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.

To examine the dynamic response of tunnel floor slabs subjected to blasting under varying stress conditions, a numerical model was developed to simulate blasting effects at different tunnel depths. This model integrated Hopkinson bar experiments conducted under confining pressure with the Riedel–Hiermaier–Thoma (RHT) constitutive framework. The study subsequently investigated the effects of geostress fields, tunnel depth and tunnel inclination on the propagation characteristics of stress waves. Additionally, the mechanisms stress wave transmission and the damage evolution within the rock mass were analyzed. Results from the numerical simulations reveal that increasing the charge depth diminishes the dissipation of post-blasting stress waves toward the free surface, thereby concentrating stress wave propagation within the rock mass and substantially amplifying shock wave intensity and impact loading. Moreover, elevated stress levels in the surrounding rock increase the peak stress wave amplitude, constrain damage propagation on the tunnel’s upper side, and redirect more stress waves toward deeper regions of the model. Increasing tunnel inclination was also found to intensify stress concentration and augment stress wave intensity. Notably, at a tunnel inclination of 5°, stress wave intensity attains its maximum; beyond this angle, the development of stress waves exhibits irregular patterns.