Uniaxial compressive strength (UCS) is a critical parameter in the disaster prevention of engineering projects, requiring a large budget and a long time to estimate in different rocks or the early stage of a project. If predicted accurately, the UCS of rocks significantly affects geotechnical applications. This paper develops a dataset of 734 samples from previous studies on different countries’ magmatic, sedimentary, and metamorphic rocks. Within the study context, three main factors, point load index, P-wave velocity, and Schmidt hammer rebound number, are utilized to estimate UCS. Moreover, it applies extreme learning machines (ELM) to map the nonlinear relationship between the UCS and the influential factors. Five metaheuristic algorithms, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), butterfly optimization algorithm (BOA), and sparrow search algorithm (SSA), are used to optimize the bias and weight of ELM and thus enhance its predictability. Indeed, several performance parameters are utilized to verify the proposed models’ generalization capability and predictive performance. The minimum, maximum, and average relative errors of ELM achieved by the whale optimization algorithm (WOA-ELM) are smaller than the other models, with values of 0.22%, 72.05%, and 11.48%, respectively. In contrast, the minimum and mean residual error produced by WOA-ELM are less than the other models, with values of 0.02 and 2.64 MPa, respectively. The results show that the UCS values derived from WOA-ELM are superior to those from other models. The performance indices (coefficient of determination (R2): 0.861, mean squared error (MSE): 17.61, root mean squared error (RMSE): 4.20, and value account for (VAF): 91% obtained using the WOA-ELM model indicates high accuracy and reliability, which means that it has broad application potential for estimating UCS of different rocks.

Mine geological disaster is a complex non-linear system. The traditional prediction model has the disadvantages of low prediction accuracy and poor reliability. In order to solve this problem, the open-pit mine slope displacement is taken as the research object. Based on a new algorithm extreme learning machine (ELM), the new intelligent algorithm sparrow search algorithm (SSA) are introduced to determine the weights and thresholds of the input layer and hidden layer of ELM. The open-pit mine slope displacement prediction model of improved ELM is constructed and applied to an engineering example. The results show that the root mean square error of SSA-ELM model is only a quarter of that of BP model, which is 50% higher than that of GM (1,1) and ELM models. The correlation coefficient of the prediction results of the SSA-ELM model is 0.983, and the accuracy is better than that of the traditional model. The single ELM model and the PSO-ELM model show that the SSA algorithm has better improvement effect. The SSA model has good comprehensive performance and high prediction accuracy. It is feasible to apply it to the prediction of slope displacement in open-pit mines.