Because natural coarse aggregates were depleting rapidly, concrete industry has been trended toward substitute aggregates from industrial by-products or waste. One of the waste materials is oil palm ash (OPS), which is widely generated in the processing of palm oil in the tropics. Concretes made with OPS to estimate the compressive strength (CS) is cost and time consuming. This study aims to propose novel hybrid models by concepts of extreme gradient boosting (XGB) model optimized with different optimization algorithms such as sinecosine algorithm, multiverse optimization algorithm (MVO), and particle swarm optimization for predicting the uniaxial CS (UCS) of oil palm shell lightweight aggregate concrete (OPS). Also, the multivariate adaptive regression spline model is also developed to present a meaningful relationship between input and output variables. To this aim, a data set containing data samples for concrete made with OPS was gathered from the published literature. Results show that all models have acceptable performance in predicting the UCS, representing the admissible correlation between observed and predicted values and models' robustness. In the training step, the value of R 2 , the root mean square error, and the variance accounted factor for MVO-XGB are 0.9713, 1.5777, and 97.129. These values in testing phase are 0.9019, 2.6786, and 89.158. Therefore, the MVO-XGB model outperforms others, and the results demonstrate the ability of the MVO algorithm to determine the optimal value of XGB parameters.

Aiming at the problems of low accuracy, low efficiency, and many parameters required in the current calculation of rock slope stability, a prediction model of rock slope stability is proposed, which combines principal component analysis (PCA) and relevance vector machine (RVM). In this model, PCA is used to reduce the dimension of several influencing factors, and four independent principal component variables are selected. With the help of RVM mapping the nonlinear relationship between the safety factor of slope stability and the principal component variables, the prediction model of rock slope stability based on PCA-RVM is established. The results show that under the same sample, the maximum relative error of the PCA-RVM model is only 1.26%, the average relative error is 0.95%, and the mean square error is 0.011, which is far lower than that of the RVM model and the GEP model. By comparing the results of traditional calculation method and PCA-RVM model, it can be concluded that the PCA-RVM model has the characteristics of high prediction accuracy, small discreteness, and high reliability, which provides reference value for accurately predicting the stability of rock slope.