不同刚度植物杆群对规则波传播及紊动特性影响研究

以不同刚度硅胶圆杆群为概化植物模型,测定其抗弯弹性模量,通过波浪水槽实验,研究规则波在不同刚度植物杆群内的流速分布、紊动特征及不同刚度杆群的消浪效果。实验结果表明,当波浪通过柔性杆群时,受其摆动的影响,流速周期变化从单峰型逐渐转变成双峰型,杆群刚度越小形成的二次波峰越明显;不同刚度杆群内水体紊动强度变化显示,杆群刚度越大,造成杆群内水体的紊动强度越大;随着杆群抗弯弹性模量的增大,其消浪系数也增大,消浪系数的增长与材料的抗弯弹性模量值非线性关系,而是在某一弹性模量范围内,对消浪系数的影响较为敏感。     

煤渣改良土的抗冻能力及损伤特性研究

为研究煤渣改良土作为季节冻土区路基填料的抗冻能力,以不同煤渣掺量及不同养护龄期下的煤渣改良土为研究对象,利用GDS三轴试验系统开展了不同冻融次数下改良土的三轴压缩试验,获得了最佳的煤渣掺量及养护时间数据,提出冻融及加载综合影响下改良土的总损伤变量,并据此建立了损伤本构关系。结果表明：改良土的抗冻能力随着煤渣掺入量的增大出现先增强后减弱的变化趋势,随龄期的增长而逐渐增强。冻融循环对改良土物理力学性质的影响主要集中在前5次循环过程,超过5次后影响逐渐减弱。冻融加载总损伤变量能够较好地反映冻融及加载过程中改良土性质的劣化,据此建立的损伤本构关系具有一定的精度,可以为季节冻土区煤渣改良土路基工程提供参考。     

Revisiting the death of geography in the era of Big Data: the friction of distance in cyberspace and real space

Many scholars have argued that the importance of geographic proximity in human interactions has been diminished by the use of the Internet, while others disagree with this argument. Studies have noted the distance decay effect in both cyberspace and real space, showing that interactions occur with an inverse relationship between the number of interactions and the distance between the locations of the interactors. However, these studies rarely provide strong evidence to show the influence of distance on interactions in cyberspace, nor do they quantify the differences in the amount of friction of distance between cyberspace and real space. To fill this gap, this study used massive amounts of social media data (Twitter) to compare the influence of distance decay on human interactions between cyberspace and real space in a quantitative manner. To estimate the distance decay effect in both cyberspace and real space, the distance decay function of interactions in each space was modeled. Estimating the distance decay in cyberspace in this study can help predict the degree of information flow across space through social media. Measuring how far ideas can be diffused through social media is useful for users of location-based services, policy advocates, public health officials, and political campaigners.     

SensePlace3: a geovisual framework to analyze place–time–attribute information in social media

SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources.     

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.     

摄动理论在腐蚀管线随机地震反应分析中的应用

埋地管线的腐蚀情况十分复杂,具有随机性的特点,从而导致其在地震激励下的反应必然具有随机性。本文在管线腐蚀离散状态模型和弹性地基梁原理的基础上,利用随机摄动理论推导给出了腐蚀管线在地震激励下位移和应力的解析表达式,并进行了均值和标准差的计算。实例分析中将该方法计算结果与Monte Carlo模拟方法计算结果进行了对比。结果表明,利用随机摄动方法可以快速、精确地求得腐蚀管线的随机地震反应。     

基于粒径分维值检验的松散介质渗透系数空间估值——以河北平原为例

为了克服松散含水介质非均质性各向异性对介质渗透系数的求取所带来的困难,以水动力条件为线索,利用互相关法寻找同一时代地层及同一时代地表水的主、次流方向.在主流方向上,建立了石家庄-束鹿-衡水段介质颗粒粒径随距离递变的数学模型,并以此为基础推求无钻孔地段的粒径.同时,运用分形理论,计算了中、上更新统地层主流方向平均粒径空间分布的分维值,并将其作为检验粒径估值正确与否的判据.通过不同岩性的渗透系数-深度模型,实现了用平均粒径预测渗透系数的全过程.     

折射层析成像法测试隧道围岩松动圈

通过应用折射层析成像法在乌鞘岭特长隧道中的测试,说明该方法对于横向速度变化不均一,过渡带速度渐变的地质情况具有分辨率高、图像直观、易于解释,适合对隧道围岩松动圈的测试.