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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.

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Soundless chemical demolition has great potential for application in the field of building demolition in densely built areas. However, the application of this method was limited due to less guidance can be applied to reinforced concrete. Thus, this study investigated the effect of longitudinal reinforcement on the crushing degree of concrete columns. Experimental results showed that the number of steel bars had a greater impact on the development of crack width than diameter. Moreover, the longitudinal reinforcement had a greater impact on the transverse cracks than other types of cracks. This study introduced fractal dimension and characteristic value of specimen to describe the overall crushing degree and reinforcement arrangement of the specimens, respectively. The fractal dimension increased with an increase in the characteristic value. This study explored the influence of longitudinal reinforcement on the crushing degree of concrete columns, which laid the foundation for soundless chemical demolition of buildings in densely built areas.