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1.
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
2.
为解决建筑物震害信息提取自动化程度不高的问题,本文将全卷积神经网络应用于建筑物震害遥感信息提取。以玉树地震后获取的玉树县城区0.2m分辨率航空影像作为建筑物震害信息提取试验数据源,将试验区地物划分为倒塌建筑物、未倒塌建筑物和背景3类。对427个500×500像素的子影像进行人工分类与标注,选取393个组成训练样本集,34个用于验证。利用训练样本集对全卷积神经网络进行训练,采用训练后的网络对验证样本进行建筑物震害信息提取及精度评价。研究结果表明:建筑物震害遥感信息提取总体分类精度为82.3%,全卷积神经网络方法能提高信息提取自动化程度,具有较好的建筑物震害信息提取能力。 相似文献
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Evaggelos Ntotsios Costas Papadimitriou Panagiotis Panetsos Grigorios Karaiskos Kyriakos Perros Philip C. Perdikaris 《Bulletin of Earthquake Engineering》2009,7(2):469-483
A bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors.
Sophisticated structural identification methods, combining information from the sensor network with the theoretical information
built into a finite element model for simulating bridge behavior, are incorporated into the system in order to monitor structural
condition, track structural changes and identify the location, type and extent of damage. This work starts with a brief overview
of the modal and model identification algorithms and software incorporated into the monitoring system and then presents details
on a Bayesian inference framework for the identification of the location and the severity of damage using measured modal characteristics.
The methodology for damage detection combines the information contained in a set of measurement modal data with the information
provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in
the structure. The effectiveness of the damage detection algorithm is demonstrated and validated using simulated modal data
from an instrumented R/C bridge of the Egnatia Odos motorway, as well as using experimental vibration data from a laboratory
small-scaled bridge section. 相似文献
5.
This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
6.
Assessing reparability: simple tools for estimation of costs and performance loss of earthquake damaged reinforced concrete buildings 
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下载免费PDF全文 Maria Polese Marco Di Ludovico Marco Marcolini Andrea Prota Gaetano Manfredi 《地震工程与结构动力学》2015,44(10):1539-1557
Tools for assessing building reparability via the estimation of expected performance loss (PL) and associated costs for repair of existing RC building classes damaged by an earthquake are presented. The assessment approach relies on the availability of a number of suitably developed: (i) capacity curves for representative building classes; (ii) curves relating global ductility demand μ to the expected PL for the same classes; and (iii) PL–cost for repair relationship calibrated on database collecting cost data of more than 2300 buildings damaged after 2009 L'Aquila earthquake. The tools are developed applying a simplified procedure involving the simulated design of existing building classes, the assumption of predefined collapse mechanism types and the analyses of the seismic behavior of equivalent SDOF systems representative of ‘intact’ and ‘damaged’ structures after an earthquake. The use of these tools may give useful preliminary indications to decision makers for establishing reparability priorities in the aftermath of damaging earthquakes or to insurance companies to value sound insurance premium for existing building classes. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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作为深度学习方法的一种,长短时记忆神经网络(LSTM)是一种信号处理的重要方法.本文基于实际观测地电场数据来合成训练集,对特定结构的长短时记忆神经网络进行训练,将训练所得网络对测试集数据进行测试后,将网络应用至实际观测数据.结果显示,经过训练的网络很好地学到了训练集样本的特征,对测试集数据的信噪比压制了约20 dB,并过滤了人为添加的特定频率的干扰成分,对实际观测数据处理后得到明显的日变、半日变以及半月变、月变、半年变、年变等潮汐响应,表明长短时记忆神经网络可以有效应用于地电场数据处理研究. 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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Evaluation of displacement‐based vulnerability assessment methodology using observed damage data from Christchurch 下载免费PDF全文
下载免费PDF全文 On 22 February 2011, Christchurch City experienced a destructive magnitude (Mw) 6.2 aftershock following the main event of magnitude (Mw) 7.1 on the 4 September 2010. Severe damage was inflicted on the building stock, particularly within the central business district (CBD) of Christchurch. The strong motion stations around the CBD region and extensive building damage survey information from the Christchurch City Council provided a unique opportunity to calibrate a theoretical regional vulnerability assessment model developed and refined to be applicable for New Zealand (NZ) buildings. In this study, data from the building safety evaluation survey conducted by Christchurch City Council are synthesised and processed to extract details on building typologies in the CBD region and the colour tagging assigned to each building depending on the degree of damage. A displacement‐based framework is used to carry out vulnerability assessment for Christchurch buildings to estimate damage sustained under the recorded ground motions in the February event. As the damage survey indicators were ‘colour tags’, a mapping scheme has been explored to link the observed colour tagging damage statistics with ‘drift‐based damage limit states’ adopted in the theoretical approach. A sensitivity analysis is carried out to calibrate the mapping scheme, which can provide estimates of proportions of buildings likely to fall in different colour regimes when used in conjunction with the proposed vulnerability assessment methodology. It is shown that the methodology is reasonably robust, thereby increasing the confidence in using this approach to predict seismic vulnerability of building stock in NZ. