结构随机响应的一种改进的概率谱叠加方法

目前有一些关于多自由度结构体系的地震响应中响应峰值按降序(即由大到小的顺序)排列时的峰值统计性质的研究。本文对这些研究中的概率方法提出一些改进。由于这些改进，概率谱叠加方法在实际应用中就变得更准确和实用。本文以五层的结构模型为例，计算其在埃尔森特罗(1940)地震激励下的响应，并与时程分析结果进行比较。结果表明，改进的概率谱叠加方法能够谁确的计算较高几阶重要响应峰值的幅值。     

概率赋范空间的S-凸和K-凸

文献[1][2]认为M－PN空间是局部凸的。通过定理说明这个结论对t-模T（a,b)≥min(a,b)时成立。又通过反例表明，当t-模型T（a,b)=max(a b-1,0）时无论邻域Nθ（ε，λ）概率有界还是概率半有界结论均不成立。还讨论了Takahashi凸性并对S－凸和K－凸作了比较。     

长期非平稳荷载调谐质量减振阻尼器优化设计

研究外荷载为长期非平稳随机过程。考虑长期荷载的特性 ,采用 1个概率谱密度函数来反映长期非平稳随机荷载及其特征 ;概率谱密度函数是基于大量的一般谱密度函数的统计特性获得。以延长结构的抗疲劳使用寿命为目标函数 ,提出了调谐质量减振阻尼器的优化设计方法 ,这在实际工程中有着极为广阔的应用前景。本文旨在从理论上发展长期非平稳随机荷载作用下调谐质量减振阻尼器的优化设计方法 ;文中采用长期波浪实测数据 ,给出了 1个数值算例说明整个设计过程。     

某地区黑云角闪岩应力松弛特性研究

岩体工程失稳往往由工程岩体蠕变、应力松弛等时效力学行为下的损伤演化所致。本文以某坝基黑云角闪岩为研究对象,进行了2组单轴应力松弛试验,获取了不同应力条件下的应力松弛及其相应的应力松弛速率曲线。试验结果表明：根据应力松弛曲线形态可分为快速松弛,减速松弛及应力稳定3个阶段,应力松弛的演化与岩石裂隙的萌生和流变变形密切相关。在此基础上,提出了采用非线性Maxwell模型描述岩石应力松弛特性,模拟结果与试验结果吻合较好。本文研究可为类似工程的长期安全稳定性研究提供理论依据。     

基于异步迭代算法的冲击地压预测

采用三层BP神经网络方法对冲击地压建立了数学模型。网络的训练算法采用具有松弛因子的动态异步迭代法,该算法在克服网络的麻痹现象及局部极小问题上都优于目前常用的训练方法,因而,采用此算法对网络进行了训练及震级的预报。基于新汶矿务局华丰煤矿1999～2000年的冲击地压现场监测数据,把冲击地压的能量、产生波的幅值、频次做为输入数据,相应期间的最大震级为输出数据,组成神经网络的训练样本及测试样本,对原始数据进行了数学预处理,网络结构采用了输入层3个结点,中间层7个结点,输出层1个结点的前向神经网络;网络最终的训练误差为0.06,预测结果的相对误差率平均为 9.2 %,预测效果比较理想。     

三峡工程永久船闸高边坡岩体卸荷松弛特征研究

三峡工程永久船闸高边坡岩体卸荷松弛特征明显,采用地质调查、钻孔声波测试、内外部变形监测、现场变形试验、钻孔芯样力学试验、压水试验、孔壁录像、钻孔弹模测试等手段进行综合研究,结果表明,边坡岩体可划分为强、弱、微三个卸荷带,强、弱卸荷带岩体力学性质有明显弱化,微卸荷岩体力学性质无明显变化。在卸荷带内未产生新的结构面,但卸荷引起了结构面的进一步扩展和张开。岩体变形是结构面张开和岩块变形的综合反映,主要受开挖卸荷影响,锚索对于限制结构面张开作用明显。     

隐形碳关税:概念辨析与国际治理

碳关税是各国高度关注的贸易问题,因涉及各国经贸利益,南北国家在碳关税问题上分歧很大。任何有关碳关税的政策措施,都会引起发展中国家的强烈反对。因此,部分发达国家试图另辟蹊径,在国际贸易中通过增加生产标准、碳标签等技术要求,以比较隐蔽的方式实现执行碳关税的目的。文中将这些隐蔽的但能起到碳关税执行效果的政策措施归纳为隐形碳关税,并定义隐形碳关税是指那些虽然没有在边境环节征收碳关税,但与征收碳关税起到相同贸易壁垒作用的,对发展中国家出口产品和服务构成限制的政策和措施。隐形碳关税比较典型的表现形式包括生产标准、碳标签等措施。这些措施本身是政策中性的,并不构成隐形碳关税,但如果叠加了转移应对气候变化成本、限制发展中国家产业发展等目的,这些措施的性质便不再中性,而成为现实中的贸易壁垒。隐形碳关税的治理应该是国际气候治理进程的一个部分,《联合国气候变化框架公约》则应是隐形碳关税治理的主要国际平台。无论是在气候公约内还是气候公约外的治理机制,隐形碳关税的国际治理都应遵循气候公约的相关原则,尤其是共同但有区别责任原则,区别对待发达和发展中国家的责任和义务,充分发挥生产标准、碳标签等措施的积极环境效用,同时约束其不当使用,建立公平、互信、务实的国际合作模式,实现气候治理与经济发展的协同。     

长江流域夏季降水的贝叶斯多模式集合概率预测

A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).     

Assessing the reliability of probabilistic flood inundation model predictions

An ability to quantify the reliability of probabilistic flood inundation predictions is a requirement not only for guiding model development but also for their successful application. Probabilistic flood inundation predictions are usually produced by choosing a method of weighting the model parameter space, but previous study suggests that this choice leads to clear differences in inundation probabilities. This study aims to address the evaluation of the reliability of these probabilistic predictions. However, a lack of an adequate number of observations of flood inundation for a catchment limits the application of conventional methods of evaluating predictive reliability. Consequently, attempts have been made to assess the reliability of probabilistic predictions using multiple observations from a single flood event. Here, a LISFLOOD‐FP hydraulic model of an extreme (>1 in 1000 years) flood event in Cockermouth, UK, is constructed and calibrated using multiple performance measures from both peak flood wrack mark data and aerial photography captured post‐peak. These measures are used in weighting the parameter space to produce multiple probabilistic predictions for the event. Two methods of assessing the reliability of these probabilistic predictions using limited observations are utilized; an existing method assessing the binary pattern of flooding, and a method developed in this paper to assess predictions of water surface elevation. This study finds that the water surface elevation method has both a better diagnostic and discriminatory ability, but this result is likely to be sensitive to the unknown uncertainties in the upstream boundary condition. Copyright © 2015 John Wiley & Sons, Ltd.