Using Artificial Neural Network to Predict the Effect of Blasting Densification

• Main Authors: Wei-Hung Chan, 詹偉弘
• Other Authors: 丁原智
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/u5j9ub

碩士 === 國立臺北科技大學 === 資源工程研究所 === 100 === Taiwan located on West-Pacific seismic belt, thousands of earthquakes occurred around Taiwan in a month. The most famous 921 earthquake which known as Chi-chi earthquake induced soil liquefaction and caused huge damage. Thurs, how to improve the building ground before construction is an important improvement project. This research departs in to two parts: The first part used blasting densification to improve land site. The blasting holes and explosives were designed and set. The sandy soil around the blasting sources was liquefied by high pressure shock waves which were caused by blasting. Drilling and receiving soil samples to conduct soil character tests. The site experiment factors included the weight of explosives, the depth of blasting holes, the distance of blasting holes and the vibration velocity of soil particles. The effect of blasting densification depended on the results of soil character test including water content, porosity, saturation and relative density. The second part used back-propagation neural network. It contented input layer, hiding layer and output layer. Each neurons of layer were connected by weights and biases. After training and learning repeatedly, the errors between training values and real test values were feedback to weights, and regulated the weights and biases until the network was convergence. The predicting values of water content, porosity, saturation and relative density were compared to real test values and the errors were discussed in the end. The result of experiment shows that the values of water content, porosity and saturation increased when time increased, but the values of relative density decreased when time increased; the range of error which was predicted by back-propagation neural network is under 20%, the largest error percentage is 19%.