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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In order to investigate ways of reducing vibrations of building structures subjected to excitation acting on intermediate storey, active vibration controls are conducted with active control devices installed on different floors of the structure, and the effective location of control devices is also investigated. In this paper, we propose a new ‘Discrete‐Optimizing Control Method’ for vibration control. The control forces are determined analytically which makes the ‘discrete‐index function’ minimum. Through numerical simulation, the Discrete‐Optimizing Control Method is proved to be an effective control method. The response reduction effects are best when the control devices are concentrated on the adjacent three floors of the vibration source. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
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I. Popova A. Rozhnoi M. Solovieva B. Levin V. Chebrov 《Izvestiya Physics of the Solid Earth》2016,52(2):305-317
In this paper, we suggest a technique for forecasting seismic events based on the very low and low frequency (VLF and LF) signals in the 10 to 50 Hz band using the neural network approach, specifically, the error back-propagation method (EBPM). In this method, the solution of the problem has two main stages: training and recognition (forecasting). The training set is constructed from the combined data, including the amplitudes and phases of the VLF/LF signals measured in the monitoring of the Kuril-Kamchatka region and the corresponding parameters of regional seismicity. Training the neural network establishes the internal relationship between the characteristic changes in the VLF/LF signals a few days before a seismic event and the corresponding level of seismicity. The trained neural network is then applied in a prognostic mode for automated detection of the anomalous changes in the signal which are associated with seismic activity exceeding the assumed threshold level. By the example of several time intervals in 2004, 2005, 2006, and 2007, we demonstrate the efficiency of the neural network approach in the short-term forecasting of earthquakes with magnitudes starting from M ≥ 5.5 from the nighttime variations in the amplitudes and phases of the LF signals on one radio path. We also discuss the results of the simultaneous analysis of the VLF/LF data measured on two partially overlapping paths aimed at revealing the correlations between the nighttime variations in the amplitude of the signal and seismic activity. 相似文献
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J. Wang 《Pure and Applied Geophysics》2002,159(5):1021-1041
—?A neural network module has been implemented in the Prototype International Data Centre (PIDC) for automated identification of the initial phase type of seismic detections. Initial training of the neural networks for stations of the International Monitoring System (IMS) requires considerable effort. While there are many seismic phases in the analyst-reviewed database that can be assumed as the ground-truth resource of the initial phase type of Teleseism (T), Regional P (P), and Regional S (S), no ground-truth database of noise (N) is available. To reduce analyst effort required in building a ground-truth database, an “Adaptive Training Approach” is proposed in this paper. This approach automatically selects training patterns to take advantage of the learning ability of neural networks and information on the accumulated observation database. Using this approach, neural networks were trained on the data provided by station STKA, Australia. The performance of automated phase identification has been improved significantly by the retrained neural networks. This approach is also validated by comparison with the performance using the ground-truth noise database. 相似文献
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《Soil Dynamics and Earthquake Engineering》2000,20(1-4)
The paper deals with an application of neural networks for detection of natural periods of vibrations of prefabricated, medium height buildings. The neural network technique is also used to simulate the dynamic response at selected floor of one of the analysed buildings subject to seismic loading induced by explosives in a nearby quarry. Both the training and testing patterns were formulated on the basis of measurements performed on actual structures. The results of neural network identification of natural periods of the considered buildings obtained with different soil, geometrical and stiffness parameters are compared with the results of experiments. The application of back-propagation neural networks enables us to identify the natural periods of the buildings with accuracy quite satisfactory for engineering practice. The experimental and generated data of vibration displacements are compared and much clearer comparison is given on the phase plane: displacements versus velocities. It was stated that a good generalization takes place both with respect to displacements and velocities. 相似文献
14.
