## 4 Conclusions
These XGBoost models achieved a mean determination coefficient of approximately 0.8773, a mean RMSE of 3.1396, a mean MAE of 2.4371, and a mean MAPE of 13.6238%, whereas the values obtained with the GBR model were 0.8765, 3.15, 2.4396 and 13.6751%, respectively. However, our proposed WAE method achieved the best prediction results for the compressive strength of LWAC, with weights of \(0\leq\alpha_{1}\leq 0.15\), \(0.65\leq\alpha_{3}\leq 1\) and \(\alpha_{2}=1-\alpha_{1}-\alpha_{3}\). We postulate that WAE methods combining SVR and XGBoost, with a value of \(\alpha_{2}\) between 0.15 and 0.20, and a value of \(\alpha_{3}\) between 0.80 and 0.85 represent the best strategies. With these weight parameters, our WAE models achieved a mean determination coefficient of about 0.878, a mean RMSE of about 3.127, a mean MAE of about 2.415 and a mean MAPE of 13.4%. This is coherent with the results of our ranking analysis of the best models for each technique, which identified the WAE models with weights in these intervals as the best options, followed by the XGBoost and GBR models, and a third group consisting of the RF, SVR and MLP models. This ranking finished with the ANN, RT, KNN and MLR models, in this order. Our analysis of the importance of the input variables in each machine learning technique showed that for the RF, MLP, SVR and GBR models, the most informative variables were the LWA particle density, the LWAC fixed density and the experimental dry density, although the order of importance of some of these variables differed. However, for the worst model (KNN), only two of these three variables were the most important (the LWAparticle density and the LWAC fixed density). The XGBoost models, and hence the WAE models, had more informative features and outperformed all other techniques discussed in this work. These two methods included more important variables than the other techniques in terms of predicting the compressive strength of segregated LWAC (experimental dry density, segregation index, \(P\)-wave velocity, concrete lying time and LWA particle density). The most important of these five variables were the experimental dry density, the segregation index and the \(P\)-wave velocity. The use of non-destructive techniques in the evaluation of the quality of concrete allows for monitoring of the condition of concrete and ensuring its proper production during construction. The large variability in the aggregates, types of LWA, cement, target density of concrete, and the additions and additives used for its production makes it difficult to correlate non-destructive techniques with strength quality. The results of our study demonstrate that computational intelligence models are reliable for use in predicting the compressive strength of LWAC using ultrasonic pulse velocity and would allow for continuous verification of the quality of the LWAC, both for industrialised construction and during the placement of concrete, since they include more variables in the learning process. Conceptualisation, V.M., J.P. and A.J.T.-A. ; methodology, V.M. and J.P.; software, V.M., H.P., and J.P.; validation, V.M., H.P., J.P. and A.J.T.-A. ; formal analysis, V.M. and H.P. ; investigation, V.M., H.P. and J.P.; data curation, V.M. and A.J.T.-A. ; writing--original draft preparation, V.M., H.P. and J.P.; writing--review and editing, V.M., J.P. and A.J.T.-A. ; supervision, V.M. and J.P.; All authors have read and agreed to the published version of the manuscript. This research was funded by MCIN/ AEI/10.13039/501100011033, grant PID2021-123627 OB-C55 and by \"ERDF A way of making Europe\". The data are contained within the article. The authors declare no conflict of interest.