...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Fuzzy sets and systems >Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition
【24h】

Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition

机译：ANFIS网络的进化帕累托优化，用于在清水条件下对桩组的冲刷进行建模

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The existence of waves and currents around the foundations of pile groups causes scour and reduces the stability of structures in coastal environments and rivers. In this study, a new hybrid multi-objective technique is developed to predict scour at pile groups in clear water condition. Singular Decomposition Value (SVD) and the Differential Evolution (DE) algorithm are used to determine the linear consequent parameters and nonlinear antecedent parameters, respectively, in an Adaptive Neuro-Fuzzy Inference System (ANFIS). Furthermore, the Pareto curve is used to select the tradeoff between two different objective functions, namely training error (TE) and prediction error (PE) to consider the flexibility of modeling and to optimally design the proposed method (ANFIS-DE/SVD). First, the most effective parameters on predicting scour at pile groups are determined and modeled by ANFIS-DE/SVD. Subsequently, sensitivity analysis is carried out using ANFIS-DE/SVD to identify the effect of each parameter on predicting scour at pile groups. The results indicate that the best input combination for estimating scour at pile groups includes critical velocity due to the incipient motion of sediment on the bed, mean particle diameter, flow depth, pile diameter, center-to-center distance between adjacent piles in line with the flow, center-to-center distance between adjacent piles perpendicular to the flow, number of piles parallel to the flow and number of piles normal to the flow. According to the sensitivity analysis results, the pile diameter and number of piles normal to the flow are the most effective parameters for predicting scour at pile groups in clear water condition. Furthermore, the ANFIS-DE/SVD results for the superior input combination are compared with the genetic algorithm and empirical equations. The results indicate that ANFIS-DE/SVD outperforms the other techniques. (C) 2016 Elsevier B.V. All rights reserved.
机译：桩基周围的波浪和水流的存在会引起冲刷并降低沿海环境和河流中建筑物的稳定性。在这项研究中，开发了一种新的混合多目标技术来预测在清水条件下桩组的冲刷。在自适应神经模糊推理系统（ANFIS）中，奇异分解值（SVD）和差分演化（DE）算法分别用于确定线性结果参数和非线性先验参数。此外，帕累托曲线用于选择两个不同目标函数（即训练误差（TE）和预测误差（PE））之间的权衡，以考虑建模的灵活性并优化设计所提出的方法（ANFIS-DE / SVD）。首先，通过ANFIS-DE / SVD确定并模拟了预测桩群冲刷的最有效参数。随后，使用ANFIS-DE / SVD进行敏感性分析，以识别每个参数对预测桩组冲刷的影响。结果表明，估算桩组冲刷的最佳输入组合包括临界速度，这是由于沉积物在床层上的初始运动，平均粒径，流深，桩直径，相邻桩之间的中心距与流，垂直于流的相邻桩之间的中心距，平行于流的桩数以及垂直于流的桩数。根据敏感性分析结果，桩直径和垂直于水流的桩数是预测在清水条件下桩组冲刷的最有效参数。此外，将高级输入组合的ANFIS-DE / SVD结果与遗传算法和经验方程进行了比较。结果表明，ANFIS-DE / SVD优于其他技术。 （C）2016 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号