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1.
This paper proposes the use of neural networks to predict damage due to earthquakes from the indices of recorded ground motion. Since the relationship between ground motion indices and resulting damage is difficult to express in mathematical form, neural networks are conveniently applied for this problem. Simulated earthquake ground motions are used to have a well-distributed data set and the ductility factor from non-linear analysis of two single-degree-of-freedom structural models is used to represent the damage. A sensitivity analysis procedure is described to identify qualitatively the input parameters that have a greater influence on the damage. The result of the trained neural network is then verified by using several recorded earthquake ground motions. It is found that some instability in the prediction can occur. Instability occurs when input values exceed the range of the training data. The neural network model using PGA and SI as input give the best performance in the recall tests using actual earthquake ground motion, demonstrating the usefulness of neural network models for the quick estimation of damage through earthquake intensity monitoring. 相似文献
2.
运用RBF人工神经网络模型,结合中国震例,通过对1976年以来新疆天山地震带MS≥4.7地震前异常参数研究分析,筛选出15个地震异常指标使其作为RBF神经网络的输入样本,经过31组样本集的训练和5组检验样本的检验,建立了基于RBF神经网络地震震级预测模型,通过对实际震例的检验取得了较为理想的预报效果。 相似文献
3.
This work presents a novel procedure for identifying the dynamic characteristics of a building and diagnosing whether the building has been damaged by earthquakes, using a back‐propagation neural network approach. The dynamic characteristics are directly evaluated from the weighting matrices of the neural network trained by observed acceleration responses and input base excitations. Whether the building is damaged under a large earthquake is assessed by comparing the modal parameters and responses for this large earthquake with those for a small earthquake that has not caused this building any damage. The feasibility of the approach is demonstrated through processing the dynamic responses of a five‐storey steel frame, subjected to different strengths of the Kobe earthquake, in shaking table tests. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
4.
Seismic stability of slopes has been traditionally analyzed with vertically propagated earthquake waves. However, for rock slopes, the earthquake waves might approach the outcrop still with a evidently oblique direction. To investigate the impact of obliquely incident earthquake excitations, the input method for SV and P waves with arbitrary incident angles is conducted, respectively, by adopting the equivalent nodal force method together with a viscous-spring boundary. Then, the input method is introduced within the framework of ABAQUS software and verified by a numerical example. Both SV and P waves input are considered herein for a 2D jointed rock slope. For the jointed rock mass, the jointed material model in ABAQUS software is employed to simulate its behavior as a continuum. Results of the study show that the earthquake incident angles have significance on the seismic stability of jointed rock slopes. The larger the incident angle, the greater the risk of slope instability. Furthermore, the stability of the jointed rock slopes also is affected by wave types of earthquakes heavily. P waves induce weaker responses and SV waves are shown to be more critical. 相似文献
5.
输入地震波的合理选择是影响结构时程分析结果可信度的重要因素。已有的最优双指标选波方案没有考虑地震动的持时和能量分布的影响。文中考虑地震动持时这一影响结构弹塑性反应的重要因素,以抗震结构的能量反应规律为基础讨论输入地震波选择问题,建议以地震动弹性总输入能反应作为补充指标的选波方案。 相似文献
6.
将粗糙粗集理论和神经网络原理结合起来,建立了基于粗集-神经网络的建筑物震害预测模型.首先运用粗糙集理论,根据原始样本建立决策表进行属性离散化、属性重要性排序、属性约简和分类规则的提取;然后将所提取的关键成分作为神经网络的输入练模型.实例研究表明,基于粗集-神经网络的多层砖房震害预测结果与实际震害基本吻合.该模型简化了神经网络结构,提高了训练速度和分类精度,还能对各因素对房屋震害的影响度进行分析. 相似文献
7.
详细的建筑结构特征参数是得到合理地震易损性分析结果的基础。本文给出了一种结合已有地震易损性分析成果,在具备有限特征参数的情况下,利用BP神经网络进行单体或群体结构震害等级推演的方法。以陕西省渭南市607栋设防砌体易损性评估结果为样本构建了一个3层BP神经网络模型,并对北京市海淀区近2万栋设防砌体不同地震烈度下的可能破坏状态进行推演,结果能够反映区域本地化特征,也与抗震设计目标和震害案例相符。该方法适用于少量特征参数下单体或群体结构的快速震害等级推演,可为相似烈度地区的建筑结构风险评估提供参考。 相似文献
8.
