scholarly journals Space-Time Effect Prediction of Blasting Vibration Based on Intelligent Automatic Blasting Vibration Monitoring System

2021 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Fan Chen ◽  
Gengsheng He ◽  
Shun Dong ◽  
Shunjun Zhao ◽  
Lin Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Rough Set ◽  
Significant Influence ◽  
Vibration Frequency ◽  
Fuzzy Neural Network ◽  
Vibration Velocity ◽  
Blasting Vibration ◽  
High Rise ◽  
Fuzzy Neural

The vibration produced by blasting excavation in urban underground engineering has a significant influence on the surrounding environment, and the strength of vibration intensity involves many influencing factors. In order to predict the space-time effects of blasting vibration more accurately, an automatic intelligent monitoring system is constructed based on the rough set fuzzy neural network blasting vibration characteristic parameter prediction model and the network blasting vibrator (TC-6850). By setting up the regional monitoring network of monitoring points, the obtained monitoring data are analyzed. An artificial intelligence model is used to predict the influence of stratum condition, excavation hole, and high-rise building on blasting vibration velocity and frequency propagation. The results show that the artificial intelligence prediction model based on a rough set fuzzy neural network can accurately reflect the formation attenuation effect, hollow effect, and building amplification effect of blasting vibration by effectively fuzzing and standardizing the influencing factors. The propagation of blasting vibration in a soil–rock composite stratum is closely related to the surrounding rock conditions with a noticeable elastic modulus effect. The hollow effect is regional, which has a significant influence on the surrounding ground and buildings. Besides, the blasting vibration of the excavated area is stronger than that of the unexcavated area. The propagation of blasting vibration on high-rise buildings was complicated, of which the peak vibration velocity is maximum at the lower level of the building and decreased with the rise of the floor gradually. The whip sheath effect appears at the top floor, which is related to the blasting vibration frequency and the building’s natural vibration frequency.

Influences of Tunnel Excavation Blasting Vibration on Surface High-Rise Building

2010 ◽  
Vol 163-167 ◽  
pp. 2613-2617
Author(s):  
Hai Liang Wang ◽  
Tong Wei Gao
Keyword(s):  
Vibration Frequency ◽  
Vertical Vibration ◽  
Frame Structure ◽  
Vibration Monitoring ◽  
Vibration Velocity ◽  
Blasting Vibration ◽  
Road Tunnel ◽  
Concrete Frame ◽  
High Rise ◽  
Order Of Magnitude

According to the 33 floors high building, blasting vibration monitoring had been carried on. The building, along Yunnan road tunnel of Qingdao Cross-harbor Tunnel Guide Line Project, has concrete frame structure. Monitoring data had been analyzed. Results showed that rules of vertical vibration velocity and main vibration frequency have similar relevance. Amplification effect of them was existed on the middle and top of the building. From the 2nd floor of downward ground to ground, the value of them suddenly decreased. Main vibration frequency is in the range of 101~102 order of magnitude.

Data mining method based on rough set and fuzzy neural network

2020 ◽  
Vol 38 (4) ◽  
pp. 3717-3725
Author(s):  
Jingyong Zhou ◽  
Yuan Guo ◽  
Yu Sun ◽  
Kai Wu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Rough Set ◽  
Fuzzy Neural Network ◽  
Mining Method ◽  
Data Mining Method ◽  
Fuzzy Neural

A pretreatment method of wastewater based on artificial intelligence and fuzzy neural network system

2020 ◽  
Vol 39 (2) ◽  
pp. 1711-1720
Author(s):  
He Chan ◽  
Yan Nai-He
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Saline Wastewater ◽  
Pretreatment Method ◽  
Fuzzy Function ◽  
Dissolved Oxygen Control ◽  
Fuzzy Neural

A pretreatment method of industrial saline wastewater based on Artificial Intelligence based fuzzy neural network analysis was proposed to improve the pretreatment accuracy of industrial saline wastewater. This method uses a four-layer AI fuzzy neural network model and proposes a graded fuzzy neural network model for pretreatment method of industrial saline wastewater, it includes input layer, fuzzification layer, fuzzy logical layer and output layer, and designs the framework and calculation mode of the fuzzy function block and the neural network module. Finally, the dynamic simulation experiments of dissolved oxygen control in the fifth zone and nitrate nitrogen control in the second zone are carried out based on the simulation benchmark model (BSM1) platform. The experimental results show that this approach can effectively raise the adaptive control accuracy of the system compared with PID, feed forward neural network and conventional recurrent neural network.

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