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Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

机译:基于神经网络和进化多项式回归的静动力耦合荷载下的筏板感应沉降评估

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摘要

Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and piles configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio 1/d is reported as the least effective parameter on the settlements of CPRFs.
机译:耦合桩筏基础(CPRF)广泛用于在桩和筏之间分担上部结构的重载荷,并减少总沉降和差异沉降。由静态/静态-动态耦合载荷引起的沉降是工程师在设计CPRF时要考虑的主要问题之一。通常使用三维有限元/有限差分建模或通过昂贵的实际比例/原型模型测试来进行CPRF诱导沉降的评估。由于不能简单地进行分析,尤其是在静动态载荷耦合的情况下,因此本文提出了两种实用的方法来获得沉降值。首先,建立了在不同静载荷和静动力载荷下的不同非线性有限差分模型,以计算施加的沉降。针对两种载荷工况,针对不同的轴向载荷和桩构型,数量,长度,直径和间距进行分析。基于经过充分验证的三维有限差分建模的结果,然后应用人工神经网络和进化多项式回归,并将其引入为能够准确表示静态和静态-静态动态沉降的有效方法。此外,使用基于余弦振幅法的灵敏度分析,将轴向载荷作为影响最大的参数引入,而将比率1 / d报告为对CPRF沉降的最小有效参数。

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