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131.
机房是移动通信基站的重要组成部分,其地震易损性将决定基站在震后的功能状态,即基站所辖范围内的移动通信服务在震后是否可以正常使用。在对我国北方某市典型落地通信基站机房进行实地考察的基础上,确定了影响机房地震后使用功能的重要设施(即基站板房、内部走线架、通信机柜和蓄电池组);随后采用有限元软件ABAQUS分别建立了这些设施的数值模型,通过Pushover分析确定了每种设施的损伤模式、损伤水平评价指标及其数值;通过IDA分析得到了每种设施的抗震性能,并通过对IDA分析结果的统计得到了这些重要设施的地震易损性曲线;最后,给出了基于故障树模型的典型通信基站机房震后功能评估的方法。该工作将作为基本环节用于城市及地区移动通信系统的地震后功能状态评估与预测。 相似文献
132.
该研究在温州两个深软土层场地上完成了剪切波速测试误差专项实验,统计拟合了误差分布规律并研究了其对PGA的影响,得到结论:每米深度上的实测误差基本符合标准正态分布,温州地区深软场地的测试误差与我国其他几个地区覆盖土层较浅场地上的每米实测误差程度基本相同;两个场地上各深度的误差标准差大致相同,约为16%;实测误差对PGA影响明显,误差取1倍标准差时PGA的变化程度可能达到30%,取2倍标准差时可能高达45%;PGA的变化程度受输入地震动的频率特性制约,长周期频率成分对深软场地计算结果的影响明显。 相似文献
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134.
本文以振动台试验方式研究了打包带加固对西藏典型单层混凝土砌块房屋抗震性能的影响,同时考虑了结构平面布置、抗震设防及砌筑质量三个因素。选取3种西藏地区民居的经典户型制作了7个1∶3缩尺模型进行振动台试验,模型分别为3个无打包带加固模型、3个有打包带加固模型以及1个加固一半的模型。试验结果表明打包带加固墙体能明显提高墙体的整体性,减轻墙体震损;无打包带加固但砌筑质量好的单层民居抗震能力较好,基本满足当地9度设防的要求;高烈度下平面布置不规则的结构容易因扭转作用而发生破坏,圈梁、构造柱也在高烈度下发挥较大作用。打包带加固技术作为一种经济实用的加固技术可在西藏地区进行推广。 相似文献
135.
为了缓解联肢剪力墙中小跨高比连梁发生低延性的剪切破坏,增强连梁的变形和耗能能力,可在单连梁中轴线位置设置半通缝并配置交叉斜筋,形成半通缝连梁。本文完成了的对7种连梁的模拟,分析了在小跨高比、低周反复荷载作用条件下不同类型带楼板连梁的承载力、变形能力、刚度退化和耗能能力以及不同跨高比、不同开缝位置对带楼板半通缝连梁抗震性能的影响。结果表明:楼板会使半通缝连梁的剪压比增大,延性下降;但相比于普通连梁和双连梁,半通缝连梁具有较好的变形能力和承载力,可在实际中推广。 相似文献
136.
137.
为仅利用结构损伤状态的柔度矩阵对结构进行损伤程度识别,先对损伤状态的均匀荷载面曲率曲线进行最小二乘法拟合。根据曲率曲线差判断结构的损伤位置,对损伤位置的点进行剔除后,再利用未损伤位置上的点进行局部最小二乘法拟合,代替损伤前的均匀荷载面曲率曲线,用于结构的损伤定位与定量。通过一简支梁数值算例,先以理论的二次多项式进行拟合,考虑单损伤和多损伤的情况,进行损伤识别分析,再分析多项式次数、测点数目以及不同噪声水平对损伤定量精度的影响。结果表明:在一定范围内,次数越高拟合误差越小,但差别不明显,采用理论的二次多项式拟合即可满足结构损伤识别要求,无噪声的情况下,测点数目减半不影响损伤识别的精度,该方法具有一定的抗噪声能力。 相似文献
138.
Fabio?OrianiEmail authorView authors OrcID profile Raj?Mehrotra Grégoire?Mariethoz Julien?Straubhaar Ashish?Sharma Philippe?Renard 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):321-340
Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is the modified Markov model, belonging to the family of Markov-chain based techniques, which introduces non-stationarity in the chain parameters to preserve the long-term behavior of rainfall. The second technique is direct sampling, based on multiple-point statistics, which aims at simulating a complex statistical structure by reproducing the same data patterns found in a training data set. The two techniques are compared by first simulating a synthetic daily rainfall time-series showing a highly irregular alternation of two regimes and then a real rainfall data set. This comparison allows analyzing the efficiency of different elements characterizing the two techniques, such as the application of a variable time dependence, the adaptive kernel smoothing or the use of low-frequency rainfall covariates. The results suggest, under different data availability scenarios, which of these elements are more appropriate to represent the rainfall amount probability distribution at different scales, the annual seasonality, the dry-wet temporal pattern, and the persistence of the rainfall events. 相似文献
139.
Vahid?NouraniEmail authorView authors OrcID profile Afshin?Partoviyan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):545-562
Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds. 相似文献
140.
Amit?KumarEmail authorView authors OrcID profile M.?P.?Sharma Tao?Yang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(11):3183-3193
The identification and accurate quantification of sources or sinks of greenhouse gas (GHG) have become a key challenge for scientists and policymakers working on climate change. The creation of a hydropower reservoir, while damming a river for power generation, converts the terrestrial ecosystems into aquatic and subsequently aerobic and anaerobic decomposition of flooded terrestrial soil organic matter resulting in the emission of significant quantity of GHG to the atmosphere. Tropical/subtropical hydropower reservoirs are more significant sources of GHG compared to boreal or temperate one. This paper aims to estimate the emission factor (gCO2eq./kWh) and net GHG emission from Koteshwar hydropower reservoir in Uttarakhand, India. Further, estimated GHG are compared with those from global reservoirs located in the same eco-region so that its impact could be timely minimized/mitigated. Results have shown that emission factor and net GHG emission of Koteshwar reservoir are, respectively, estimated as 13.87 gCO2eq./kWh and 167.70 Gg C year?1 which are less than other global reservoirs located in the same eco-region. This information could be helpful for the hydropower industries to construct reservoirs in tropical eco-regions. 相似文献