## 4 Conclusions  
In this work, several well-known machine learning techniques have been explored with the aim of selecting the best in terms of predicting the compressive strength of segregated LWAC with expanded clay. The attributes involved in this problem were the particle density of the LWA, the laying time of the concrete, the vibration time, the experimental dry density of the specimen obtained after 28 days, the \(P\)-wave velocity, and the segregation index based on the \(P\)-wave velocities proposed in [6]. The KNN, RF, GBR, XGBoost and  
Figure 28: Normalised feature importance for RF, GBR and XGBoost models. (**a**) RF model; (**b**) GBR model; (**c**) XGBoost model. Figure 29: Normalised feature permutation importance for KNN, SVR and MLP models. (**a**) KNN model; (**b**) SVR model; (**c**) MLP model. SVR algorithms were implemented, and their performance was explored for varying values of the hyperparameters involved. In addition, a weighted ensemble method was devised that combined the RF, SVR and XGBoost models by using weight parameters \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), respectively. In an attempt to optimise the benefits of the variance-reducing effects of this ensemble learning technique, the amount of regularisation was explored through the parameter \(C\) of the SVR technique. The efficiency of these models was compared, both with each other and with models proposed in previous works [6; 19]. Two cross-validation procedures were applied, involving Monte Carlo and repeated \(k\)-fold cross-validation, and no significant differences in the results were found. The behaviour of these methods indicated that the KNN models based on the Manhattan distance outperformed the MLR models presented in [19]. However, this technique gave the poorest performance of those implemented in this work. All of the other techniques studied in this work (MLP, RF, SVR, GBR, XGBoost and WAE) outperformed the machine learning techniques used in previous works (MLR, ANN and RT) [6; 19]. The MLP, RF and SVR models behaved similarly, with best mean RMSEs of 3.6765, 3.6303 and 3.6591, respectively, and mean MAPEs of 15.0795%, 15.5406%, and 15.1239%. However, the RF models were the most stable (least influenced by the data splitting process), and a ranking analysis indicated that their best model was above the MLP models. The same performance was obtained for the MSE and MAE splitting criteria, and the best results were achieved with an ensemble of 100 trees, a maximum depth of five, and a proportion of samples for training each base estimator greater than or equal to 0.75. A good compromise between accuracy and the prevention of overfitting was found based on pre-pruning the tree with a minimum of 20 samples for child nodes and 10 for parent nodes. Suitable settings for the hyperparameters when training the MLP networks were the use of the L-BFGS optimiser, a ReLU activation function, and a strength of 0.1 for Ridge regularisation, which gave the best results with one hidden layer and a number of neurons between 30 and 40. However, with regard to the stability of these models, we observed that the MLP method was the most strongly influenced by the choice of the training and test data split. In relation to SVR, good configurations of parameters were obtained with the RBF kernel, \(\epsilon=0.04\), a regularisation parameter \(C\) of between 0.3 and 0.7, and \(\gamma=\frac{1}{d\text{Var}(X)}\). These models exhibited similar stability to those obtained with the best GBR, XGBoost, and WAE techniques. The optimised version of the GBR technique, XGBoost, which allows for some regularisation techniques to be applied, outperformed the other GBR models. Suitable hyperparameter settings were the use of the dark booster, a strength of 0.001 for L2 regularisation, a maximum depth of three, and between 90 and 130 boosting stages.