Comparative assessment of nonlinear static and dynamic methods for analysing building response under sequential earthquake and tsunami

This paper presents a comprehensive comparison of different dynamic and static approaches for assessing building performance under sequential earthquakes and tsunami. A 10-storey reinforced concrete seismically designed Japanese vertical evacuation structure is adopted as a case study for the investigation. The case study building is first assessed under sequential earthquake and tsunami nonlinear response history analyses: the first time this is done in the literature. The resulting engineering demand parameters are then compared with those obtained when the analysis procedure is systematically simplified by substituting different static approaches for the nonlinear response history analyses in both the earthquake and tsunami loading phases. Different unloading approaches are also tested for the cases when an earthquake pushover is adopted. The results show that an earthquake nonlinear response history analysis, followed by a transient free vibration and a tsunami variable depth pushover, provides the best alternative to full dynamic analyses in terms of accuracy and computational efficiency. This structural analysis combination is recommended and has the advantage that it does not require the tsunami inundation time history to be known in advance. The proposed double pushover approach is instead deemed only suitable for the collapse assessment of regular low to mid-rise buildings and for the development of collapse fragility functions. An important observation made is that sustained earthquake damage seems not to affect the tsunami resistance of the case study building when the fully dynamic analysis is carried out for the sequential loading. This observation will be the subject of future work.     

Forecasting monthly precipitation using sequential modelling

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.     

一种改进的序列图像激光线提取方法

利用结构光提取目标表面三维信息,对被测目标进行检测,是各种检测系统中常用的方法。由于激光具有方向性好,光能分布集中等优点,所以很多检测系统都采用激光作为检测光源。本文在分析了线型激光图像特征的基础上,针对旋转件在线同轴度检测系统所采用的红色激光条纹图像提出了一种精确、快速、稳定的激光线提取算法。     

海洋沉积物的顺序萃取方法及其在冲绳海槽热液影响沉积物中的应用

不同沉积环境和沉积机制下进入海洋沉积物中的元素分别与不同介质结合,因此,将沉积物中不同结合形态元素相分离的顺序萃取方法是研究沉积物源和沉积环境的重要手段。以Tessier等建立的萃取流程为基础,针对深海沉积物标准物质GBW07315中Fe、Mn、Co、Cu、V元素,分析了方法的准确性和重复性。结果表明:各元素回收率基本介于94%～126%之间,平均值为106%。在重复性方面,除V元素在铁锰氧化物结合态中的相对标准偏差较高外,其余元素在铁锰氧化物结合态、有机结合态以及残渣态中的相对标准偏差均低于15%,而碳酸盐结合态和可交换态中元素的相对标准偏差较大,这与其样品中这些结合态的元素含量相对较低有关。因此,对于深海沉积物来讲,顺序萃取法具有较好的稳定性和可靠性,尤其是对其中铁锰氧化物结合态、有机结合态和残渣态的萃取效果令人满意。将该方法应用于冲绳海槽热液影响沉积物中,实现了热液源铁锰氧化沉积的有效萃取和分离。     

海洋沉积物的顺序淋滤萃取分析研究现状与发展趋势

在过去几十年里,为了探索海洋沉积物中元素的赋存形态,发展了很多分离沉积物中不同元素结合态的淋滤萃取方法。这些方法对于了解沉积物中微量元素的来源、迁移、分散、活动、生物化学及物理化学行为等特征具有重要的意义,并且在海洋地球化学和海洋环境污染研究中已得到广泛应用。本文综合论述了海洋沉积物的顺序淋滤萃取分析方法的研究进展,介绍了两种影响较大的方法,对其选用的萃取试剂以及分析流程进行了详细讨论,并比较了方法的优点和不足。     

渤海湾北部和西部海域表层沉积物中无机碳形态研究

采用氯化钠(NaC1)、氨水(NH3·H2O)、氢氧化钠(NaOH)、盐酸羟胺(NH2OH·HC1)溶液对渤海湾北部和西部海域表层沉积物中无机碳进行了连续浸取,并将无机碳分为5种赋存形态:交换态(NaCl相)、弱碱结合态(NH3·H2O相)、强碱结合态(NaOH相)、弱酸结合态(NH2OH·HCl相)和残渣态.同时,结...     

