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钢管混凝土短柱支座隔震性能研究 总被引：3，自引：0，他引：3
本文提出一种钢管混凝土短柱隔震支座座，通过伪静力试验测定了短柱支座的恢复力特性，给出了有关恢复力的某些特征参数的表达式；对一座廿层砌体主房屋进行了非线性地震反应分析，考察了短柱支座的特性及其隔震效果；通过模型振动台试验，进一步验证了短柱支座的耳震效果和计算模型的准确性。 相似文献
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摩擦-钢管混凝土短柱复合隔震支座性能试验与隔震分析 总被引：3，自引：0，他引：3
作者在钢管混凝土柱支座隔震房屋实验研究中发现,隔震支座中上盖与底座的接触无论如何处理,摩擦力总是存在的．因此,隔震支座实际上是摩擦耗能与钢管混凝土耗能复合减震系统。本文通过对单个和一组隔震支座的实验研究,确定了短柱支座的恢复力模型,采用了高阶单步算法分析了装有此类支座隔震结构的地震反应,验证了复合隔震支座良好的隔震性能及隔震效果。 相似文献
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作者在钢管混凝土柱支座隔震房屋实验研究中发现,隔震支座中上盖与底座的接触无论如何处理,摩擦力总是存在的。因此,隔震支座实际是摩擦耗能与钢管混凝土耗能复合减震系统。本文通过对单个和一组隔震支座的实验研究,确定了短柱支座的恢复力模型,采用了高阶单步算法分析了装有此类支座隔震结构的地震反应,验证了复合隔震支座的良好的隔震性能及隔震效果。 相似文献
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钢管混凝土短柱隔震装置的试验与隔震结构仿真分析 总被引：3，自引：0，他引：3
本文阐述了钢管混凝土短柱装置的工作原理,给出了装置的构造,对隔震装置的试验结果进行了详细分析,并对传统结构模型与隔震结构模型的对比实验作了仿真计算与分析．从计算结果可知,钢管混凝土基底隔震装置具有良好的隔震性能和推广使用价值。 相似文献
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耗能-隔震柔性底层钢管混凝土-钢筋混凝土组合框架的实验研究 总被引：1，自引：1，他引：1
本文用钢管混凝土柱作为结构底层耗能柱、用承重墙和隔震器控制结构底层倒塌破坏,从而提出了一种新的耗能-隔震柔性底层结构体系。通过本文12根钢管混凝土柱和文献[7]中7根钢管混凝土柱的低周疲劳实验,初步确定了钢管混凝土柱地震损伤模型的参数;通过两个钢管混凝土-钢筋混凝土三层框架模型和一个纯钢筋混凝土三层框架模型的拟动力实验,研究和比较了两类结构体系的地震损伤。 相似文献
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变刚度钢管混凝土短柱隔震装置的性能研究 总被引：7，自引：1，他引：6
本文对变刚度钢管混土短柱震的滞回收性进行了理论分析和试验验证。对装置的隔震作用以及耗能特性进行了讨论通过低周反复荷载试验,证了分析的正确性。同时,试验还证明,变刚度钢管混凝土短柱震装置能很好的地耗散能量,从而减小地震反应,作者认为,该是一种价格性能比较优越的隔震装置,在工程实践中将有广阔的应用与推广前景。 相似文献
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基础隔震房屋模型振动台试验研究 总被引：5，自引：0，他引：5
本文结合日本在建的基础隔震实际工程,采用中国有关工厂生产的铅芯橡胶支座的作为基础隔震支座,进行了基础隔震房屋模型和基础固家房屋模型模拟地震动台试验,并引入能量分析方法对两种试验结果进行了分析比较。结果表明,基础隔震模型隔震效果明显,隔震层滞回变能有效吸收地震动入能量,减小模型结构的塑性变形和累积损伤。 相似文献
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单层框架建筑模型基础隔震实验研究 总被引：1，自引：0，他引：1
对带叠层橡胶隔震基础的钢筋混凝土单层框架模型进行了振动台地震模拟试验,比较了隔震结构与非隔震结构的地震反应,结果表明,用叠层橡胶支座隔震后,框架模型的地震反应比不隔震时大大减小,证明叠层橡胶支座基础隔震技术是一种既有效又很安全的减震措施。 相似文献
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滑动可预报模型，时间可预报模型和双限随机应力水平模型是三种相关的地震预报唯象模型。对于一个指定地区，用哪种模型更合理呢？为解决这个问题，本文提出了两地震预报模式适用程度的数值分析方法，利用这些方法可以合理地鉴别出氖要用的地震预报唯象模型是否适用于指定地区的地震预报分析。 相似文献
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Huth Radan Mládek Richard Metelka Ladislav Sedlák Pavel Huthová Zuzana Kliegrová Stanislava Kyselý Jan Pokorná Lucie Halenka Tomáš Janoušek Martin 《Studia Geophysica et Geodaetica》2003,47(4):863-873
This note summarizes results of the first integration of regional numerical weather prediction model ALADIN in a climate mode. The ALADIN model, developed in an international cooperation led by Météo France, is operationally used for weather prediction. The grid step of the model is 12 km; the integration domain covers a major part of Europe. A one-month-long run has been performed with this model on observed boundary conditions (represented by assimilations by the global model ARPEGE). It is demonstrated that no excessive error is generated and accumulated in the model during the integration; hence the model is integrable for extended time periods and may serve a basis for a development towards a regional climate model. 相似文献
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Although the nonlinear power form model structure is widely accepted by practitioners in the flood regionalization modelling, there is a lack of studies on whether there is a room for further improvement, and if the answer is yes, what should be done to explore alternative model structures. A framework is proposed in this study towards investigating this issue by the following steps: (i) a universal data‐driven model is utilized to see if there is a room for improvement compared with the conventional model, and (ii) if improvement is achieved, this means that there should exist more effective model structures than the current form. However, because the universal data‐driven models are usually opaque, more explicit model structures should be explored, which are convenient for practical usage. In this study, the proposed framework is applied in a case study using the catchment characteristics from the Flood