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Home > Archives > Volume 16, No 6 (2018) > Article

DOI: 10.14704/nq.2018.16.6.1598

Monitoring Information Pre-warning System of Foundation Pit Engineering Based on Human Brain Cortex RBF Neural Network

Xuan Ji, Hesong Hu, Zhuo Yang, Mengxiong Tang


For the requirements of information, integration and sharing of foundation pit monitoring, a monitoring information pre-warning system of foundation pit engineering based on human brain cortex RBF neural network Kalman filtering algorithm has developed on the Revit platform. The neural network algorithm is embedded in the system to achieve scientific pre-warning of system through the powerful de-noising function of human brain cortex RBF neural network Kalman filtering algorithm. At the same time, the system also boasts functions, such as storage, processing, analysis, and inquiry of monitoring information and automation output. The system relies on the Revit platform to realize information sharing and multi-person cooperation, which improves the running efficiency under the network environment and provides a powerful information platform for foundation pit monitoring.


Human Brain Cortex RBF Neural Network, Kalman Filtering Algorithm, Pre-warning System, BIM Integrated Management

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Burke LI, Rangwala S. Tool condition monitoring in metal cutting: a neural network approach. Journal of Intelligent Manufacturing 1991; 2(5): 269-80.

Cheng CS, Cheng SS. A neural network-based procedure for the monitoring of exponential mean. Computers & Industrial Engineering 2001; 40(4): 309-21.

Dornfeld DA, DeVries MF. Neural network sensor fusion for tool condition monitoring. CIRP Annals-Manufacturing Technology 1990; 39(1): 101-05.

Hong GS, Rahman M, Zhou Q. Using neural network for tool condition monitoring based on wavelet decomposition. International Journal of Machine Tools and Manufacture 1996; 36(5): 551-66.

Iliyas SA, Elshafei M, Habib MA, Adeniran AA. RBF neural network inferential sensor for process emission monitoring. Control Engineering Practice 2013; 21(7): 962-70.

Kim S, Yoon C, Kim, B. Structural monitoring system based on sensitivity analysis and a neural network. Computer-Aided Civil and Infrastructure Engineering, 2010; 15(4), 189-95.

Laskaris N, Fotopoulos S, Papathanasopoulos P, Bezerianos A. Robust moving averages, with Hopfield neural network implementation, for monitoring evoked potential signals. Electroencephalography and Clinical Neurophysiology/ Evoked Potentials Section 1997; 104(2): 151-56.

Lee JW, Kirikera GR, Kang I, Schulz MJ, Shanov VN. Structural health monitoring using continuous sensors and neural network analysis. Smart Materials and Structures 2006; 15(5): 1266.

Rafiee J, Arvani F, Harifi A, Sadeghi MH. Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical Systems and Signal Processing 2007;21(4): 1746-54.

Salles G, Filippetti F, Tassoni C, Crellet G, Franceschini G. Monitoring of induction motor load by neural network techniques. IEEE Transactions on Power Electronics 2000; 15(4): 762-68.

Saxena A, Saad A. Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing 2007; 7(1): 441-54.

La Scala M, Trovato M, Torelli F. A neural network-based method for voltage security monitoring. IEEE Transactions on Power Systems 1996; 11(3): 1332-41.

Su H, Chong KT. Induction machine condition monitoring using neural network modeling. IEEE Transactions on Industrial Electronics 2007; 54(1): 241-49.

Verikas A, Malmqvist K, Bacauskiene M, Bergman L. Monitoring the de-inking process through neural network-based colour image analysis. Neural Computing & Applications 2000; 9(2): 142-51.

Wang G, Cui Y. On line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing 2013; 24(6): 1085-94.

Xiaoli L, Yingxue Y, Zhejun Y. On-line tool condition monitoring system with wavelet fuzzy neural network. Journal of Intelligent Manufacturing 1997; 8(4): 271-76.

Yuan S, Wang L, Peng G. Neural network method based on a new damage signature for structural health monitoring. Thin-Walled Structures 2005; 43(4): 553-63.