地下工程围岩分类模糊推理系统设计

将现场获取的影响地下工程围岩分类的几种主要因素视为判别围岩类别论域上的模糊子集,利用建立的地下工程围岩分类模糊逻辑推理系统对围岩类别进行模糊逻辑推理运算,获得与实际情况相一致的围岩类别的判定,直接为地下工程的信息化设计和施工提供依据。     

Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques,which do not fully provide quantification of uncertainty in the constructed models and fail to optimally weight geological field observations against constraints from geophysical data.Here,using the Bayesian Obsidian software package,we develop a methodology to fuse lithostratigraphic field observations with aeromagnetic and gravity data to build a 3D model in a small(13.5 km×13.5 km)region of the Gascoyne Province,Western Australia.Our approach is validated by comparing 3D model results to independently-constrained geological maps and cross-sections produced by the Geological Survey of Western Australia.By fusing geological field data with aeromagnetic and gravity surveys,we show that 89%of the modelled region has>95%certainty for a particular geological unit for the given model and data.The boundaries between geological units are characterized by narrow regions with<95%certainty,which are typically 400-1000 m wide at the Earth's surface and 500-2000 m wide at depth.Beyond~4 km depth,the model requires geophysical survey data with longer wavelengths(e.g.,active seismic)to constrain the deeper subsurface.Although Obsidian was originally built for sedimentary basin problems,there is reasonable applicability to deformed terranes such as the Gascoyne Province.Ultimately,modification of the Bayesian engine to incorporate structural data will aid in developing more robust 3D models.Nevertheless,our results show that surface geological observations fused with geophysical survey data can yield reasonable 3D geological models with narrow uncertainty regions at the surface and shallow subsurface,which will be especially valuable for mineral exploration and the development of 3D geological models under cover.     

Intelligent control for modeling of real‐time reservoir operation,part II: artificial neural network with operating rule curves

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network‐based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M‐5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input–output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M‐5 curves in real‐time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved. Copyright © 2004 John Wiley & Sons, Ltd.     

Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

关于岩土样本统计分析的几点讨论

结合岩土单元体的天然成因及人为设定的基本特性,阐述了岩土样本离散的必然性及其离散程度与岩土单元体分划范围的相关性,通过对变异系数的适用性及岩土样本概况性的分析,提出采用均同指数评价样本分布的离散程度和按相对容量标准控制岩土样本数量等观点,建议切实针对岩土母体的分布形态推断标准值,同时对小样本条件下标准值的确定方式进行了分析和建议。     

基于GIS的贝叶斯统计推理技术在印度野牛生境概率评价中的应用

目前,ＧＩＳ技术已被广泛应用在野生动物生境研究中。但是,作为空间数据分析和处理工具,ＧＩＳ缺乏进行启发式推理的能力。因此,与擅长于此的贝叶斯统计推理技术相结合则是解决这一问题的重要途径。以西双版纳纳板河流域生物圈保护区为试验区,综合应用ＧＩＳ技术和多元统计技术建立印度野牛生境的两个逻辑斯蒂多元回归模型：趋势表面模型和环境模型,第一个模型的自变量是位置坐标,第二个模型的自变量是一组环境因子,然后应用贝叶斯统计合并这两个模型产生贝叶斯综合模型。结果表明,贝叶斯综合模型优于环境模型,可应用于野生动物生境概率评价。     

Setting minimum head capsule abundance and taxa deletion criteria in chironomid-based inference models

Criteria for removing training set lakes and taxa in chironomidbased inference models, due to low abundances, have largely been ad hoc. We used an anoxia inference model and a hypolimnetic oxygen model from southcentral Ontario to determine what effect subfossil head capsule abundance and taxa deletion criteria have on fossil inference statistics. Results from six training set lakes suggest that a minimum abundance of 40–50 head capsules is sufficient for use in inference models, however more diverse samples likely require more than 50 head capsules. Taxa deletion criteria substantially improved the predictive ability of inference models (lowered the root mean squared error of prediction (RMSEP)). The common practice of including taxa with only 2% abundance in at least two lakes was one of the deletion criteria that much improved inference models. Similar deletion criteria, such as 2% in at least 3 lakes and 3% in at least 1 lake, produced comparable improvements (up to 18% reduction in RMSEP).     

基于案例挖掘的边坡稳定性智能评价系统研究

虽然边坡灾害治理实践已经积累了大量的成功的或失败的边坡治理工程案例,但这些工程案例产生的大量数据信息未被充分利用与开发,造成了极大的资源浪费。为此,基于边坡工程案例,应用数据挖掘与知识发现和递归的自-组织模糊神经推理网络的方法,初步提出了一种基于案例挖掘的边坡稳定性智能评价系统,并通过案例挖掘的应用实例表明了该系统的有效性和可行性。