Estimation Handbook in conjunction with the gamma test, support vector machine (SVM) and genetic programming (GP). First, the gamma test is used for the purpose of input variables selection where no model structure needs to be defined as a priori, and therefore, the result can be applied to any model structures for model building. Second, an SVM, which is a powerful data‐driven nonlinear model capable of modelling a variety of nonlinear systems, is applied to the index flood model for the first time. Once the best model is determined using those two data‐driven tools, GP is employed to find an alternative model structure. As the SVM is not formulated for producing explicit model functional form, the GP offers an advantage at this point where it can infer an explicit mathematical model functional form. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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A two-way nested-grid ocean-circulation model for the Meso-American Barrier Reef System 总被引：2，自引：0，他引：2
A two-way nested-grid ocean-circulation model is developed for the Meso-American Barrier Reef System (MBRS), using a newly developed two-way interactive nesting technique. The unique feature of this new nesting technique is its use of the semi-prognostic method (Sheng et al. 2001) to exchange information between different grids through the model momentum equations. The nested-grid model for the MBRS has a fine-resolution inner model embedded in a coarse-resolution outer model. The outer model is the western Caribbean Sea model developed by Sheng and Tang (2003), with a horizontal resolution of roughly 19 km. The inner model domain covers the northwest Caribbean Sea (NWCS) between 79°W and 89°W and between 15.5°N and 22°N, with a horizontal resolution of roughly 6 km. The nested-grid ocean model is initialized with the January mean temperature and salinity and forced by the monthly mean COADS (comprehensive ocean-atmosphere data set) wind stress and surface heat flux. The model sea-surface salinity is restored to the monthly mean climatology. The nested-grid model is integrated for 2 years and the second-year model results are presented in this paper. The model-calculated annual-mean near-surface currents over the NWCS agree reasonably well with the time-mean near-surface currents inferred by Fratantoni (2001) from trajectories of the satellite-tracked 15-m drogued drifters in the 1990s. The two-way nested model is also used to quantify the role of local wind stress, local density gradients and boundary forcings of the outer model in driving the annual-mean circulation in the region.Responsible Editor: Phil Dyke 相似文献
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Coupled stress release model is proposed in the paper considering the interaction between different parts based on stress
release model by Vere-Jones, and is used to historical earthquake data from North China. The results by this model are compared
with the results by original stress release model using AIC criterion. The results show that coupled stress release model
is better than original model. 相似文献
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A key point in the application of multi‐model Bayesian averaging techniques to assess the predictive uncertainty in groundwater modelling applications is the definition of prior model probabilities, which reflect the prior perception about the plausibility of alternative models. In this work the influence of prior knowledge and prior model probabilities on posterior model probabilities, multi‐model predictions, and conceptual model uncertainty estimations is analysed. The sensitivity to prior model probabilities is assessed using an extensive numerical analysis in which the prior probability space of a set of plausible conceptualizations is discretized to obtain a large ensemble of possible combinations of prior model probabilities. Additionally, the value of prior knowledge about alternative models in reducing conceptual model uncertainty is assessed by considering three example