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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]

In this study, three artificial intelligence models, namely group method of data handling, gene expression programming and random forest, are proposed to predict the ultimate bearing capacity of concrete filled steel tube stub columns. A total of 220 data samples collected from the literature was used to construct the three models. Five statistical indices were used to evaluate the performance of the three models and the other five existing design codes and two reference models. Compared with the optimal model among the seven existing models, the coefficient of variation, mean absolute percentage error, root relative squared error and integral of absolute error values of all datasets of the three models (i.e., group method of data handling, gene expression programming and random forest) were decreased by 46.47%, 56.49%, 71.35% and 65.78%; 49.82%, 59.49%, 66.88% and 64.51%; 79.27%, 84.83%, 78.64% and 83.71%, respectively; while the determination coefficient values of all datasets of the three models were increased by 7.36%, 6.79% and 8.22%, respectively. The results show that the predicted results of the three models agree well with the experimental results, and the random forest model has the best comprehensive performance. [ABSTRACT FROM AUTHOR]

The creep index plays an important role in calculating the long-term settlement of natural soft clays, so it is vital to determine the creep index quickly and accurately. However, the prediction accuracy of the existing creep index models is low. This study presents seven gene expression programming (GEP) models by using different combinations of the liquid limit wL, plasticity index Ip, void ratio e and clay content CI as input variables for the prediction of creep index. A total of 151 datasets were collected from the available literature for building and testing the GEP models. The proposed GEP models were compared with two machine learning (ML) models (i.e., back propagation neural network and random forest) and five conventional empirical models in terms of three statistical indicators. The research results showed that the prediction performances of the two proposed GEP models (i.e., with combinations C I - w L - e and C I - I p - w L - e as input, respectively) surpass those of the five conventional empirical models and two ML-based models, recommended for predicting the creep index of natural soft clays in engineering practice. [ABSTRACT FROM AUTHOR]

Accurate predictions of significant wave heights are crucial in the field of marine and ocean engineering. This paper presents two hybrid intelligent models, EN-1 and EN-2, that combine random forest (RF) and extreme learning machine (ELM) methods through direct linear weighting and gene expression programming-based nonlinear weighting methods, respectively, for the prediction of weekly mean significant wave heights. The performances of the EN-1 and EN-2 models were compared with those of 11 reference models. The results showed that the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) of the EN-1 and EN-2 models were 0.9850, 0.0567, 0.0736 and 3.7107%, 0.9940, 0.0335, 0.0487 and 2.2373%, respectively, for the training datasets and 0.9802, 0.0627, 0.0847 and 4.1461%, 0.9754, 0.0707, 0.0966 and 4.2614%, respectively, for the testing datasets, indicating that the EN-1 and EN-2 models have good predictive performance and can be effectively used for the prediction of weekly mean significant wave heights. In addition, a sensitivity analysis was conducted to investigate the influence of the input variables on the model performance. • Two hybrid models are proposed for the prediction of weekly mean significant wave heights. • The differences of the two weighting methods in improving the performance of the model are compared. • Sensitivity analysis is conducted to investigate the influence of input variables on the model performance. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP) is widely used in the field of structural engineering, for example, as a confining material for concrete. The ultimate conditions (i.e., compressive strength and ultimate axial strain) are key factors that need to be considered in the practical applications of FRP-confined concrete cylinders. However, the prediction accuracy of existing confinement models is low and cannot provide an effective reference for practical applications. In this paper, a database containing experimental data of 221 FRP-confined normal concrete cylinder specimens was collected from the available literature, and eleven parameters such as the confining stress, stiffness ratio and strain ratio were selected as the input parameters. Then, a promising machine learning algorithm, i.e., group method of data handling (GMDH), was applied to establish a confinement model. The GMDH model was compared with nine existing models, and the prediction results of these models were evaluated by five comprehensive indicators. The results indicated that the GMDH model had higher prediction accuracy and better stability than existing confinement models, with determination coefficients of 0.97 (compressive strength) and 0.91 (ultimate axial strain). Finally, a convenient graphical user interface (GUI) was developed, which can provide a quick and efficient reference for engineering design and is freely available. [ABSTRACT FROM AUTHOR]

