某异型钢管混凝土拱桥地震反应分析

本文以某异型钢管混凝土拱桥为研究对象,通过ANSYS建立了结构的空间有限元模型,计算和分析了该桥的动力特性。同时,通过MATLAB程序生成拟合规范反应谱的人工地震波,并运用时程分析法计算了该桥在一维和多维输入下的地震反应,分析了该桥的地震反应规律,为钢管混凝土拱桥的抗震性能分析提供了一定的依据。     

Positional uncertainty in manually digitized map data

Digital map coordinates represent the locations of real world entities. As such, differences can exist between the ‘tru’ and digital database coordinates of those entities. This paper reports on a statistical characterization of positional error in manually-digitized and map-registered point data, the relative contribution of point type and operator to digitization error, and the effects of map media type on the positional uncertainty associated with registration.

Manually-digitized point data were collected by four operators from mylar and paper maps. Point locations for a number of different feature types were sampled from United States Geological Survey (USGS) 1:24 000 scale maps. Linear models were used to estimate the variance components due to among-operator, map media, point type and registration effects. The statistical distribution of signed distance deviations for manually-digitized data was leptokurtic relative to a random normal variate. Unsigned deviations averaged 0-054 mm. Squared distance deviations were not different from a Chi-square random variate. Variance components indicate that among-operator differences in positional uncertainty were large and statistically significant, while differences among point type were small and non-significant. Signed distance deviations associated with a first-order afhne followed a normal distribution. Unsigned distance deviations associated with a first-order affinc transformation averaged 0068mm, and squared distance deviations were distributed as a Chi-square. Differences in transformation accuracy were not related to type of map media.     

Representing geographical objects with scale‐induced indeterminate boundaries: A neural network‐based data model

The degree of uncertainty of many geographical objects has long been known to be in intimate relation with the scale of its observation and representation. Yet, the explicit consideration of scaling operations when modeling uncertainty is rarely found. In this study, a neural network‐based data model was investigated for representing geographical objects with scale‐induced indeterminate boundaries. Two types of neural units, combined with two types of activation function, comprise the processing core of the model, where the activation function can model either hard or soft transition zones. The construction of complex fuzzy regions, as well as lines and points, is discussed and illustrated with examples. It is shown how the level of detail that is apparent in the boundary at a given scale can be controlled through the degree of smoothness of each activation function. Several issues about the practical implementation of the model are discussed and indications on how to perform complex overlay operations of fuzzy maps provided. The model was illustrated through an example of representing multi‐resolution, sub‐pixel maps that are typically derived from remote sensing techniques.     

Chemical and isotopic assessment of surface water–shallow groundwater interaction in the arid Grande river basin,North-Central Chile

ABSTRACT

This paper analyses the composition of surface water and shallow groundwater in the Grande River basin, North-Central Chile, using this information to characterize water interactions. Chemical and isotopic data for surface water and groundwater (7 and 6 sampling locations, respectively) were obtained from three sampling campaigns performed in March–April (autumn), August–September (late winter) and December (early summer) 2012. Precipitation samples were also collected. Data was processed using spatial distribution charts, Piper and Stiff diagrams, and multivariate analysis. In general, the results for each method converge on a high degree of connectivity between surface water and shallow groundwater in the study area. Furthermore, approximately a 10% of groundwater contribution to the surface flow discharge was estimated for a particular reach. This multi-method approach was useful for the characterization of surface water–groundwater interactions in the Grande River basin, and may become a suitable and replicable scheme for studies in arid and semi-arid basins facing similar water management challenges.

Editor D. Koutsoyiannis; Associate editor B. Dewals     

新丰江库区上地壳三维细结构层析成像

在新丰江库区布设一个范围约50 km×40 km、由50个地震临时台站组成的观测台阵,接收来自不同方位的人工震源产生的莫霍界面反射波;台阵中的20个台站和5个区域固定台还对2009年3月至2010年5月发生在库区的地方震进行了观测.本文联合利用人工地震莫霍面反射波走时和天然地震直达波走时,采用连续模型反演技术重建了库区上地壳P波、S波慢度扰动和V_p/V_s扰动分布图像.研究结果表明:新丰江库区东、西部地区上地壳结构存在明显的差异.库区东部地区构造复杂,多条断裂在该区呈交叉状分布.北西向的石角-新港-白田断裂带在库区段内具有复杂的岩性和构造特征,该断裂带在新港至双塘一线可能延伸至地下8 km左右;近北东向的断裂带切割地壳较深.峡谷区及大坝以东附近地区存在上、下贯通的波速比高值区,尤其是大坝以西的深水峡谷区,存在一条顺河走向的陡倾角断层裂隙带,为库水渗透提供了良好通道.库区西部地区为相对稳定构造区,完整坚硬的花岗岩体透水性能较差,受库水渗透影响很小.新丰江水库诱发地震的形成与深部构造环境密切相关.峡谷区及大坝以东附近地区上地壳介质性质呈现明显的横向不均匀性,微震分布在介质物性结构的特定部位,"软"、"硬"交错的介质环境是倾滑正断层型微小震产生的可能原因.     

Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks

Understanding rock material characterizations and solving relevant problems are quite difficult tasks because of their complex behavior, which sometimes cannot be identified without intelligent, numerical, and analytical approaches. Because of that, some prediction techniques, like artificial neural networks (ANN) and nonlinear regression techniques, can be utilized to solve those problems. The purpose of this study is to examine the effects of the cycling integer of slake durability index test on intact rock behavior and estimate some rock properties, such as uniaxial compressive strength (UCS) and modulus of elasticity (E) from known rock index parameters using ANN and various regression techniques. Further, new performance index (PI) and degree of consistency (Cd) are introduced to examine the accuracy of generated models. For these purposes, intact rock dataset is established by performing rock tests including uniaxial compressive strength, modulus of elasticity, Schmidt hammer, effective porosity, dry unit weight, p‐wave velocity, and slake durability index tests on selected carbonate rocks. Afterward, the models are developed using ANN and nonlinear regression techniques. The concluding remark given is that four‐cycle slake durability index (I_d4) provides more accurate results to evaluate material characterization of carbonate rocks, and it is one of the reliable input variables to estimate UCS and E of carbonate rocks; introduced performance indices, both PI and Cd, may be accepted as good indicators to assess the accuracy of the complex models, and further, the ANN models have more prediction capability than the regression techniques to estimate relevant rock properties. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine

In a humid northern boreal climate, the success rate of artificial regeneration to Scots pine (Pinus sylvestris L.) can be improved by including a soil water content (SWC) based assessment of site suitability in the reforestation planning process. This paper introduces an application of airborne visible-near-infrared imaging spectroscopic data to identify suitable subregions of forest compartments for the low SWC-tolerant Scots pine. The spatial patterns of understorey plant species communities, recorded by the AISA (Airborne Imaging Spectrometer for Applications) sensor, were demonstrated to be dependant on the underlying SWC. According to the nonmetric multidimensional scaling and correlation results twelve understorey species were found to be most abundant on sites with high soil SWCs. The abundance of bare soil, rocks and abundance of more than ten species indicated low soil SWCs. The spatial patterns of understorey are attributed to time-stability of the underlying SWC patterns. A supervised artificial neural network (radial basis functional link network, probabilistic neural network) approach was taken to classify AISA imaging spectrometer data with dielectric (as a measure volumetric SWC) ground referencing into regimes suitable and unsuitable for Scots pine. The accuracy assessment with receiver operating characteristics curves demonstrated a maximum of 74.1% area under the curve values which indicated moderate success of the NN modelling. The results signified the importance of the training set’s quality, adequate quantity (>2.43 points/ha) and NN algorithm selection over the NN algorithm training parameter optimization to perfection. This methodology for the analysis of site suitability of Scots pine can be recommended, especially when artificial regeneration of former mixed wood Norway spruce (Picea abies L. Karst) - downy birch (Betula pubenscens Ehrh.) stands is being considered, so that artificially regenerated areas to Scots pine can be optimized for forestry purposes.     

Do benthic biofilters contribute to sustainability and restoration of the benthic environment impacted by offshore cage finfish aquaculture?

Benthic biofilters were deployed under a cage fish farm and in two reference locations to assess the influence of the farm on the biofilters and the surroundings, as well as to verify the usefulness of this technology as a mitigation tool. The biofilters underneath the farm recruited a fouling community practically identical to that of the control biofilters, which included a variety of trophic strategies. The former showed a higher ¹⁵N enrichment, indicating that fouling beneath the farm was benefiting from the farm waste. The waste retention efficiency was low (0.02 g N m⁻² month⁻¹) beneath the farm. Benthic biofilters aggregated demersal wild fish around and within them. Pelagic wild fish also frequently used the biofilters beneath the farm, forming compact shoals around them. The increased complexity of the habitat below the fish farm enhanced biodiversity, but this improvement did not lead to the recovery of the sediments around the biofilters.     

基于MATLAB神经网络方法的多层砖房震害预测

提出利用MATLAB人工神经网络工具箱建立基于贝叶斯正则算法的BP神经网络模型,以地震区多层砖房震害调查数据为因子的震害预测方法.神经网络模型输入震害因子包括建筑的层数、施工质量、房屋整体性等,输出值为建筑物在地震作用下的破坏程度.结果表明,本方法可以对多层砖房的震害样本进行预测并达到较理想的效果.