Blasting excavation is widely used in mining, tunneling and construction industries, but it leads to produce ground vibration which can seriously damage the urban communities. The peak particle velocity (PPV) is one of main indicators for determining the extent of ground vibration. Owing to the complexity of blasting process, there is controversy over which parameters will be considered as the inputs for empirical equations and machine learning (ML) algorithms. According to current researches, the burden has controversial impact on the blast-induced ground vibration. To judge whether the burden affects blast-induced ground vibration, the data of ground vibration considering burden have been recorded at the Wujiata coal mine. Correlation coefficient is used to analyze the relationship between variables, the correlation between the distance from blasting center to monitored point (R) and peak particle velocity (PPV) is greatest and the value of correlation coefficient is − 0.67. This study firstly summarizes the most common empirical equations, and a new empirical equation is established by dimension analysis. The new equation shows better performance of predicting PPV than most other empirical equations by regression analysis. Secondly, the machine learning is confirmed the applicability of predicting PPV. Based on the performance assessments, regression error characteristic curve and Uncertainty analysis in the first round of predicting PPV, the random forest (RF) and K-Nearest Neighbors (KNN) show better performance than other four machine learning algorithms. Then, in the second round, based on the artithmetic optimization algorithm (AOA), the optimized random forest (AOA-RF) model as the most accurate model compared with the optimized K-Nearest Neighbors (AOA-KNN) presented in the literature. Finally, the points of predicted PPV which have been informed of danger are marked based on Chinese safety regulations for blasting.

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.

Alkali-activated slag (AAS) is an environmentally friendly green cementitious material that can replace ordinary Portland cement (OPC) and has attracted extensive research by scholars all over the world. However, research regarding its creep performance has been lacking, which in turn affects its further application. The creep of alkali-activated slag concrete is large, and fiber addition has been shown to improve this problem. Polypropylene (PP) fiber has good alkali resistance and is economical. This paper studies the effect of the stress–strength ratio and fiber length on the creep property of PP fiber-reinforced alkali-activated slag (FRAAS) concrete. At the stress–strength ratio of 0.15, PP fiber addition is able to greatly reduce the creep of concrete. When the stress–strength ratio increases, the shorter fiber loses the anchoring force and the holes caused by the longer fiber crack. This in turn leads to the deterioration of the inhibition effect on concrete creep. The CEB-FIP 2010 model is highly accurate, but the final value prediction is small. The early prediction value of the GL2000 model is rather large and conservative. The creep coefficient of the prediction model and the measured secant modulus of PP FRAAS concrete with different fiber lengths under different stress–strength ratios may solve the issue of creep prediction.

The dynamic response law of the vibration cavity effect in the adjacent large-section rectangular coal roadways induced by deep-hole bench blasting vibrations was deeply revealed, and the prediction accuracy of the PPV in the coal roadway was improved. The vibration equations of the coal roadway were derived based on the stress wave propagation theory and the wave-front momentum conservation theorem. Based on coal roadway vibration monitoring data and numerical simulations, the cavity effect and vibration response characteristics of the coal roadway induced by deep-hole bench blasting under varying blast source distances and relative angle conditions were analyzed. A predictive model for PPV of rectangular coal roadway surrounding rock, incorporating the relative angle as one of the key influencing factors, was developed. The results showed that the presence of cavities and changes in the relative angle enhance the asymmetry of the dynamic response of blasting stress waves near the free surfaces of the surrounding rock on each side of the coal roadway, resulting in significant differences. Moreover, as the blasting distance decreases, the cavity effect tends to promote greater PPV differences on each side of the coal roadway. The prediction model exhibited improved accuracy by about 15.6% compared to the traditional Sadovski equation for the face-blasting side of the coal roadway. It demonstrates better adaptability and predictive capability. This research provides a theoretical basis for the dynamic response of adjacent large-section rectangular coal roadways and for preventing dynamic instability failures in open-pit mining.

This study systematically analyzes the influence of the charge length-to-diameter ratio and stemming length on the radius and volume of blasting craters in coal and rock blasting crater tests to effectively address the challenge of achieving coal–rock separation in mixed blasting construction. In addition, it examines the energy distribution mechanism of blasting fragmentation and establishes characteristic equations for coal and rock blasting craters. Numerical simulations and blasting tests are conducted to investigate the casting effect of rock benches and the fragmentation characteristics of coal and rock benches under different charge structures. The results indicate that when the ratio of charge length to stemming length exceeds 0.91 and 0.74 for the coal and rock benches, respectively, the utilization rate of explosive energy for rock fragmentation gradually surpasses that for rock throwing. The charging structure is identified as a key factor in achieving coal–rock mixed blasting and separation mining. The explosive energy is effectively utilized with a bottom interval length of 2 m for rock benches and a stemming length ranging from 2.5 to 3 m for coal seams. This configuration raises the connectivity of rock damage cracks, improves the distribution of tensile cracks at the top of the coal seam, and prevents bulging or coal–rock interactions (blasting mixing) at the coal–rock interface. The findings demonstrate that the optimized charging structure effectively achieves separate mining in coal–rock mixed blasting, fulfilling the requirement of avoiding coal–rock mixing during blasting. The research provides valuable mining strategies and technical experience for achieving separate mining in coal–rock mixed blasting in open-pit coal mines and improving the recovery of thin coal seams.