《Journal of Engineering Mechanics》刊登“神经模型与有限元模型在设计高精度快速非线性大坝中的比较”

作者：Joghataie, Abdolreza，Dizaji, Mehrdad Shafiei

刊物：《Journal of Engineering Mechanics》.2013年10月，第139卷10期，第1311-1324页.

关键词： 混凝土坝，裂缝、地震荷载、有限元法、重力坝、滞后、神经模型、非线性响应、弥散裂缝模型

摘要：本文提出一种基于人工神经网络来分析重力坝在地震荷载作用下非线性滞后响应的方法。该方法的主要优点是基于了大坝的检测数据为分析特定大坝提供了有力的工具，比之前分析软件的计算结构更精确。特别对于强震条件下响应为非线性和滞后性的情况，上述优点更明显。此处将神经网络建模称为神经建模，并可以给出较多可性能性的分析结果。

例如，在地震荷载作用下大坝的非线性分析要求考虑时间效应；大坝的神经建模设计的优点之一是可以提供短时间内地震响应的实际精确结果。通过神经建模训练和试验的控制精确数据，可以评估方法的精确性，并使用分析软件模拟试验。弥散裂缝模型是成功用于混凝土重力坝的模型之一，在此研究中不但用到此模型，还用到了多层前馈神经网络。此方法的第一步是分析不同地震作用下的大坝，以模拟线性和非线性响应的方法收集大量数据。第二步是进行神经建模，基于收集的数据研究地震作用下大坝的非线性滞后响应。第三步是检验神经建模的精度和一般能力，然后利用神经建模分析众多不同地震作用的大坝。试验成功通过后，神经建模就可以提供任意给定地震作用下大坝的可靠和精确分析结果。

Designing High-Precision Fast Nonlinear Dam Neuro-Modelers and Comparison with Finite-Element Analysis

Authors：Joghataie, Abdolreza，Dizaji, Mehrdad Shafiei

Journal：《Journal of Engineering Mechanics》. Oct2013, Vol. 139 Issue 10, p1311-1324.

Keyword：Concrete dams，Cracking，Earthquake loads，Finite element method，Gravity dams，Hysteresis，Neuro-modeler，Nonlinear response，Smeared crack model

Abstract：In this paper, a method has been proposed to use artificial neural networks for the modeling of concrete gravity dams with nonlinear hysteretic response under earthquake loading. The main advantage of this method is that it makes it possible to design an analysis tool for a specific dam based on the data obtained from monitoring said dam; hence, it is expected that the analysis tool could provide more precise results than analysis software presently available. This advantage is especially pronounced when the dam is to be analyzed under strong earthquakes where its response is nonlinear and hysteretic. The modeler neural network, referred to here as the neuro-modeler, offers considerable possibilities. For example, the nonlinear analysis of dams under earthquake loading requires considerable time; one advantage of designing neuro-modelers for dams is that they can give practically precise results about the response in a short time. As the first study on the subject to prepare a controlled precise data for the training and testing of the neuro-modeler so that the precision of the method could be evaluated, analysis software was used to simulate the experiment. The smeared crack model, which has been one of the models used successfully in the literature to model concrete gravity dams, has been used in this study as well. Multilayer feed-forward neural networks have also been used. The first step in this method is to analyze the dam under study as it is subjected to different earthquake simulations to collect large data about its linear and nonlinear response. The second step is to train a neuro-modeler, based on the collected data, to implicitly learn the nonlinear hysteretic response of the dam being subjected to the training earthquakes. The third step is to test the precision and generalization capabilities of the neuro-modeler where it is used for the analysis of the dam under a number of selected earthquakes of different properties including both near- and far-field excitations. After passing the tests successfully, the neuro-modeler is expected to be able to provide reliable and precise results about the response of the dam under any given earthquake.

原文链接：https://vpn2.nlc.gov.cn/prx/000/http/web.ebscohost.com/ehost/detail?vid=4&sid=d883a9a3-533c-4459-b37f-f16f572783e0%40sessionmgr13&hid=25&bdata=Jmxhbmc9emgtY24mc2l0ZT1laG9zdC1saXZl#db=a9h&AN=90259402