Research of Evaluated Concrete Compressive Strength Applied by an Artificial Neural Network

• Main Authors: Wu Ming-Hung, 吳銘宏
• Other Authors: Wang Her-Yung
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/19814537694911483223

碩士 === 國立高雄應用科技大學 === 土木工程與防災科技研究所 === 97 === Artificial Neural Network(ANN) algorithm which is quite suitable for dealing with the changeable counting and for running mathematical calculations for problems with parameters that are related in a complicated manner. Because it can determine many of the factors related to concrete compression strength, ANN can follow the known data collection, both training and establishing the relations between the parameters automatically. The relations between the parameters do not need to be assumed, and this method is suitable for applications to relevant armored concrete component problems, namely for predicting the compression strength and multiple factors under a varying external force. Tests have already produced a large number of supplies that are similar to concrete and have predicted data such as compression strength. Using data from the Technological Civil Engineering Laboratory of SGS Taiwan, main factors including those related to water, cement, stone of six unit,stone of three unit, particulate material, W/C, design, collapse degree, design air content, sand content,unit weight, and age are used in order to predict the parameters of the concrete compression strength value, using a neural network method. The procedure extrapolates the concrete and estimates the intensity value in advance, and then with real concrete intensity contrast, it is proven that the concrete compression strength value is accurately predicted. The result of this study can be applied to advance estimates of concrete compression strength, and the results show that this method is accurate in testing the data.