Application of Modularized Neural Network to Predicting the Strength of High Performance Concrete

• Main Authors: Ming-Kuan Tsai, 蔡閔光
• Other Authors: Shin-Lin Hung
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/91885448748645719345

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. For overcoming the disadvantage of standalone neural network learning model, modularized neural networks divide the original problem into several little aspects, which could be learned easily and calculate. This work presents a modularized neural network 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, the training instances are also trained using two standalone neural networks, one for conventional concrete and the other for HPC. Moreover, the sensitive analysis is employed to find the strength relationship between the input and output data. The simulation results reveals that the modularized neural network can reasonably predict the strength of HPC.