提出利用MATLAB人工神经网络工具箱建立基于贝叶斯正则算法的BP神经网络模型,以地震区多层砖房震害调查数据为因子的震害预测方法.神经网络模型输入震害因子包括建筑的层数、施工质量、房屋整体性等,输出值为建筑物在地震作用下的破坏程度.结果表明,本方法可以对多层砖房的震害样本进行预测并达到较理想的效果. 相似文献
9.
为了解四川德阳地下水位动态,进而分析地震前兆动态,本文设计了一个基于BP神经网络的地下水位预测系统。采用SWY-Ⅱ数字式水位仪对德阳地下水位数据进行采集。根据采集的2015年水位数据,利用BP神经网络对地下水位变化进行预测,以一年的采集数据进行训练和测试,采用3个输入节点、1个输出节点设计了BP神经网络结构。为了进一步验证本预测系统,本文对2017年7月1日—10月26日地下水位情况进行了预测。实验表明:该方案能有效实现地下水位的预测,为地震前兆工作提供可靠数据。 相似文献
10.
天然地震和爆破事件识别是地震监测预警的重要内容.近年来, 快速发展的深度学习算法以其强大的数据特征挖掘和图像识别能力, 能够较快并准确地约束地震事件属性.利用多输入卷积神经网络算法构建天然地震和爆破事件自动分类网络模型, 其中输入信息包括多台站地震波形和单台站的地震时频数据, 使得卷积神经网络同时获取事件的波形、频谱和极性特征.根据美国犹他州2012年记录到的天然地震和采石场爆破的观测资料, 构建深度学习的训练数据集并进行模型训练, 并据此判断2013—2016年间已知的天然地震和爆破事件.结果表明, 多输入卷积神经网络具有较高的识别精度, 识别率高达97%. 相似文献
11.
Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network (CNN) and recurrent neural network (RNN). The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90.7% in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92.6% and an error rate lower than 2%. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area. 相似文献
12.
丽江7.0级地震前,永胜台定点形变出现前所未有的巨大变化,东西向的变化较南北向的变化更显。展示了在源区附近7级大震前定点形变突出变化的一个典型震例。此震例对于今后研究观测点附近大震前的异常特征有重要的参考意义。 相似文献
13.
The neuro‐controller training algorithm based on cost function is applied to a multi‐degree‐of‐freedom system; and a sensitivity evaluation algorithm replacing the emulator neural network is proposed. In conventional methods, the emulator neural network is used to evaluate the sensitivity of structural response to the control signal. To use the emulator, it should be trained to predict the dynamic response of the structure. Much of the time is usually spent on training of the emulator. In the proposed algorithm, however, it takes only one sampling time to obtain the sensitivity. Therefore, training time for the emulator is eliminated. As a result, only one neural network is used for the neuro‐control system. In the numerical example, the three‐storey building structure with linear and non‐linear stiffness is controlled by the trained neural network. The actuator dynamics and control time delay are considered in the simulation. Numerical examples show that the proposed control algorithm is valid in structural control. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
14.
以探索深度卷积神经网络震级估算模型对2021年5月21日云南漾濞和5月22日青海玛多地震预警震级估算的可行性为目标,本文使用P波到达后不同时间窗下的特征参数作为深度卷积神经网络输入进行训练和验证,构建了1~40 s内不同时间窗下的深度卷积神经网络单台震级估算模型,并采用多台加权平均方法对玛多地震主震以及漾濞地震主震、前震和余震共11次地震事件进行实时震级估算.结果表明:在P波到达后1~40 s的不同时间窗下,随着时间窗的增加,深度卷积神经网络单台震级估算模型对训练集和验证集的震级估算误差和标准差逐渐减小,且逐渐趋于稳定;对于云南漾濞MS6.4主震与青海玛多MS7.4主震,大部分单台震级估算误差主要分布在±0.5震级单位内;通过多台加权平均方法计算预测震级,漾濞主震与玛多主震在首台触发后2 s时,震级估算结果分别是M6.0和M7.0;同时,随着首台触发后时间的增加,漾濞与玛多主震的震级估算逐渐接近实际震级,且在首台触发后9 s时,漾濞主震和玛多主震的震级估算分别是M6.2和M7.3;在首台触发后1 s时,对于玛多与漾濞地震的前震和余震,多台加权平均震级估算结果也主要分布在±0.5震级单位误差范围内.该方法为中国地震预警系统的震级估算方案提供了潜在可能. 相似文献
15.