南大西洋热液沉积物的不同相态提取方法与元素赋存状态

本文对采自南大西洋受不同程度热液活动影响的表层沉积物样品进行了元素和矿物组成分析,并对热液沉积物的碳酸盐相、Fe-Mn氧化物相和残渣态进行了一系列顺序提取实验.选用不同浓度的盐酸羟胺(HH)和醋酸(HAc)混合溶液对样品的Fe-Mn氧化物相进行提取,通过分析不同实验条件下Fe-Mn氧化物相Ti/Nd、Ti/Pb比值和F...     

Integration of Tracer Test Data to Refine Geostatistical Hydraulic Conductivity Fields Using Sequential Self-Calibration Method

On the basis of local measurements of hydraulic conductivity,geostatistical methods have been found to be useful in heterogeneity characterization of a hydraulic conductivity field on a regional scale. However,the methods are not suited to directly integrate dynamic production data,such as,hydraulic head and solute concentration,into the study of conductivity distribution. These data,which record the flow and transport processes in the medium,are closely related to the spatial distribution of hydraulic conductivity. In this study,a three-dimensional gradient-based inverse method-the sequential self-calibration (SSC) method-is developed to calibrate a hydraulic conductivity field,initially generated by a geostatistical simulation method,conditioned on tracer test results. The SSC method can honor both local hydraulic conductivity measurements and tracer test data. The mismatch between the simulated hydraulic conductivity field and the reference true one,measured by its mean square error (MSE),is reduced through the SSC conditional study. In comparison with the unconditional results,the SSC conditional study creates the mean breakthrough curve much closer to the reference true curve,and significantly reduces the prediction uncertainty of the solute transport in the observed locations. Further,the reduction of uncertainty is spatially dependent,which indicates that good locations,geological structure,and boundary conditions will affect the efficiency of the SSC study results.     

EVALUATION OF GEOCHEMICAL QUALITY CONTROL IN DETERMINATION OF Mn IN SOILS USING A SEQUENTIAL CHEMICAL EXTRACTION

Sequential chemical extraction procedure has been widely used to partition particulate trace metals into vari-ous fractions and to describe the distribution and the statue of trace metals in geo-environment.One sequential chemical extraction procedure was employed here to partition various fractions of Mn in soils.The experiment was designed with quality controlling concept in order to show sampling and analytical error.Experimental results obtained on duplicate analy-sis of all soil samples demonstrated that the precision was less than 10%(at 95% confidence level).The accuracy was estimated by comparing the accepted total concentration of Mn in standard reference materials (SRMs) with the measured sum of the individual fractions.The recovery of Mn from SRM1 and SRM2 was 94.1% and 98.4%,respectively.The detection limit,accuracy and precision of the sequential chemical extraction procedure were discussed in detailed.All the results suggest that the trueness of the analytical method is satisfactory.     

遗传算法及其在GOMS模型反演中的应用效果分析

几何光学交互遮蔽模型（GOMS）是一种重要的遥感前向模型,它较好解释了“热点”现象,具有较强的前向模拟能力。但由于其固有的非线性性,给反演带来困难。本文尝试采用近年来兴起的并行随机全局寻优算法－遗传算法对GOMS进行反演,并针对传统遗传算法的不足进行了改进。在使用相同先验知识的条件下,将该算法与目前最有效的约束非线性最优化确定性搜索算法－逐步二次规划法对GOMS模型的反演效果进行了比较,结果表明,逐步二次规划法搜索效率较高,但结果受初值的影响很大,初值选择不当,易收敛于局部最优解,而遗传算法具有全局最优的收敛效果,但局部搜索效率较差。在某些对精度要求不高,而对搜索效率要求较高的场合,可以采用遗传算法与确定性搜索算法相结合的混合遗传算法,以提高算法的搜索效率,获得较为满意的效果。