以實驗計劃法與類神經網路建構混凝土的坍流度模型

Modeling Workability of Concrete Using Design of Experiments and Artificial Neural Networks

在混凝土科技中，工作度的重要性是明顯的。在規範與標準中，用以估計工作度的經驗圖表是基於未添加強塑劑與礦物攙料（如飛灰與爐灰）等材料的混凝土。對添加這些材料的混凝土，這些關係之妥當性應該要加以研究。由於這些關係的高度複雜性，傳統的迴歸分析可能不足以建構精確的模型。類神經網路是建構非線性模型的有效工具。因此，本研究以實驗計劃法與類神經網路建構一個坍流度模型。在這個模型中，坍流度是混凝土所有成份用量的函數，包括水泥、飛灰、爐灰、水、強塑劑、粗骨材，與細骨材。本研究導出下列結論：(1) 利用雛形模式找出可疑的實驗數據，並予以重新實驗，對建構精確之模型有非常顯著的助益。(2) 類神經網路可以建構一個比二階多項式迴歸分析更精確的坍流度模型。

The significance of workability in concrete technology is obvious. Thecurrent empirical diagrams and tables presented in codes and standards forestimating workability are based on tests of concrete without supplementarycementitious materials (fly ash, blast furnace slag, etc.). The validity ofthese relations for concrete with supplementary cementitious materialsshould be investigated. Because of the high complexity of these relations,conventional regression analysis is not sufficient to build an accurate model.The artificial neural network (ANN) is a powerful tool for modelingcomplex nonlinear models. Therefore, in this study, a slump flow modelhas been built using design of experiments (DOE) and ANN. In this model,the slump flow is a function of the content of all concrete ingredients,including cement, fly ash, blast furnace slag, water, superplasticizer, coarse aggregate, and fine aggregate. This study led to the following conclusions:(1)Discovering doubtful experimental data produced by using the prototypemodel and repeating these experiments is very significantly beneficial forbuilding a reliable model. (2) ANN can build a more accurate slump flowmodel than a 2-order polynomial regression can.