Conventional design methodology for the earthquake‐resistant structures is based on the concept of ensuring ‘no collapse’ during the most severe earthquake event. This methodology does not envisage the possibility of continuous damage accumulation during several not‐so‐severe earthquake events, as may be the case in the areas of moderate to high seismicity, particularly when it is economically infeasible to carry out repairs after damaging events. As a result, the structure may collapse or may necessitate large scale repairs much before the design life of the structure is over. This study considers the use of design force ratio (DFR) spectrum for taking an informed decision on the extent to which yield strength levels should be raised to avoid such a scenario. DFR spectrum gives the ratios by which the yield strength levels of single‐degree‐of‐freedom oscillators of different initial periods should be increased in order to limit the total damage caused by all earthquake events during the lifetime to a specified level. The DFR spectra are compared for three different seismicity models in case of elasto‐plastic oscillators: one corresponding to the exponential distribution for return periods of large events and the other two corresponding to the lognormal and Weibull distributions. It is shown through numerical study for a hypothetical seismic region that the use of simple exponential model may be acceptable only for small values of the seismic gap length. For moderately large to large seismic gap lengths, it may be conservative to use the lognormal model, while the Weibull model may be assumed for very large seismic gap lengths. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
15.
Artificial neural networks for removal of couplings in airborne transient electromagnetic data 下载免费PDF全文
下载免费PDF全文 Kristoffer K. Andersen Casper Kirkegaard Nikolaj Foged Anders V. Christiansen Esben Auken 《Geophysical Prospecting》2016,64(3):741-752
Modern airborne transient electromagnetic surveys typically produce datasets of thousands of line kilometres, requiring careful data processing in order to extract as much and as reliable information as possible. When surveys are flown in populated areas, data processing becomes particularly time consuming since the acquired data are contaminated by couplings to man‐made conductors (power lines, fences, pipes, etc.). Coupled soundings must be removed from the dataset prior to inversion, and this is a process that is difficult to automate. The signature of couplings can be both subtle and difficult to describe in mathematical terms, rendering removal of couplings mostly an expensive manual task for an experienced geophysicist. Here, we try to automate the process of removing couplings by means of an artificial neural network. We train an artificial neural network to recognize coupled soundings in manually processed reference data, and we use this network to identify couplings in other data. The approach provides a significant reduction in the time required for data processing since one can directly apply the network to the raw data. We describe the neural network put to use and present the inputs and normalizations required for maximizing its effectiveness. We further demonstrate and assess the training state and performance of the network before finally comparing inversions based on unprocessed data, manually processed data, and artificial neural network automatically processed data. The results show that a well‐trained network can produce high‐quality processing of airborne transient electromagnetic data, which is either ready for inversion or in need of minimal manual processing. We conclude that the use of artificial neural network scan significantly reduce the processing time and its costs by as much as 50%. 相似文献
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多层及高层框架结构地震损伤诊断的神经网络方法 总被引：12，自引：4，他引：12
本文提出了强震后多层及高层框架结构地震损伤诊断的神经网络方法。文中在提出有结点损伤的梁柱有限元刚度矩阵的基础上，建立了有结点损伤框架结构的有限元模型。通过完好结构和有损伤结构的有限元分析，获取二者应变模态差值作为损伤标识量，并输入径向基（RBF）神经网络进行训练，得到了框架结构结点损伤诊断的神经网络系统。数值仿真分析结果表明，此神经网络可以对多层及高层框架结构结点各种程度的损伤做出成功诊断。 相似文献
17.
