The risks faced by the mining industry have always been prominent for every walk of life in China. As the direct cause of accidents, individual unsafe behaviors are closely related to their risk perception. So, it is important to explore the factors affecting miners’ risk perception and analyze the influencing mechanisms between these factors and risk perception. The questionnaire survey method was used to collect the data of risk perception from nearly 400 respondents working in metal mines in China. Exploratory factor analysis and confirmatory factor analysis were used to analyze and process collected data. The impact of four factors affecting miners’ risk perception was verified, namely: organizational safety atmosphere, organizational trust, knowledge level, and risk communication. Then, regression analysis, Pearson correlation analysis, and structural equation model analysis were used to examine the effect of the four influencing factors on miners’ risk perception. The four influencing factors all have a positive impact on miners’ risk perception; knowledge level has the largest explained variation of miners’ risk perception, followed by risk communication. Organizational trust and organizational safety atmosphere have an indirect and positive impact on miners’ risk perception intermediated by knowledge level and risk communication. The results offer four important aspects of mine safety management to help miners establish quick and accurate risk perception, thereby reducing unsafe behaviors and avoiding accidents.
Abstract Cut blasting, in which new surfaces and relief space for subsequent blasting are created, is one of the most critical steps in the establishment of large-diameter long-hole (LDL) stopes. To reduce the damage to the chamber roof caused by stemming recoil and improve the rock breaking effect, 15 groups of small-scale model tests with minimum burdens of 3, 4, 5, 6, and 7 cm and stemming lengths of 0, 2, 4, 5, 6, and 7 cm were designed to optimize the matching relationship between the stemming length and minimum burden. First, through the model tests, values were obtained for ten evaluation indexes related to the total mass of fragments, crate size, fragment size, fragmentation energy consumption, and stemming recoil area. Then, the normal cloud combination weighting method was used to combine six subjective and objective weighting methods, and combined weights were obtained. Finally, the test schemes were optimized according to the Euclidean distance and similarity. The test results showed that the best blasting scheme involves a burden of 5 cm and a stemming length of 5 cm, followed by that involving a burden of 4 cm and a stemming length of 4 cm, and the optimal stemming length is approximately equal to the minimum burden. A field test of LDL stope cut blasting was conducted, with a stemming length of 2.2 m and a minimum burden of 2.2 m in the boreholes. The highly satisfactory field blasting effect indicates that the stemming length and minimum burden are reasonable.
Ground settlements above a tunnel as a result of tunnel construction can be predicted with the help of input variables that have direct physical significance. Several empirical and artificial intelligence methods for estimating ground settlements have been established by researchers. However, these methods have some limitations because the large number of influential factors involved makes tunnel–ground interaction complicated. In this work, a random forest (RF) was developed and employed to predict ground settlements above tunnels. To achieve this goal, tunnel geometry, geological properties, and construction parameters were investigated as input variables to utilize in the RF modeling, resulting in the maximum surface settlement value (Smax) and trough width (i) as the ground surface settlement index. To demonstrate the applicability of the RF model, two data sets associated with different features, which were obtained from a detailed investigation of different tunnel projects published in literature, were utilized for model development and were applied to check the performance capacity of the developed model. A fivefold cross-validation procedure was then applied to identify the optimal parameter values during modeling, and an external testing set was employed to validate the prediction performance of the model. Two performance measures, R2 and RMS error, were employed. The relative importance of different parameters in the prediction of ground settlements was also investigated. Findings demonstrate that the RF method provides promising results and offers an alternative means in predicting ground settlements induced by tunneling.
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