基于人工神经网络的砂浆渗浇纤维混凝土强度预测

Testing for compressive strength

抗压强度测试

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

基于人工神经网络的砂浆渗浇纤维混凝土强度预测

Authors / 作者信息

Abstract / 摘要

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 SIFCON mixes, and specimens were cast and tested after 28 days curing. The obtained experimental data are trained using ANN which consists of 4 input parameters like Percentage of fiber (PF), Aspect Ratio (AR), Type of admixture (TA) and Percentage of admixture (PA). The corresponding output parameters are compressive strength, tensile strength and flexural strength. The predicted values obtained using ANN show a good correlation between the experimental data. The performance of the 4-14-3 architecture was better than other architectures. It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics of SIFCON.

本文基于人工神经网络（ANN）预测含有不同矿物掺合料的砂浆渗浇钢纤维混凝土（SIFCON）强度。对84个砂浆渗浇钢纤维混凝土（SIFCON）样本进行了28天强度测试，将实验数据用人工神经网络进行训练，神经网络的4个输入参数为纤维百分比（PF）、试样宽高比（AR）、外加剂类型（TA）和外加剂百分比（PA），输出参数为抗压强度、抗拉强度和抗弯强度。基于人工神经网络的预测值与实验数据之间的相关性良好。4-14-3结构体系的预测结果优于其他情况。研究结果表明人工神经网络可用于砂浆渗浇钢纤维混凝土强度参数的预测。

Keywords / 关键词

artificial neural networks; root mean square error; SIFCON; silica fume; metakaolin; steel fiber

人工神经网络；均方根误差；纤维混凝土；硅灰；偏高岭土；钢纤维

注：文章转自FSCE期刊，不代表本平台观点，版权归原作者所有。