A Bayesian probabilistic approach for damage detection has been proposed for the continuous monitoring of civil structures (Sohn H, Law KH. Bayesian probabilistic approach for structure damage detection. Earthquake Engineering and Structural Dynamics 1997; 26 :1259–1281). This paper describes the application of the Bayesian approach to predict the location of plastic hinge deformation using the experimental data obtained from the vibration tests of a reinforced‐concrete bridge column. The column was statically pushed incrementally with lateral displacements until a plastic hinge is fully formed at the bottom portion of the column. Vibration tests were performed at different damage stages. The proposed damage detection method was able to locate the damaged region using a simplified analytical model and the modal parameters estimated from the vibration tests, although (1) only the first bending and first torsional modes were estimated from the experimental test data, (2) the locations where the accelerations were measured did not coincide with the degrees of freedom of the analytical model, and (3) there existed discrepancies between the undamaged test structure and the analytical model. The Bayesian framework was able to systematically update the damage probabilities when new test data became available. Better diagnosis was obtained by employing multiple data sets than just by using each test data set separately. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
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We present a framework for the seismic risk assessment of water supply networks, operating in either normal or abnormal conditions. We propose a methodology for assessing the reliability of water pipe networks combining data of past non‐seismic damage and the vulnerability of the network components against seismic loading. Historical data are obtained using records of damages that occur on a daily basis throughout the network and are processed to produce‘survival curves’, depicting their estimated survival rate over time. The fragility of the network components is assessed using the approach suggested in the American Lifelines Alliance guidelines. The network reliability is assessed using graph theory, whereas the system network reliability is calculated using Monte Carlo simulation. The methodology proposed is demonstrated both on a simple, small‐scale, network and also on a real‐scale district metered area from the water network of the city of Limassol, Cyprus. The proposed approach allows the estimation of the probability that the network fails to provide the desired level of service and allows the prioritization of retrofit interventions and of capacity‐upgrade actions pertaining to existing water pipe networks. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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采用多尺度分割和深度学习相结合的方法对震后倾斜摄影三维影像建筑物震害信息进行提取,获取建筑物的屋顶和墙体多种破坏信息。以2017年九寨沟MS7.0地震后倾斜摄影三维影像为例,依据三维影像建筑物顶面和墙体等进行样本的多尺度分割,样本分为完好建筑物面、破坏建筑物面、其它地物和背景等三类,选取211个100×100像素的样本集对卷积神经网络模型进行训练,采用训练后的模型提取灾区千古情风景区和漳扎镇小学的建筑物震害信息,并将提取结果与目视解译结果进行精度对比,结果显示:破坏建筑物面提取精度分别为65.5%和71.1%,总体分类精度分别为82.1%和84.1%,卡帕（Kappa）系数分别为68.7%和64.9%,表明该方法在倾斜摄影三维影像建筑物震害提取方面具有一定的优势。 相似文献
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Vibration measurements were performed on two adjacent, three-storey reinforced concrete frame buildings with hollow clay brick infill panels. The first building was a bare frame and the second one was a similar frame infilled with brick panels. The fundamental period for the infilled frame building was much smaller than that of the bare frame building. Using shear beam lumped mass models and the vibration data the actual lateral stiffness of both buildings was identified. The lateral stiffness of the infilled frame building was found to be seven times that of the bare frame building. Four numerical models of the infilled frame building were constructed. The frame and floors were represented using an experimentally validated model and the infill panels by one of three commonly used ‘equivalent diagonal truss’ models or by plane stress finite elements. Only the plane stress finite element model produced a reasonable agreement with the experimental results. Copyright © 1999 John Wiley & Sons, Ltd. 相似文献