In this study, two hybrid models, grey wolf optimizer (GWO) combined with group method of data handling (GMDH) and random forest (RF) optimized by particle swarm optimization (PSO), are proposed to predict the ultimate axial strength of concrete-filled double-skin steel tube (CFDST) columns. 139 sets of data collected from the literature were used to build the proposed models. The proposed hybrid models were compared with other machine learning models as well as empirical equations and design specifications. The results show that for all datasets, the integral absolute error (IAE), root relative squared error (RRSE), mean absolute percentage error (MAPE) and coefficient of determination (R2) predicted by PSO-RF and GWO-GMDH models are 0.057, 0.092, 0.068 and 0.992; 0.068, 0.094, 0.110 and 0.989, respectively. Compared with the single RF and GDMH models, the IAE, RRSE and MAPE values of the proposed PSO-RF and GWO-GMDH models decreased by 3.91%, 13.66% and 6.34%; 37.81%, 48.78% and 39.24%, respectively, and the R2 value increased by 1.24% and 1.67%, respectively. In addition, the factors affecting the failure of CFDST columns are analyzed through the parameter sensitivity analysis. [ABSTRACT FROM AUTHOR]

Significant wave height prediction is challenging owing to the nonlinear and nonsmooth attributes of wave heights. This study presents a hybrid model coupling group method of data handling (GMDH) with grey wolf optimizer (GWO) for the prediction of significant wave heights. The datasets were assembled from three different observations, Stations 41001, 41002 and 44004 in the Atlantic; the datasets of Stations 41001 and 41002 were used for training, and those of Station 44004 were used for testing. The performance of the GWO-GMDH model was compared with four artificial intelligence models, the GMDH, gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and back propagation (BP) neural network models, and one empirical equation derived by Buckingham π -theorem. Both regression plots and statistical indices (e.g., correlation coefficient (R), root mean squared error (RMSE), mean squared error (MSE) and mean absolute percentage error (MAPE)) were adopted to evaluate the performance of the hybrid GWO-GMDH model. The MSE, RMSE, MAPE and R values were 0.041, 0.202, 7.353% and 0.953, respectively, for the training datasets and 0.031, 0.175, 7.598% and 0.941, respectively, for the testing datasets. Compared with the single GMDH model, the statistical indices of the training datasets of the hybrid GWO-GMDH model were almost the same; however, the MSE, RMSE and MAE values decreased by 24.39%, 13.37% and 7.95%, respectively, and the R value increased by 2.28% in the testing datasets. Compared with the GEP, BP, and ANFIS models and empirical equation models, the GWO-GMDH model also shows high accuracy and robustness, especially compared with empirical formulations. In addition, a graphical user interface (GUI) was developed to facilitate the application of practical engineering use. • Propose a hybrid GWO-GMDH model to predict the monthly mean significant wave heights. • GWO algorithm was used to optimize the hyperparameters of GMDH. • Compare the GWO-GMDH with GEP, BP, ANFIS models and empirical equation. • Construct a GUI of GWO-GMDH for practical engineering. [ABSTRACT FROM AUTHOR]

A low deviation creep coefficient prediction model is vital for engineering practice related to soft clays. However, current empirical methods for calculating creep coefficients are not sufficiently reliable. This study presents a new hybrid intelligent model that couples random forest (RF) and support vector machine (SVM) by weighting for prediction of creep coefficient of soft clays. The genetic algorithm (GA) is used to search the optimum hyperparameters of the proposed model. A total of 151 datasets collected from the literature were used to construct the proposed model. Limited by the size of the dataset, k-fold cross-validation is used in the optimization processes. The obtained results show that the forecasting performances of the proposed model surpass those of the empirical methods, recommended for the prediction of creep coefficient in practical engineering. In addition, for the convenience of practical engineering application, we have built a web page program for the proposed hybrid model, and the users can use the model by visiting the website. • A hybrid intelligent model coupling random forest and support vector machine was proposed to predict the creep index. • Genetic algorithm was used to search the optimum hyperparameters of the proposed models. • Robustness and parametric analysis were conducted to verify the reasonableness of the hybrid model. [ABSTRACT FROM AUTHOR]