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This paper presents an empirical model for predicting the uniaxial compressive strength (UCS) of rocks using gene expression programming (GEP). A total of 44 datasets collected from the literature was used to construct the GEP model. The GEP model developed is evaluated using four conventional regression models and an artificial neural network (ANN) model in terms of three statistical indices. The comparison results confirmed that the proposed GEP model has the lowest root mean square error (RMSE) and the highest coefficient of determination ($R^2$) and correlation coefficient ($R$) values compared to the four conventional regression models and the ANN model in the literature. It is concluded that the proposed GEP model can be applied to predict the UCS of rocks.

It is necessary to make an accurate assessment of uniaxial compressive strength (UCS) for rock mass classification and rock engineering design. However, there are many shortcomings in the conventional tests for UCS of rocks. The aim of this study is to present a hybrid model by integrating the genetic algorithm (GA) into the least-squares support vector machine (LSSVM) to predict the UCS of rock materials. The GA technique was utilized to improve the forecasting accuracy of the proposed LSSVM. To develop the proposed hybrid GA–LSSVM model, four main factors including the block punch index, point load strength, Schmidt rebound hardness and ultrasonic P-wave velocity were considered as input variables, while the UCS of rock materials was the output. A comparison was conducted among the proposed GA–LSSVM, the adaptive neuro-fuzzy inference system, the fuzzy inference system, the artificial neural network and the statistical method in accordance with three statistical indexes. The results of the comparisons show that the developed GA–LSSVM model has great potential to accurately estimate the UCS of rock materials.

Fiber-reinforced polymer (FRP) is an excellent building material for strengthening concrete structures, but it is difficult to accurately evaluate the bond strength of FRP-to-concrete due to the influence of various parameters. In this study, a novel hybrid model which combines particle swarm optimization (PSO) with random forest (RF) was proposed to predict the bond strength of FRP-to-concrete. The PSO algorithm was used to optimize the hyperparameters of the RF model. A total of 749 specimens collected from the literature were used to develop the proposed PSO-RF model. Each sample contains 11 parameters required for the model. These 11 parameters are (1) the compressive strength of concrete, (2) the tensile strength of concrete, (3) the width of concrete specimen, (4) the maximum aggregate size of concrete, (5) the tensile strength of FRP, (6) the thickness of FRP, (7) the elastic modulus of FRP, (8) the tensile strength of adhesive, (9) the bond length of FRP, (10) the bond width of FRP, and (11) the bond strength of FRP-to-concrete. The proposed PSO-RF model was compared with other machine learning models as well as ten empirical equations. Six statistical indices, namely root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Willmott's Index of Agreement (WIA), and Legates-McCabe's Index (LM) were used to evaluate the prediction performance of the abovementioned models. The results show that the RMSE, MAE, R2, NSE, WIA and LM values of the PSO-RF model are 1.529 kN, 0.942 kN, 0.986, 0.984, 0.996 and 0.892, respectively, for the training datasets and 2.672 kN, 1.967 kN, 0.963, 0.961, 0.989 and 0.761, respectively, for the test datasets. It can be concluded that the proposed PSO-RF model has the best comprehensive performance in predicting the bond strength of FRP-to-concrete. In addition, the sensitivity analysis of the PSO-RF model was also conducted in this study. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a powerful tool that has proven highly effective in many applications, but creating new deep learning models is becoming increasingly challenging. However, in some fields, such as fluid dynamics, theoretical models can help design powerful DL models. Based on the existing air2water (A2W) model, this paper proposes a hybrid neural network model, DL-A2W, which combines the long short-term memory (LSTM) neural network with the A2W model to predict lake water temperature. The DL-A2W model was established using the datasets of the UK Centre for Ecology & Hydrology, and the performance of the model was evaluated through three experiments. Compared with other models, the DL-A2W model has the lowest mean absolute error (MAE), mean absolute percent error (MAPE), root mean squared error (RMSE), and the highest Nash-Sutcliffe efficiency coefficient (NSC) at any given prediction step. The values of MAE, MAPE, RMSE and NSC of the DL-A2W model on the test set were 0.223–0.388, 1.946–3.296%, 0.375–0.647 and 0.985–0.995, respectively. The results show that the DL-A2W model has good generalization ability and portability, and can accurately perform multi-step ahead prediction of lake water temperature. [ABSTRACT FROM AUTHOR]