## 1 Introduction
In [42], a comparative study of machine learning methods was conducted in the context of predicting the strength of concrete with blast furnace slag. \(K\)-nearest neighbours (KNN), SVR and random forest (RF) achieved high levels of prediction accuracy. In [43], MLP, RF and KNN algorithms were used to predict the compressive strength of concrete mixed with ground granulated blast furnace slag and fly ash. The results showed similar performance for the RF and MLP regression models, which outperformed the KNN model. Moreover, RF was the most stable and was the least influenced by the data splitting process. Recently, in [44; 45], various machine learning algorithms were proposed to predict the compressive strength of lightweight concrete. In [44], Gaussian progress regression (GPR), SVR, and ensemble learning were applied to a dataset consisting of 120 data points. The predictor variables were cement content, water content, fine aggregate, normal weight coarse aggregate, lightweight coarse aggregate, and water-cement ratio. The dataset was partitioned into 70% for training and 30% for testing. To obtain the best fitted models, 10-fold cross-validation was applied to the training dataset. The optimised GPR and SVR models obtained the best accuracy, with training correlation coefficients of 0.9933 and 0.9947, respectively, and testing correlation coefficients of 0.8915 and 0.8882. The optimised SVR gave an RMSE of 3.2988 for the training dataset and 4.6111 for the test dataset, while the optimised GPR achieved values for the RMSE of 1.7982 and 4.4002, respectively. The study performed in [45] utilised 420 data points and applied the SVR, ANN, RT, GPR, and XGBoost algorithms to predict the compressive strength of LWAC. The predictor variables included cement content, sand content, water-cement ratio, quantity of LWA, normal aggregate content, density of LWA, water absorption of LWA, proportion of superplasticiser, curing time, fly ash content, and type of LWA. To obtain the best models, the parameters involved in the algorithms and the percentages of the data used for training, validation and testing were varied. The best results were achieved with the GPR, RT and SVR algorithms, although the GPR models outperformed all of the other models, with a training RMSE of 1.34 and a testing RMSE of 5.06. The values obtained for the mean absolute error (MAE) were 0.69 and 3.01, respectively. In this work, we focus on the use of machine learning techniques to predict the compressive strength of LWAC with expanded clay using ultrasonic pulse velocity. The LWAC is compacted for different vibration times in order to segregate the produced concrete. The detailed experimental procedure for obtaining the dataset used in this work is explained in [6] and summarised in Section 2.2. KNN, ANN, SVR, RF and several ensemble algorithms are employed to estimate the compressive strength of LWAC using ultrasonic pulse velocity. In addition, a weighted ensemble (WAE) algorithm that combines RF, SVR and XGBoost is proposed. The input variables include the fixed density of LWAC, the particle density of LWA, the concrete laying time, the vibration time, the experimental dry density, the \(P\)-wave velocity and the segregation index based on \(P\)-wave velocities. Section 2 describes the materials and methods used in this work. After providing an overview of machine learning techniques and the methodology used to evaluate the models, Section 2 also explains the problem addressed in this paper. Section 3 analyses the behaviour of the proposed machine learning methods and discusses the numerical results obtained in this work. For each machine learning technique, an exhaustive analysis of its behaviour is presented based on the values of the parameters and hyperparameters involved. Once the best models have been obtained for each technique, they are compared to find the best machine learning techniques to predict the compressive strength of LWAC.