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ARTIFICIAL NEURAL NETWORK-BASED SETTLEMENT PREDICTION FORMULA FOR SHALLOW FOUNDATIONS ON GRANULAR SOILS

机译:基于人工神经网络的粒状土浅基沉降预测公式

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摘要

The problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. The geotechnical literature has included many formulae that are based on several theoretical or experimental methods to obtain an accurate, or near-accurate, prediction of such settlement. However, these methods fail to achieve consistent success in relation to accurate settlement prediction. Recently, artificial neural networks (ANNs) have been used successfully for settlement prediction of shallow foundations on granular soils and have been found to outperform the most commonly-used traditional methods. This paper presents a new hand-calculation design formula for settlement prediction of shallow foundations on granular soils based on a more accurate settlement prediction from an artificial neural network model. The design formula presented is a quick tool from which settlement can be calculated easily without the need for computers.
机译:估计浅层基础在颗粒状土壤上的沉降问题非常复杂,尚未完全理解。岩土工程文献包括许多基于几种理论或实验方法的公式,以获得这种沉降的准确或近乎准确的预测。但是,这些方法无法在准确的沉降预测方面取得一致的成功。近年来,人工神经网络(ANN)已成功用于粒状土壤中浅层基础的沉降预测,并且发现其性能优于最常用的传统方法。本文基于人工神经网络模型更准确的沉降预测,提出了一种新的人工计算设计公式,用于颗粒状浅层基础沉降预测。提出的设计公式是一种快速工具,无需计算机即可轻松计算出沉降。

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