Abstract
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.
Highlights
Excavation of high rock slopes in many fields, such as transportation, hydraulic and hydropower, and mining engineering, usually involves blasting due to the high efficiency, reliable effectiveness, and low costs of blasting operations [1,2,3]
The blast-induced damage was obtained through sonic tests and the natural frequency was extracted by Sensors 2021, 21, 2473 picking power spectral density (PSD) peaks of blasting vibration monitoring data
It is important to clarify the impact of the lower bench blasting on the damage state of the upper remaining rock masses, because the blasting vibration monitoring system for the current bench blasting was arranged at the toe of the upper slope bench
Summary
Excavation of high rock slopes in many fields, such as transportation, hydraulic and hydropower, and mining engineering, usually involves blasting due to the high efficiency, reliable effectiveness, and low costs of blasting operations [1,2,3]. Unlike blasting vibration velocities that are influenced by external conditions, natural frequencies of rock masses are intrinsic characteristics and relatively simple to obtain without knowing near-field vibration data. Researchers have developed a number of techniques to identify the location and degree of damage in structures using the change in the natural frequency in structural health monitoring [48,49,50] Among those techniques of damage identification, the Bayesian approach that takes into account prior knowledge and posterior probabilities is one of the most appealing and prevailing techniques [51,52]. A Bayesian approach to predict the blast-induced damage of high rock slopes using vibration and sonic data was proposed. The blast-induced damage was obtained through sonic tests and the natural frequency was extracted by Sensors 2021, 21, 2473 picking PSD peaks of blasting vibration monitoring data. The results demonstrated that the proposed approach is feasible and efficient
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