A GIS Analysis to Evaluate Areas Suitable for Crushed Stone Aggregate Quarries in New England,USA

Aggregate is a low unit-value mineral commodity. Costs to move aggregate from the mine site to the point of use is a large fraction of the resource cost to users. Production sites for aggregate occur where suitable source materials exist and where transportation and market conditions are favorable. The increasing demand for aggregate and the difficulty of developing and permitting new sites and of renewal of permits on existing sites of aggregate production indicates that aggregate will be supplied from sources yet to be developed or delineated in many areas. Site development and permitting for aggregate production is difficult because many land management plans and zoning actions fail to anticipate prospective source areas for aggregate in a way that is consistent with both the source rock quality and the transportation and socioeconomic factors that define the economic viability of the industry. Spatial analysis provides a method to integrate both geology and economic (transportation and marketplace) parameters in a regional model. Weights of evidence (WofE) analysis has been used to measure the spatial correlation of geologic map, transportation network, and population data with current production sites for crushed stone aggregate in the New England region of the northeastern United States. Weighted logistic regression (WLR) is used with the WofE results to rank areas in terms of their relative suitability for production of crushed stone. Spatial analysis indicates that 85% of the 106 crushed stone aggregate quarries in New England are sited within 1.6 km (1 mile) of either a principal highway or rail line in the region. Seventy-eight percent of crushed stone aggregate quarries are sited in census tracts with population densities exceeding 100 people/mile². These relations illustrate the importance of proximity to both transportation corridors and developing areas where aggregate is predominately used. Only one active crushed stone quarry is located in a census tract with a population density less than 15 people/mile², reflecting the lack of sufficient market demand in many rural areas to develop an operation there. However, since 1990, almost all new quarries have been developed in census tracts with population densities less than 200 people/mile², indicating the difficulty of permitting new quarry sites in highly populated areas. Crushed stone aggregate is produced predominately from three hard rock types that are distributed widely in New England; 28% of sites use granitic rock, 25% use carbonate rocks, and 25% use mafic rock types that are categorized as trap rock by the aggregate industry. The other crushed stone aggregate sources include a variety of fine-grained metamorphic rock types. Carbonate rocks and Jurassic basalt (the primary trap rock source) are the most prevalent source rocks on an area-weighted basis. Spatial analysis can be used on a regional scale to rank areas by their relative suitabilityfor crushed stone aggregate production based on geology, transportation, and population parameters. The results of this regional analysis can identify areas for more detailed evaluation. As transportation or population features change, the model can be revised easily to reflect these changes.     

幂函数对建筑物沉降的回归分析

通过实例推导幂函数对建筑物沉降进行回归分析，并且得出较为精确的结果，同时也说明在选择回归函数的时候，还会受到很多条件影响。     

基于神经网络的建筑物沉降原因分析

在我们应用回归方法对建筑物沉降原因分析及沉降趋势的预测中，由于实际情况的复杂性及主观认识的局限性，这样所得的结果含有较多的人为因素，可能会与实际情况有所差异。针对这种情况，讨论了应用神经网络方法来发现和验证引起建筑物沉降的因素及对沉降趋势的预测。实例表明，该方法能取得较好的效果。     

Computation of response spectra from mining tremors using neural networks

Underground coal and copper ore exploitation in two Polish mining regions causes mining tremors and a series of other negative phenomena in the environment. Although these tremors are strictly connected with human activity, they differ considerably from other paraseismic vibrations. The moment of their occurrence is not to be foreseen likewise for earthquakes. The main problem discussed in the paper was formulated as the neural network evaluation of a relation between mining tremor energies, epicentral distances and acceleration response spectra. Back-propagation neural networks with Resilient back-propagation learning method were used. Each input vector included information about the mining tremor energy and the epicentral distance. Values of acceleration response spectrum were expected as the outputs of neural networks. Neurally evaluated spectra were compared with spectra computed on the basis of experimental data. After the network is trained and tested, it can be used for mapping of new data of mining tremor energies and epicentral distances into the spectra. Then, what is the substantial advantage of neural approach, the prediction of acceleration response spectra can be performed without recording of surface vibrations. In the light of the results, it is visible that the presented way of computation of acceleration response spectra can be peculiarly applied to prognosis of mining tremors influences on structures.     

