摘要翻译：
本文利用人工神经网络(ANN)模型计算了印度查莫利地震和乌塔尔卡什地震动资料对结构体系的响应。该系统首先针对单个真实地震数据进行训练。然后用训练好的神经网络结构模拟不同强度的地震，结果表明，神经网络模型给出的预测响应是准确的，符合实际需要。用部分地震动数据训练神经网络，也能很好地识别结构体系的反应。这样就可以在未来地震发生时预测结构体系的安全性，而不必等到地震发生时再吸取教训。计算了建筑物在不同时间段的最大响应，并对其进行了训练，以预测建筑物在不同时间段的最大响应。对训练后的时间周期-最大响应神经网络模型进行了检验，并对训练中未使用的其它地方的实际地震数据进行了检验，结果表明该模型具有较好的一致性。
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英文标题：
《Response Prediction of Structural System Subject to Earthquake Motions
  using Artificial Neural Network》
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作者：
S. Chakraverty, T. Marwala, Pallavi Gupta and Thando Tettey
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper uses Artificial Neural Network (ANN) models to compute response of structural system subject to Indian earthquakes at Chamoli and Uttarkashi ground motion data. The system is first trained for a single real earthquake data. The trained ANN architecture is then used to simulate earthquakes with various intensities and it was found that the predicted responses given by ANN model are accurate for practical purposes. When the ANN is trained by a part of the ground motion data, it can also identify the responses of the structural system well. In this way the safeness of the structural systems may be predicted in case of future earthquakes without waiting for the earthquake to occur for the lessons. Time period and the corresponding maximum response of the building for an earthquake has been evaluated, which is again trained to predict the maximum response of the building at different time periods. The trained time period versus maximum response ANN model is also tested for real earthquake data of other place, which was not used in the training and was found to be in good agreement.
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PDF链接：
https://arxiv.org/pdf/0705.2235