Prediction of structural responses with the aid of fuzzy stochastic time series

对结构未来行为的了解是我们社会中影响深远的经济和安全相关决策的基础。预测未来的发生，强度和发展的环境影响，如水分渗透和氯化物污染，和结构损伤是必不可少的计算时间相关的安全水平，并估计结构的寿命。描述这些参数的数据序列具有随机不确定性和非正式不确定性。因此，这些序列被认为是本方法的基本扩展-作为模糊随机过程的实现。引入了一个模糊随机过程作为一个由维数模糊扩展的随机过程。研究了模糊随机时间序列的识别和量化方法。为了预测未来的结构行为，可测量和不可测量的结构响应被认为是。可测量的结构响应和可测量的影响可以直接预测，而不可测量的响应只能通过将计算模型应用于影响的时间序列数据来间接预测。平稳和非平稳模糊过程，如模糊阿尔马模型或模糊神经网络的预测模型，开发。这两个参数的方法结合贝叶斯理论扩展到适用于不确定的数据（模糊贝叶斯）和非参数的方法，包括模糊神经网络解决方案的模糊数据被考虑在内。

Knowledge about the future behaviour of a structure is the basis for far reaching economical and security relevant decisions in our society. Predictions regarding the future occurrence, intensity, and development of environmental influences, such as a moisture penetration and chloride contamination, and of structural damage are indispensable to compute time-dependent safety levels and to estimate the life time of a structure. Sequences of data describing these parameters possess both stochastic uncertainty and informal uncertainty. Therefore, these sequences are considered - as essential extension to the present methods - as realizations of fuzzy stochastic processes. A fuzzy stochastic process is introduced as a stochastic process extended by the dimension fuzziness. Methods for identification and quantification of fuzzy stochastic time series are investigated. For predicting future structural behavior both measurable and non-measurable structural responses are considered. Measurable structural responses and measurable impacts can be predictet directly, whereas nonmeasurable responses can only be predicted indirectly by applying a computational model to time series data for impacts. Suitable prediction models for stationary and nonstationary fuzzy processes, such as a fuzzy ARMA model or fuzzy neural networks, are developed. Both parametric methods in combination with Bayesian theory extended to apply for uncertain data (Fuzzy-Bayes) and nonparametric methods including fuzzy neural network solutions for fuzzy data are taken into consideration.