Identification for Bolt-Surrounding Rock System Based on Wavelet Neural Network

2012 ◽  
Vol 204-208 ◽  
pp. 738-742
Author(s):  
Jun Qiang Hu ◽  
Yong Xing Zhang ◽  
Jian Gong Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analytical Method ◽  
Dynamic Testing ◽  
Wavelet Neural Network ◽  
Surrounding Rock ◽  
Structural System ◽  
Rock System ◽  
Anchoring System

The dynamic testing is widely used in undestructive testings of bolt’s anchoring quality. But it’s difficult to estimate bolt’s anchoring quality according to the dynamic response of bolt. The bolt’s anchorage quality depends mainly on bolt-surrounding rock structural system which features must be identified. A new analytical method used in identification for bolt-surrounding rock structural system is put forward, which combine with advantages of wavelet analysis and artificial neural network. The results indicate that this wavelet neural network after training can best identify the bolt’s side rigidity factors and can be a useable intelligentized mean to assess the quality of bolt’s anchoring system.

scholarly journals Application of Wavelet Neural Network in Building Settlement Prediction

2020 ◽  
Vol 198 ◽  
pp. 03014
Author(s):  
Ruijie Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Building Design ◽  
Wavelet Neural Network ◽  
Deformation Monitoring ◽  
Polynomial Method ◽  
Observation Data ◽  
Bp Artificial Neural Network ◽  
Artificial Neural

Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

The Application Process of Wavelet Neural Network in Intelligent Evaluation of the Quality of Engineering Anchor Poles

2011 ◽  
Vol 243-249 ◽  
pp. 2969-2972
Author(s):  
Rui Jun Li ◽  
Ya Qing Shi ◽  
Jian Suo Ma ◽  
Xi Yan Jiang
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Application Process ◽  
Destructive Testing ◽  
Network Training ◽  
Quality Detection ◽  
Non Destructive ◽  
The Ideal

Most detection means on the anchorage integrity today still remain on the destructive testing level, which can hardly meet the actual needs of quality detection on large volumes of anchor poles in the anchorage engineering. This paper presents the application process of wavelet neural network in the non-destructive intelligent testing on the quality of engineering anchor poles. Taking the project of "Management Buildings and Museum of China Marine Sports School" in Qingdao as an example, this paper uses neural toolbox of MATLAB to do the network training by selecting training and simulation samples. The ideal training results indicate that with the help of neural toolbox of MATLAB, the application process of wavelet neural network can not only make intelligent evaluation of the quality of engineering anchor poles, but also make up traditional means, which can not detect large volumes of anchor poles.

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