knowledge states, expressed as quantitative relations among the alternative models. A constrained maximum entropy approach is used to find the set of prior model probabilities that correspond to the different prior knowledge states. For illustrative purposes, a three‐dimensional hypothetical setup approximated by seven alternative conceptual models is employed. Results show that posterior model probabilities, leading moments of the predictive distributions and estimations of conceptual model uncertainty are very sensitive to prior model probabilities, indicating the relevance of selecting proper prior probabilities. Additionally, including proper prior knowledge improves the predictive performance of the multi‐model approach, expressed by reductions of the multi‐model prediction variances by up to 60% compared with a non‐informative case. However, the ratio between‐model to total variance does not substantially decrease. This suggests that the contribution of conceptual model uncertainty to the total variance cannot be further reduced based only on prior knowledge about the plausibility of alternative models. These results advocate including proper prior knowledge about alternative conceptualizations in combination with extra conditioning data to further reduce conceptual model uncertainty in groundwater modelling predictions. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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Ming-Chang Wu Gwo-Fong Lin Hsuan-Yu Lin 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(7):1661-1671
The model performance is usually influenced by the quality of the data used in model construction. If the model performance is less affected by data quality, the resulting estimates will be more reliable. In this paper, the variation in model performance due to different data quality is explored in a field-scale application. Hence, two models, the proposed support vector machine (SVM) based model and the Stephen and Stewart (SS) model, are employed for daily estimation of evaporation at an experiment station. Five scenarios corresponding to different data qualities are designed to evaluate the effect of data quality on model performance. Additionally, the most effective meteorological variables influencing evaporation are obtained by a systematic input determination process. These most effective meteorological variables are used as inputs to the SVM-based model. The results show that the model performance decreases as the data quality decreases (i.e. the percentage of missing data increases). However, the estimation accuracy of SVM-based models is still better than that of the SS model. Moreover, the variation of model performance of the SVM-based model is smaller than that of the SS model. That is, the negative impact of different data quality is effectively decreased by using the SVM-based model instead of the SS model. 相似文献
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Model predictive control (MPC) of open channel flow is becoming an important tool in water management. The complexity of the prediction model has a large influence on the MPC application in terms of control effectiveness and computational efficiency. The Saint-Venant equations, called SV model in this paper, and the Integrator Delay (ID) model are either accurate but computationally costly, or simple but restricted to allowed flow changes. In this paper, a reduced Saint-Venant (RSV) model is developed through a model reduction technique, Proper Orthogonal Decomposition (POD), on the SV equations. The RSV model keeps the main flow dynamics and functions over a large flow range but is easier to implement in MPC. In the test case of a modeled canal reach, the number of states and disturbances in the RSV model is about 45 and 16 times less than the SV model, respectively. The computational time of MPC with the RSV model is significantly reduced, while the controller remains effective. Thus, the RSV model is a promising means to balance the control effectiveness and computational efficiency. 相似文献