Abstract
Blasting demolition is a popular method in the area of building demolishing. Due to the complex process of the building components’ collapse, it is difficult to predict the collapse-induced ground vibrations. As the accuracy of the empirical equation in predicting the collapse-induced ground vibration is not high, there is a significant risk of damage to the surrounding structures. To mitigate this risk, it is necessary to control and predict the peak particle velocity (PPV) and dominant frequency of ground vibration with higher accuracy. In this study, the parameters on the PPV and frequency of collapse-induced ground vibration are analyzed based on the Hertz theory. Then, fall tests are performed to simulate the collapse process of structural components and to investigate the characteristics of influential parameters on PPV and frequency. Using kernel density estimation (KDE) and Pearson correlation, the PPV and frequency are correlated with the distance from the falling point to the monitored point (R) and the mass of the falling structural component (M). Using recorded ground vibration data, the PPV and frequency are predicted using an extreme learning machine in combination with gray wolf optimization. The efficiency of the proposed algorithm is compared with other predictive models. The results indicate that the accuracy pre-diction of the proposed algorithm is better than those of plain extreme learning machines and the empirical equations, which indicates that the approach can be applied for PPV and frequency prediction of collapse-induced ground vibrations during blasting demolition.
Highlights
Due to the rapid urban and industrial development and growth, many unsafe buildings, chimneys, and bridges need to be demolished
artificial neural networks (ANNs) [14,15,16] have been adopted to predict the ground vibration in blasting engineering with a higher degree of correlation, and the results showed that the prediction performance of machine learning-based models is better than that of the empirical equations
According to the fall tests, H, M and R should be considered as the input neurons to predict the collapse-induced ground vibration
Summary
Due to the rapid urban and industrial development and growth, many unsafe buildings, chimneys, and bridges need to be demolished. As a rapid and economical means of concrete fragmentation, blasting demolition is widely used in demolition engineering. Many engineers worry are concerned that the collapse of structures may cause ground vibrations, which can threaten the safety of surrounding urban structures and communities (e.g., tunnels, underground pipelines, and aboveground buildings). Experimental studies and numerical simulations have been used to investigate the acceleration time series of the ground vibration at specific distances during blasting demolition [3,4,5,6,7]. To investigate the collapse-induced ground vibrations, the collapse process of structural components is often simplified through fall tests or dynamic compaction
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