首页> 外文期刊>KSCE journal of civil engineering >Pile in the Unsaturated Cracked Substrate with Reliability Assessment based on Neural Networks
【24h】

Pile in the Unsaturated Cracked Substrate with Reliability Assessment based on Neural Networks

机译:基于神经网络的可靠性评估,堆积在不饱和裂缝基板中

获取原文
获取原文并翻译 | 示例
           

摘要

In this study, we present reliability assessments for a monopile socketed into a vertical fissured unsaturated substrate correlated with the degree of saturation. The pile was loaded by concentrated lateral force in a three-dimensional space. Procedures were prepared in the numerical analysis software FLAC 3D, fast lagrangian analysis of continua. FISH, a scripting language embedded within FLAC, was used to control the depth of cracks in unsaturated soil and manage a significant number of the calculations. The presented approach expands knowledge about piles loaded laterally and also the considerable influence of environmental changes. In the reliability part of the study, the excessive horizontal displacement of the pile head was examined as an implicit function. The analytical model of vertical water flux in the partially saturated soil was applied in connection with the iterative solution of the cracked depth of the substrate. The suction profile was estimated for a layer above the groundwater level. An important aspect of this paper is that the reliability analyses were prepared for this task in complex environmental conditions and with a horizontal load. All limit state functions were approximated following the response surface methodology, which was defined by neural networks based on modified perceptrons and was compared with well-known polynomials functions.
机译:在这项研究中,我们向垂直裂解的不饱和基材插入垂直裂解的不饱和基材中的型号的可靠性评估。通过三维空间中的浓缩横向力装载该桩。在数值分析软件FLAC 3D中制备程序,快速拉格朗日的Continua分析。鱼类是嵌入FLAC内的脚本语言,用于控制不饱和土壤中的裂缝深度,并管理大量的计算。提出的方法扩大了对横向装载的桩的知识,并且对环境变化的相当大的影响。在该研究的可靠性部分中,将桩头的过度水平位移作为隐式功能进行检查。垂直水通量在部分饱和的土壤中的分析模型与基材的裂缝深度的迭代溶液相关。抽吸轮廓估计用于地下水位上方的层。本文的一个重要方面是,在复杂的环境条件下为该任务和水平负载准备可靠性分析。在响应表面方法后近似全部限制状态函数,该方法由基于修改的感知的神经网络定义,并与众所周知的多项式功能进行比较。

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