scholarly journals A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2473
Author(s):  
Pengchang Sun ◽  
Wenbo Lu ◽  
Haoran Hu ◽  
Yuzhu Zhang ◽  
Ming Chen ◽  
...  
Keyword(s):  
Predictive Model ◽  
Natural Frequency ◽  
Bayesian Approach ◽  
Rock Slope ◽  
Operation Performance ◽  
Bench Blasting ◽  
Bayesian Linear Regression ◽  
Baihetan Hydropower Station ◽  
Damage Depth ◽  
High Rock Slope

The blast-induced damage of a high rock slope is directly related to construction safety and the operation performance of the slope. Approaches currently used to measure and predict the blast-induced damage are time-consuming and costly. A Bayesian approach was proposed to predict the blast-induced damage of high rock slopes using vibration and sonic data. The relationship between the blast-induced damage and the natural frequency of the rock mass was firstly developed. Based on the developed relationship, specific procedures of the Bayesian approach were then illustrated. Finally, the proposed approach was used to predict the blast-induced damage of the rock slope at the Baihetan Hydropower Station. The results showed that the damage depth representing the blast-induced damage is proportional to the change in the natural frequency. The first step of the approach is establishing a predictive model by undertaking Bayesian linear regression, and the second step is predicting the damage depth for the next bench blasting by inputting the change rate in the natural frequency into the predictive model. Probabilities of predicted results being below corresponding observations are all above 0.85. The approach can make the best of observations and includes uncertainty in predicted results.

Real-Time Assessment of Blasting Damage Depth Based on the Induced Vibration During Excavation of a High Rock Slope

2016 ◽  
Vol 39 (6) ◽  
pp. 20150187 ◽  
Author(s):  
P. Yan ◽  
Y. J. Zou ◽  
W. B. Lu ◽  
Y. G. Hu ◽  
Z. D. Leng ◽  
...  
Keyword(s):  
Real Time ◽  
Rock Slope ◽  
Damage Depth ◽  
High Rock Slope ◽  
Time Assessment

Numerical and experimental investigation of blasting damage control of a high rock slope in a deep valley

2018 ◽  
Vol 237 ◽  
pp. 12-20 ◽  
Author(s):  
Yingguo Hu ◽  
Wenbo Lu ◽  
Xinxia Wu ◽  
Meishan Liu ◽  
Peng Li
Keyword(s):  
Experimental Investigation ◽  
Rock Slope ◽  
Damage Control ◽  
High Rock Slope

HIGH ROCK SLOPE STABILITY ANALYSIS USING THE ENRICHED MESHLESS SHEPARD AND LEAST SQUARES METHOD

2011 ◽  
Vol 08 (02) ◽  
pp. 209-228 ◽  
Author(s):  
HEHUA ZHU ◽  
XIAOYING ZHUANG ◽  
YONGCHANG CAI ◽  
GUOWEI MA
Keyword(s):  
Rock Mass ◽  
Stability Analysis ◽  
Slope Stability ◽  
Least Squares ◽  
Rock Slope ◽  
Least Squares Method ◽  
Meshless Methods ◽  
Slope Stability Analysis ◽  
Rock Slope Stability ◽  
High Rock Slope

The meshless methods are particularly suitable for modeling problems with discontinuities such as joints in rock mass. The meshless Shepard and least squares (MSLS) method is a newly developed meshless method, which overcomes some limitations with other meshless methods. In the present paper, the MSLS method is extended for modeling jointed rock mass and the joint is modeled as discontinuity governing the near-field stress. A substantial high rock slope by the dam shoulder of Jinping Hydropower Station is analyzed by the developed method. Safety factors are evaluated based on the stress results along potential slip surfaces and compared with the conventional slice methods. The results demonstrate the feasibility of using the MSLS method in rock slope stability analysis and also reveal some interesting differences from the conventional slice methods. Some findings and outstanding issues demonstrated in this study are discussed in the end, which can be the topics for future development.

scholarly journals Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Tan ◽  
Ye Fen ◽  
Qi Yuan
Keyword(s):  
Neural Network ◽  
Natural Frequency ◽  
Information Fusion ◽  
Damage Identification ◽  
Back Propagation ◽  
Neural Network Models ◽  
Modal Assurance Criterion ◽  
Displacement Mode ◽  
Network Output ◽  
Damage Depth

In order to optimize the technology of the building, the damage identification of the building structure is studied. Firstly, back propagation neural network (BPNN) and information fusion technology are used to build neural network models. Secondly, the established model is trained. Finally, the displacement mode, natural frequency, Modal Assurance Criterion (MAC), and three kinds of information fusion with only one characteristic information are used as input data to analyse the results of BPNN identification damage. The results show that when the natural frequency is used as the sensitive feature of damage, the accuracy is the highest. The difference between the network output value and the expected value is the smallest, the network output is the most stable, and the network recognition effect is the best. The network output of a mixture of two damage depths is compared with the output of a single damage depth. The data of the network training set composed of the feature data with damage depth of 20 mm and 5 mm has higher accuracy and more accurate damage recognition. This research provides a reference for the optimization of building survey technology and has certain practical value.

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