Materials Reports  2019, Vol. 33 Issue (z1): 274-277 |
| INORGANIC MATERIALS AND CERAMIC MATRIX COMPOSITES |
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| Application Comparison of Artificial Intelligence Method in Civil Engineering Monitoring |
| DING Yang1, ZHOU Shuangxi2, DONG Jingliang2, WANG Zhongping3, ZHENG Zhiqiu2
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1 School of Architecture Engineering, Zhejiang University, Hangzhou 310058
2 School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013
3 School of Materials Science and Engineering, Tongji University, Shanghai 201804 |
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Abstract With the rapid development of computer technology, monitoring and forecasting methods in the field of civil engineering have been constantly updated. Taking the pouring process of mass concrete as the engineering background, combined with BP, GA-BP, PSO-BP, SOM, CNN, SVM and PNN, the prediction model was established. According to the measured data and prediction model, it can be concluded that the internal temperature of mass concrete increases first and then decreases within 2 days due to hydration and heat release. The error of prediction model based on SVM, PNN neural network and CNN neural network is less than 2%. The prediction error of BP network is about 10%, but the error is 5% after improvement by genetic algorithm. Combining seven kinds of artificial intelligence methods, choosing the appropriate algorithm and optimizing can provide the basis for monitoring, predicting and early warning in the field of civil engineering in the future.
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Published: 05 July 2019
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| About author:: Yang Ding is a Ph.D. candidate in Zhejiang University. His research interests is application of deep learning in civil engineering.Shuangxi Zhou received his Ph.D. degree in China General Academy of Building Materials Science in 2006. He is an associate professor in the school of civil architecture, East China Jiaotong University. His research interests are durability of concrete structures and new non-destructive testing technology for concrete. |
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