本文把神经网络方法引进地震预报研究当中。使用地震频次,最大震级,平均震级,等价地震次数等多项地震活动性指标作为神经网络的输入,未来时段内的最大地震震级作为其输出,可以对某一固定地区的最大地震震级作出中近期预报。选用的神经网络模型为含两个中间层的前向模型,并采用BP算法。所得结果表明,用神经网络方法可以在一定精度范围内使震级预报的内检符合率达到100%,在本文的例子中,外推预报准确率达到60%以上。 相似文献
16.
针对结构损伤检测中损伤的识别、定位以及程度的标定这三个独立并按一定先后顺序进行的检测过程,提出了一种能将以上三者同时进行的联合检测方法。该方法首先利用经验模态分解(EMD)方法将三层钢筋混凝土剪切型结构在各种损伤工况下的顶层地震作用加速度响应分解为若干固有模态函数(IMF)分量,然后以此IMF分量和未经EMD分解的原始加速度响应数据来构造损伤标识量,作为特征参数依次输入到径向基函数神经网络(RBFNN)中进行损伤检测。给出了应用此方法的具体步骤,通过仿真实验证明了利用该方法进行结构损伤一次检测的可行性和有效性,结果表明,由加速度响应经EMD分解而得到的IMF分量输入到RBFNN中能够更为精确地一次检测出结构所有损伤信息,并且RBFNN在结构损伤损度大时具有更好的检测效果。 相似文献
17.
地震经济损失快速评估是应急救灾的重要决策依据。本文选取了震级、极震区烈度、极震区烈度和抗震设防烈度之差ΔI、人口密度、人均GDP等5个指标作为输入层节点,将地震灾害的直接经济损失作为输出层节点,通过对1996—2013年的地震灾害损失资料进行训练和仿真分析,构建了Elman神经网络地震经济损失快速估计模型。运用该模型,对近年来的7个破坏性地震的直接经济损失进行评估分析,评估结果和实际直接经济损失有较好的一致性,该方法为地震经济损失快速评估提供了一种新思路。 相似文献
18.
隔震结构的设计一般是采用时程分析算法进行的,故选择合适的地震波十分重要。而目前设计人员往往依据隔震前模型进行隔震设计,存在一定的不合理性。本文依据隔震前和隔震后两种不同的选波模型和对地震波反应谱控制频段的不同提出三种选波方案:方案一为对所选地震动的加速度反应谱在场地特征周期Tg附近的平台段和隔震前结构基本周期T1a所在下降段的控制;方案二为对在Tg附近的平台段和隔震后结构基本周期T1b所在下降段的控制;方案三为对所选地震动的加速度反应谱在Tg附近的平台段和隔震前、后两结构基本周期段的分别控制。通过对某五层混凝土框架隔震结构分别输入三种方案所选的20条地震动记录,对比隔震结构的水平向减震系数的离散性,分析罕遇地震作用下支座位移的合理性,证明方案三可以取得最优的计算结果,并提出一种基于规范设计反应谱不同频段的三频段控制选波方法。此外选取5个不同结构形式的工程算例验证三频段控制选波方法对于一般结构的适用性。 相似文献
19.
分别采用基于塑性铰法的空间梁柱单元分析程序和简化的DRAIN-20分析程序,对其主体巨型钢框架结构进行了考虑双重非线性的罕遇地震作用下的时程反应分析。分析中使用了三条典型地震波和一条当地人工地震波,分别得到了顶层位移和加速度时程曲线、层间位移包络值和塑性铰出现的位置与先后顺序。分析结果表明结构在体系设计上是合理的和在大震作用下具有足够的安全度。 相似文献
20.
Overturned shelves and fallen objects scattered on floors are one of the most frequently observed forms of nonstructural damage after earthquakes. The term ‘clutter’ is adopted in this study to represent this type of damage. Clutter may cause obstructions and thus hinder the use of a room. Making a seismic evaluation of clutter is a daunting task, due to the diversity of the types of shelves and objects and the way the objects are stored. Nonetheless, in order to achieve performance‐based seismic evaluation, especially for critical facilities such as hospitals, it is reasonable to undertake the estimation of clutter when examining the association between the performance of structural and nonstructural elements. Of particular interest in this paper is clutter caused by objects stored on medicine shelves in pharmacies, which are one of the critical departments for delivering post‐earthquake emergency care. Shake table tests were conducted on three conventional types of medicine shelves. Sinusoidal waves and earthquake motions were input uniaxially. The results of the tests using the sinusoidal wave input indicated the relationship between the input excitation intensity and clutter level expressed in scattering distance from the front of the shelf. Tests using earthquake motion input were then conducted and the results were compared with those for sinusoidal waves. Based on a comparison of the results from these tests, criteria for the seismic evaluation of clutter caused by medicine shelves due to earthquakes were proposed. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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