類神經網路與經驗公式在高性能混凝土抗壓強度預測之比較

Compared Artificial Neural Networks with Experimental Formulae in Predicting Strength of Concrete

葉怡成(I-Cheng Yeh) ； 彭釗哲(Chao-Che Peng) ； 連立川(Li-Chuan Lien)

《技術學刊》 20卷3期 (2005/09) Pp. 261-268

https://doi.org/10.29507/JT.200509.0005

摘要

由於高性能混凝土的組成比傳統混凝土更複雜，因而提高了混凝土抗壓強度預測的困難度，使得迴歸分析無法建立精確的預測模型。類神經網路具有建立精確預測模型的能力，因此本研究使用此技術與一個大型的實驗數據集，來建立高性能混凝土強度預測模型。此外，利用相同的實驗數據集，本研究應用非線性迴歸分析來決定三種經驗公式的係數，並比較其結果與類神經網路的結果。最後，透過抗壓強度實驗，證明類神經網路可以建立遠比經驗公式更精確之高性能混凝土強度模型。

關鍵字

高性能混凝土 ； 抗壓強度 ； 預測 ； 類神經網路 ； 經驗公式

並列摘要

Because the proportions of high-performance concrete (HPC) are more complex than those of conventional concrete, the difficulty of prediction of strength has been increased, and an accurate model cannot be induced using regression analysis. An artificial neural network has the ability of building a highly accurate predictive model; therefore, this study used this technique and a large experimental data set to build a model of HPC strength. Also, using the same experimental data set, this study employed nonlinear regression analysis to determine the coefficients of three experimental equations of strength of concrete, and compared their results with those of artificial neural networks. Finally, using experiments of compressive strength, it was proved that the artificial neural networks can build a much more accurate model than nonlinear regression analysis for the prediction of strength of HPC.

並列關鍵字

high-performance concrete ； compressive strength ； prediction ； artificial neural networks ； experimental formulae