| 摘要: |
| 为提高结构地震反应分析的计算效率,可以仅将决定结构地震反应大小的地震动强烈震动段作为输入,因此研究对应于强烈震动段的持时预测方法具有意义。本文以地震动截取前后结构最大位移反应保持不变为标准,考虑结构进入塑性时导致的周期延长影响、高阶模态影响、估计结构屈服强度时存在不确定性的影响,提出了一种基于深度学习的地震动持时预测方法,该方法可以针对不同周期的结构给出相应的地震动持时预测结果。该深度学习模型以地震动参数和结构参数作为输入特征,对80 280个样本进行训练和预测,将该模型用于分析4层结构和16层结构的最大层间位移角,并与广泛使用的工程输入地震动持时确定方法（95%Arias持时和75%Arias持时）所得结果进行比较。结果表明:本文方法和95%Arias持时方法用于4层结构时均表现良好,但用于16层结构时95%Arias持时方法的计算误差明显变大；75%Arias持时用于4层和16层结构时的计算误差均远高于本文方法。基于人工智能的地震动持时预测方法有望实现兼具计算效率高、较小计算误差和较强适用性的优点,是处理工程输入地震动的一种有效方法。 |
| 关键词: 地震动持时 时程分析 计算效率 深度学习 最大位移反应 |
| DOI：10.11918/202108113 |
| 分类号:TU375.4 |
| 文献标识码:A |
| 基金项目:国家重点研发计划 (2017YFC1500604)；中国地震局工程力学研究所基本科研业务费专项(2018D02)；西藏自治区重点研发与转化计划项目(XZ201801-GB-07) |
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| Prediction of earthquake ground motion duration based on artificial intelligence method |
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YAO Lan1,2,LI Shuang2,3
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(1.Key Lab of Earthquake Engineering and Engineering Vibration of China Earthquake Administration (Institute of Engineering Mechanics, China Earthquake Administration), Harbin 150080, China; 2.Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education, Harbin 150090, China; 3.Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters (Harbin Institute of Technology), Ministry of Industry and Information Technology, Harbin 150090, China)
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| Abstract: |
| To improve the calculation efficiency of structural seismic response analysis, the segment of strong ground motion can be used as the only input because it determines the magnitude of structural responses. In this study, a deep-learning neural network for predicting ground motion duration was proposed. The criterion used in the method is that the maximum displacement of the structure remains unchanged before and after the ground motion truncation, and the method considers the influences of period elongation, high order modes, and the uncertainty in estimating the structural yield strength. The deep-learning method can provide prediction results of ground motion duration with different structural periods. Taking parameters of ground motion and structure as the input features, the deep-learning model used 80 280 samples for training and prediction, and was applied to analyze the maximum story drift ratios of 4-story and 16-story frames respectively. The results were compared with the errors obtained from the widely used methods (95% Arias duration and 75% Arias duration). Results show that the proposed method and the 95% Arias duration method both performed well for the 4-story frame, but the calculation error of the 95% Arias duration method was larger for the 16-story frame; the errors of the 75% Arias duration method for 4-story and 16-story frames were much larger compared with the proposed method. The proposed prediction method of ground motion duration based on artificial intelligence is expected to improve calculation efficiency, reduce error, and widen the application scope. |
| Key words: ground motion duration time history analysis calculation efficiency deep learning maximum displacement |
