configuration. A new concept of generalized wall panel is put forward to developthe numerical modeling technique of failure pattern. This concept enriches the special content of the similarity level and provides a quantitative method to compare the failure patterns of both base and new models. For the numerical modeling technique of structiral configuration, a CA modeling technique for masonry panels constrained at four edges and a dimensionless technique based on the conventional FEA are raised. The dimensional technique contributes more physical meaning of the numerical modeling of structural behavior in AIEESA.
Thirdly, criterion forboth similar zone matching and behavior matching are investgated. Three weighted criteria for similar zone matching are proposed, and a comparison is made between applications effects of three criteria to find out the optimal criterion for panels with different configuration.
Then, modeling methods of both global property variation and property variation with CA technique are studied. On one hand, global property variation could be described by transition coefficient in CA model. Meanwhile, a SVM model is built up to obtained optimal value of transition coefficient. Once the range of transition coefficient is determined, failure pattern of new model could be predictedd with AIEESA. On the other hand, this study also put forward that varying of initial value in CA model could reflects property variation properly.
Moreover, three sorts of neutral networks: BP, RBF and RA are established to predict failure loads account of corresponding failure patterns, which enable function to predict failure loadsof AIEESA.
Finally, a comparison is made between the a series of implementing results of AIEESA and corresponding experimental behavior/response to verify validation of AIEESA.
In sum, the analytical system AIEESA built up in this study realizes predicting structural behavior/response directly from existing real structural behavior/response; and it also achieves mining out plenty knowledge contained in now existing experimental data. Therefore, the AIEESAnot only overcomes inherent defect of conventional analyzing technologies and methodologies, but alsoprovides a new way to mine out knowledge contained in existing experimental data and apply to analysis of new structures. Consequently, AIEESA provides researchers and engineers an effective structural analysis tool.
引文
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