教師資料查詢 | 類別: 期刊論文 | 教師: 葉怡成 YEH, I-CHENG (瀏覽個人網頁)

標題:Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks
學年102
學期2
出版(發表)日期2014/07/01
作品名稱Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks
作品名稱(其他語言)
著者Yeh, I-Cheng
單位淡江大學土木工程學系暨研究所
出版者Pfaffikon: Scientific.Net
著錄名稱、卷期、頁數Applied Mechanics and Materials 584-586, pp.1017-1025
摘要This paper is aimed at demonstrating the possibilities of adapting Quantile Regression Neural Network (QRNN) to estimate the distribution of compressive strength of high performance concrete (HPC). The database containing 1030 compressive strength data were used to evaluate QRNN. Each data includes the amounts of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate (in kilograms per cubic meter), the age, and the compressive strength. This study led to the following conclusions: (1) The Quantile Regression Neural Networks can build accurate quantile models and estimate the distribution of compressive strength of HPC. (2) The various distributions of prediction of compressive strength of HPC show that the variance of the error is inconstant across observations, which imply that the prediction is heteroscedastic. (3) The logarithmic normal distribution may be more appropriate than normal distribution to fit the distribution of compressive strength of HPC. Since engineers should not assume that the variance of the error of prediction of compressive strength is constant, the ability of estimating the distribution of compressive strength of HPC is an important advantage of QRNN.
關鍵字
語言英文(美國)
ISSN1662-7482
期刊性質國外
收錄於EI;
產學合作
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國別瑞士
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