混凝土配合比设计的人工神经网络模型研究

A. Shaqadan, Imad Alshalout, Mohammad Abojaradeh, R. Al-kasasbeh, Abdullah Al-Khatib
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引用次数: 0

摘要

在实验室分析混凝土样品需要进行昂贵且耗时的实验。人工智能的进步为研究人员提供了一个有用的工具,以更复杂的方式提取有关实验和物理性质关系的信息,以预测混凝土的混合性能。在本研究中,使用了90个混凝土配合比试验样品。本研究旨在预测混凝土配合比质量,即抗压强度。几个量的硅粉添加,研磨时间,和水含量比计划成90混凝土块用于实验室研究。我们在28天后测量抗压强度,这是混凝土的主要性能。使用5个输入变量,训练一个人工神经网络模型来预测混凝土抗压强度。训练后的人工神经网络模型的相关值为0.98,相当高。所建立的人工神经网络模型是预测混凝土搅拌性能的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing Artificial Neural Networks Model for Concrete Mix Design
Analyzing concrete samples in the laboratory necessitates costly and time-consuming experiments. Advancements in artificial intelligence provide researchers with a helpful tool for extracting information regarding experimental and physical property relationships in a more sophisticated manner to predict concrete mix properties. In this inquiry, ninety concrete mix experiment samples are utilized. This study aims to predict concrete mix qualities, namely compressive strength. Several amounts of silica fume addition, milling duration, and water content ratio were planned into 90 concrete blocks for use in laboratory research. We measure compressive strength which is major concrete property after 28 days. Using five input variables, an ANN model was trained to forecast the concrete compressive strength. The trained ANN model show a correlation value of 0.98, which is quite high. The created ANN model is a useful tool for prediction of concrete mix behavior.
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