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首页> 外文会议>第二届自密实混凝土设计、性能及应用技术国际研讨会(2nd International Symposium on Design,Performance and Use of Self-Consolidating Concrete SCC'2009-China) >PREDICTING THE COMPRESSIVE STRENGTH OF SELF COMPACTING CONCRETE USING ARTIFICIAL NEURAL NETWORK
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PREDICTING THE COMPRESSIVE STRENGTH OF SELF COMPACTING CONCRETE USING ARTIFICIAL NEURAL NETWORK

机译：利用人工神经网络预测自密实混凝土的抗压强度

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摘要

Artificial neural network have recently been widely used to simulate the human activities in many areas of civil engineering applications. In the present paper, an artificial neural network study is carried out to predict the compressive strength of self-compacting concrete.This paper aims to show a possible applicability of artificial neural network to predict the compressive strength of self-compacting concrete. An artificial neural network model is built,trained and tested using the available experimental results for 104 different mixture proportions gathered from the technical literature. The data used in the artificial neural network model are arranged in a format of six input parameters that cover the content of cement, fly ash, water, superplasticizer, coarse aggregate and fine aggregate and, an output parameter which is compressive strength of self-compacting concrete. The statistical values for compressive strength predicted by artificial neural network are also compared to those obtained using regression models. The training and testing results in the artificial neural network model show that artificial neural network can be an alternative approach for the predicting the compressive strength of self-compacting concrete using concrete ingredients as input parameters.
机译：人工神经网络最近已广泛用于在土木工程应用的许多领域中模拟人类活动。本文对人工神经网络进行了研究以预测自密实混凝土的抗压强度。本文旨在证明人工神经网络在预测自密实混凝土的抗压强度方面的适用性。使用从技术文献中收集到的104种不同混合物比例的可用实验结果，构建，训练和测试了人工神经网络模型。人工神经网络模型中使用的数据以六个输入参数的格式排列，这些参数涵盖了水泥，粉煤灰，水，高效减水剂，粗骨料和细骨料的含量，输出参数是自密实的抗压强度具体。还将由人工神经网络预测的抗压强度统计值与使用回归模型获得的统计值进行比较。人工神经网络模型的训练和测试结果表明，人工神经网络可以作为一种替代方法，以混凝土成分为输入参数来预测自密实混凝土的抗压强度。

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