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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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基于BP神经网络模型的多层砖房震害预测方法 总被引：10，自引：2，他引：8
针对传统的基于地震烈度的建筑物震害预测方法的不足,本文以地震动峰值加速度作为建筑物震害预测的地震动指标,结合几次大地震中多层砖房的震害实例,提出了一种基于BP神经网络模型的建筑物震害预测方法,模型的输入为反映结构抗震性能的各类物理参数,输出为给定地震动峰值加速度下建筑物破坏状态的概率。研究表明:基于BP网络模型的多层砖房的震害预测结果与震害实例的实际情况比较吻合,本文的思路和方法可推广于其他不同类型的建筑结构的震害预测。 相似文献
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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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For the longitudinal seismic response analysis of a tunnel structure under asynchronous earthquake excitations, a longitudinal integral response deformation method classified as a practical approach is proposed in this paper. The determinations of the structural critical moments when maximal deformations and internal forces in the longitudinal direction occur are deduced as well. When applying the proposed method, the static analysis of the free-field computation model subjected to the least favorable free-field deformation at the tunnel buried depth is performed first to calculate the equivalent input seismic loads. Then, the equivalent input seismic loads are imposed on the integral tunnel-foundation computation model to conduct the static calculation. Afterwards, the critical longitudinal seismic responses of the tunnel are obtained. The applicability of the new method is verified by comparing the seismic responses of a shield tunnel structure in Beijing, determined by the proposed procedure and by a dynamic time-history analysis under a series of obliquely incident out-ofplane and in-plane waves. The results show that the proposed method has a clear concept with high accuracy and simple progress. Meanwhile, this method provides a feasible way to determine the critical moments of the longitudinal seismic responses of a tunnel structure. Therefore, the proposed method can be effectively applied to analyze the seismic response of a long-line underground structure subjected to non-uniform excitations. 相似文献