Application of Unsupervised Fuzzy Neural Network Reasoning Model for the prediction of the strength of High-Performance Concrete

• Main Authors: Yu-Chao Chen, 陳堉照
• Other Authors: Shih-Lin Hung
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/70430347689492363565

碩士 === 國立交通大學 === 土木工程系 === 88 === In addition to the four basic ingredients of the conventional concrete, i.e., Portland cement, fine and coarse aggregates, and water, the making of HPC needs to incorporate the supplementary cementations materials, such as fly ash and blast furnace slag, and chemical admixtures such as superplasticizer. Hence, the characteristics of HPC are much more complex and hard to build an effective model to estimate the strength by mathematical model. Proposed by Hung and Jan, Unsupervised Fuzzy Neural Network(UFN) Reasoning Model has been proved an effective learning model in engineering design. In this work, a UFN reasoning model has been apply to predict the strength properties of high-performance concrete (HPC) mixes. About thousand data collected from different labs are used as training instances. For the sake of comparison, a supervised neural network with BFGS learning model is also employed to train the training data. The simulation results reveal that the UFN reasoning model can not only reason hundreds training data in reasonable computational time but also yield superior prediction of HPC strength to those generated through supervised neural network learning models.