Slope stability evaluation using Back Propagation Neural Networks

The Yudonghe landslide, located in western Hubei Province of China, consists of eastern and western subunits as well as a main landslide mass with upper and lower slip surfaces. As an important landslide close to Shuibuya Dam on the Qing River, its stability is crucial, as the slide might reactivate because of a change in ground-water level caused by filling of the Shuibuya Reservoir. Existing weakness zones, growth of ruptures, the downslope attitude of geologic strata, and water infiltration, which reduced the strength of rocks and soils, have been found to be the most important factors contributing to the Yudonghe landslide. With regard to the landslide processes, it can be noted that the original large-scale slide activity was due to erosion by the Qing River, the second sliding resulted from the fall of blocks from the head scarp, and the final activity was the growth of the eastern and western secondary slides. A base failure was the main type of slope movement, however, it was obvious that more than one sliding event occurred, as inferred from striations and fractures detected by microstructure analysis of soils along the failure surfaces. Slope instability was evaluated by the method of Back Propagation Neural Networks (BPNN), in which a four-layer BPNN model with five input nodes, two hidden layers, and two output nodes was constructed using a training data set of landslide samples throughout the Qing River area. The predicted results of this analysis showed that the factor of safety was 1.10, which indicates that the Yudonghe landslide is currently in a marginally stable condition.     

中国高铁网络结构特征及其组织模式

基于2018年高铁网络OD数据,运用社会网络分析方法从高铁网络、城市节点等方面探讨中国城市高铁网络结构特征及其地域组织模式,结果表明:①中国高铁网络整体较为松散,东北地区网络密度最高,东部和中部地区作为整体网络的中介作用明显;②重要高铁线路的"廊道效应"突出,中心度呈现出以京广、京沪和沪昆高铁组成的"三角旗状"空间格局并向两侧城市呈不规则递减的态势;③多层级网络识别出紧密关联高铁线路和四横四纵向八横八纵格局的转变;④高铁网络的地域组织形式表现为点-轴串珠模式、双核组团模式和极核模式,高铁网络的完善使组织模式由单核趋向于网络化转变。     

济南市岩溶水动态变化的神经网络模拟及泉水喷涌趋势预测

本文根据影响岩溶水位的因素,利用改进的BP神经网络建立一个能够反映济南市泉域岩溶地下水动态变化的随机模型,并进行检验,与运用多元回归模型预测的结果相比较,结果表明:BP神经网络模型进行岩溶地下水动态变化预测是可行的,该模型具有较强的学习、容错和联想功能,对岩溶地下水动态变化的预测精度大大的提高。最后在模拟的基础上,又对泉水喷涌的宏观趋势作了进一步的预测和分析。     

交通设计院所网络系统建设与思考

本文主要介绍了交通设计院所计算机网络系统建设的设计方案,并就计算机网络建设中应注意的问题提出几点思考.     

火山岩储层建模初探

火山岩储层建模研究是一个世界性难题。近年来 ,国内外在含油气盆地的火山岩中发现了大量的油气藏。为了探索火山岩储层物性的空间分布特征 ,作者选取了松辽盆地徐家围子地区火山岩发育的营城组为研究目的层 ,以火山岩相分析为基础 ,以基于相分析的神经网络技术为手段 ,通过多种参数的选取和计算 ,尝试性地建立了该区火山岩储层地质模型。该模型表明 :层状火山机构比块状火山机构的物性好 ;多相火山机构比单相火山机构物性好 ;裂缝发育的火山机构比裂缝不发育的火山机构物性好。基底涌流相、火山空落相、火山沉积相是有利的储集相带。     

研究气温变化趋势的新方法——三组耐抗线

针对气象资料难以满足高斯分布的情况,引进了具有耐抗性的三组耐抗线方法,分析了满足不同信度情况下最小二乘回归和三组耐抗线的差异,并用西藏近30 a气温资料进行了检验。结果表明三组耐抗线方法比最小二乘法研究气候变化趋势有明显优势;采用最小二乘法得到的西藏气候变暖估计偏高。