基于物理性质的膨胀土渗透性人工神经网络预测

IF 0.2 Q4 ENGINEERING, MULTIDISCIPLINARY
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引用次数: 0

摘要

渗透性是一个与建筑行业有关的土壤参数,用于了解渗透、径流和沉降过程。在渗透性研究中,特别是在膨胀土中,测试误差的风险是不可避免的。由于土的孔隙尺寸小、收缩膨胀大,渗透试验的试错难度较大。对影响土壤渗透性的土壤物理性质进行了多项研究。然而,得到的相关结果精度仍然较差。人工神经网络(ANN)是一种机器学习系统,可以改变其结构来解决系统中包含的问题。利用人工神经网络在数据学习中帮助建立的模型以较小的误差值预测未来的输出值。本研究旨在利用人工神经网络关联,研究膨胀土的物理性质对其渗透性的影响之间的相关性,并为未来的输入建立相关方程。以土壤液限、土壤塑性指数(IP)、细粒%、土壤渗透性为输入数据进行研究。结果表明,土壤物理性质与渗透率之间具有良好的相关性,输出的回归方程具有较高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Permeability Prediction for Expansive Soil Based on Physical Properties Using Artificial Neural Networks
Permeability is a soil parameter related to the construction industry to understand the processes of infiltration, runoff, and settlement. The risk of testing errors is inevitable in permeability investigations, especially in expansive soils. Trial and error in permeability testing becomes difficult due to soils with small pore sizes and large shrinkage expansion. Several studies related to soil physical properties that affect permeability have been conducted. However, the correlation results obtained still have poor accuracy. Artificial neural networks (ANN) are machine learning systems that can change their structure to solve problems that are included in the system. The use of ANNs in data learning is applied to help the established model predict future output values with a small error value. This research aims to study the correlation between the physical properties of expansive soil that affect its permeability using ANN correlation and then produce correlation equations for future inputs. The research was conducted with input data in the form of soil liquid limit, soil plasticity index (IP), %fine grains, and soil permeability as output data. Results demonstrated a good correlation between soil physical properties and permeability, revealing high accuracy in the output regression equation.
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来源期刊
Makara Journal of Technology
Makara Journal of Technology ENGINEERING, MULTIDISCIPLINARY-
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审稿时长
20 weeks
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