Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks
Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks
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DOI：
10.4028/www.scientific.net/amm.584-586.1017
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发表时间：
2014-07
期刊：
影响因子：
--
通讯作者：
I-Cheng Yeh
中科院分区：
文献类型：
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作者：
I-Cheng Yeh
本文旨在论证应用量子回归神经网络(QRNN)估计高性能混凝土(HPC)抗压强度分布的可能性。使用包含1030个抗压强度数据的数据库对QRNN进行评价。每个数据包括水泥、高炉矿渣、飞灰、水、高效减水剂、粗集料、细集料(每立方米千克)、龄期和抗压强度。研究得出以下结论：(1)分位数回归神经网络可以建立准确的分位数模型，估计高性能混凝土抗压强度的分布。(2)HPC抗压强度预测的各种分布表明，误差的方差在不同观测之间是不恒定的，这意味着预测是异方差的。(3)对数正态分布可能比正态分布更适合于高性能混凝土抗压强度的分布。由于工程师不应假设抗压强度预测误差的方差是恒定的，因此估计高性能混凝土抗压强度分布的能力是QRNN的一个重要优势。
This paper is aimed at demonstrating the possibilities of adaptingQuantile Regression Neural Network (QRNN) to estimate the distribution ofcompressive strength of high performance concrete (HPC). The databasecontaining 1030 compressive strength data were used to evaluate QRNN. Each dataincludes the amounts of cement, blast furnace slag, fly ash, water,superplasticizer, coarse aggregate, fine aggregate (in kilograms per cubicmeter), the age, and the compressive strength. This study led to the followingconclusions: (1) The Quantile Regression Neural Networks can buildaccurate quantile models and estimate the distribution of compressive strengthof HPC. (2) The various distributions of prediction of compressive strength of HPCshow that the variance of the error is inconstant across observations, whichimply that the prediction is heteroscedastic. (3) The logarithmic normaldistribution may be more appropriate than normal distribution to fit thedistribution of compressive strength of HPC. Since engineers should not assumethat the variance of the error of prediction of compressive strength isconstant, the ability of estimating the distribution of compressive strength ofHPC is an important advantage of QRNN.