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Artificial neural network and fuzzy neural integrated systems for geotechnical modeling.

机译：岩土工程建模的人工神经网络和模糊神经集成系统。

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摘要

One of the most important tasks of solving geotechnical problem is the interpretation of data from measurements made at field or at lab. The treatments of these parameters can be sorted into three main categories: (i) classification the data; (ii) site characterization through the interpreting of data; (iii) geotechnical properties prediction and modeling. Recently, a new approach called artificial neural network has emerged to fulfill those tasks.; Another important aspect of risk and decision analysis with geotechnical problems involves the consideration of many uncertain variables. There are basically two types of uncertainty (Casti, 1990): (i) ignorance, including measurement error, indecision about the mathematical form of the model and including stochasticity, spatial variation, and individual heterogeneity. Ignorance and variability are fundamentally different. The variability (statistical) uncertainty associated with these variables can be handled through probability and statistical theory. On the other hand, ignorance is subjective (non-random type) and can not be translated into probability in the same way. These often involve fuzzy information, i.e. information which is vague, imprecise, qualitative, linguistic, or incomplete.; Since the fuzzy set theory (FST) and artificial neural networks (ANN) are both numerical model-free estimators. They can share the ability to improve the intelligence of systems working in uncertain, imprecision and noisy environments. This suggests that we may combine fuzzy logic control and decision systems with artificial neural networks. Two different fuzzy neural network models have been developed in this study: (i) ANFISA—Adaptive Network-based Fuzzy Inference Systems for Approximation. (ii) FANN—Fuzzy Artificial Neural Network. Fundamentally, ANFISA is about taking a fuzzy inference system (FIS) and tuning it with a backpropagation algorithm based on some collection of input-output data. This allows the fuzzy inference systems to team. On the other hand, FANN model fuzzifies all the network parameters within the artificial neural network architecture, which will let the system be capable of processing fuzzy numbers.; The proposed artificial neural network model and two types of fuzzy neural integrated models are used for four different geotechnical applications, which are (i) Liquefaction-Induced Horizontal Ground Displacement Prediction. (ii) Liquefaction Potential Assessment. (iii) Axial Load Capacity of Piles Prediction. (iv) Uplift Capacity of Suction Caissons Prediction. Large historic databases have been used for each application for developing the models. (Abstract shortened by UMI.)
机译：解决岩土工程问题最重要的任务之一是根据现场或实验室的测量结果解释数据。这些参数的处理方式可以分为三大类：（i）对数据进行分类; （ii）通过解释数据进行现场表征; （iii）岩土特性预测和建模。最近，一种名为人工神经网络的新方法应运而生。带有岩土工程问题的风险和决策分析的另一个重要方面涉及对许多不确定变量的考虑。基本上有两种类型的不确定性（Casti，1990）：（i）无知，包括测量误差，对模型的数学形式的不确定，包括随机性，空间变化和个体异质性。无知和变异是根本不同的。与这些变量相关的可变性（统计）不确定性可以通过概率和统计理论来处理。另一方面，无知是主观的（非随机类型），不能以相同的方式转化为概率。这些通常涉及模糊信息，即模糊，不精确，定性，语言或不完整的信息。由于模糊集理论（FST）和人工神经网络（ANN）都是无数值模型的估计器。他们可以共享提高在不确定，不精确和嘈杂环境中工作的系统的智能的能力。这表明我们可以将模糊逻辑控制和决策系统与人工神经网络相结合。这项研究开发了两种不同的模糊神经网络模型：（i）ANFISA-基于自适应网络的近似模糊推理系统。 （ii）FANN-模糊人工神经网络。从根本上讲，ANFISA涉及采用模糊推理系统（FIS）并使用基于某些输入输出数据集合的反向传播算法对其进行调整。这允许模糊推理系统进行分组。另一方面，FANN模型模糊了人工神经网络架构中的所有网络参数，这将使系统能够处理模糊数。所提出的人工神经网络模型和两种类型的模糊神经集成模型用于四种不同的岩土工程应用，即（i）液化引起的水平地面位移预测。 （ii）液化潜力评估。 （iii）桩的轴向承载能力预测。 （iv）吸力沉箱预测的提升能力。大型历史数据库已用于每种应用程序以开发模型。 （摘要由UMI缩短。）

著录项

  • 作者

    Wang, Jun.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Engineering Civil.
  • 学位 Ph.D.
  • 年度 1999
  • 页码 260 p.
  • 总页数 260
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;
  • 关键词

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