RESUMEN

Procida Island, located in the Gulf of Naples (southern Italy), is characterized by steep cliffed coasts, articulated in a succession of headlands and small embayments with narrow pocket beaches, such as Ciraccio and Chiaia, often characterized by instability. In this study, a methodology for coastal cliff susceptibility assessment has been conceived based on hydraulic and geomorphological characteristics, which supported the construction of a Cliff Stability Index (CSI). The geomorphological characteristics are related to the whole cliff face, the cliff material resistance, and the cliff failure mechanisms. The hydraulic actions on the cliff are related to the wave impact which is exerted by the breaking waves once the wave run-up distance exceeds the beach width. The index takes into account the slope of the cliff, the rock strength, the wave energy at the cliff base produced by the broken wave and the presence of defence structures at the cliff base. The resulting index classification, obtained by addition of the partial sub-indices, has been compared with the observed coastal cliff evolution from 1954 to 2021.

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RESUMEN

In order to evaluate the stability of deep surrounding rock, all of the affecting factors should be theoretically identified. However, some factors have slight impacts on the stability of deep surrounding rock compared with others. To conduct an effective risk assessment, key factors should be first extracted. The analytic hierarchy process (AHP) and grey relation analysis (GRA) methods are integrated to determine the key factors. First, the AHP method is applied to sort the factors by calculating the weights of them. Seven out of fifteen factors are extracted as the key factors, which account for 80% of the weights. Further, the GCA method is used to validate the effects of these key factors by analyzing the correlation between the performance of each factor and that of the reference. Considering the influence of these key factors and experts' judgements, the multilevel fuzzy comprehensive evaluation method is adopted to obtain the risk level of the deep surrounding rock stability. Finally, the risk assessment of the deep surrounding rock in the E-Zhuang coal mine of Chinese Xinwen Mining Area illustrates the operability of the proposed method.

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RESUMEN

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.