A Machine Learning Approach to Prediction of the Compressive Strength of Segregated Lightweight Aggregate Concretes Using Ultrasonic Pulse Velocity
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Título: | A Machine Learning Approach to Prediction of the Compressive Strength of Segregated Lightweight Aggregate Concretes Using Ultrasonic Pulse Velocity |
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Autor/es: | Migallón, Violeta | Penadés Migallón, Héctor | Penadés, Jose | Tenza-Abril, Antonio José |
Grupo/s de investigación o GITE: | Computación de Altas Prestaciones y Paralelismo (gCAPyP) | Tecnología de Materiales y Territorio (TECMATER) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Ingeniería Civil |
Palabras clave: | Concrete | Lightweight aggregate | Prediction | Compressive strength | Machine learning | Average weighted ensemble |
Fecha de publicación: | 2-feb-2023 |
Editor: | MDPI |
Cita bibliográfica: | Migallón V, Penadés H, Penadés J, Tenza-Abril AJ. A Machine Learning Approach to Prediction of the Compressive Strength of Segregated Lightweight Aggregate Concretes Using Ultrasonic Pulse Velocity. Applied Sciences. 2023; 13(3):1953. https://doi.org/10.3390/app13031953 |
Resumen: | Lightweight aggregate concrete (LWAC) is an increasingly important material for modern construction. However, although it has several advantages compared with conventional concrete, it is susceptible to segregation due to the low density of the incorporated aggregate. The phenomenon of segregation can adversely affect the mechanical properties of LWAC, reducing its compressive strength and its durability. In this work, several machine learning techniques are used to study the influence of the segregation of LWAC on its compressive strength, including the K-nearest neighbours (KNN) algorithm, regression tree-based algorithms such as random forest (RF) and gradient boosting regressors (GBRs), artificial neural networks (ANNs) and support vector regression (SVR). In addition, a weighted average ensemble (WAE) method is proposed that combines RF, SVR and extreme GBR (or XGBoost). A dataset that was recently used for predicting the compressive strength of LWAC is employed in this experimental study. Two different types of lightweight aggregate (LWA), including expanded clay as a coarse aggregate and natural fine limestone aggregate, were mixed to produce LWAC. To quantify the segregation in LWAC, the ultrasonic pulse velocity method was adopted. Numerical experiments were carried out to analyse the behaviour of the obtained models, and a performance improvement was shown compared with the machine learning models reported in previous works. The best performance was obtained with GBR, XGBoost and the proposed weighted ensemble method. In addition, a good choice of weights in the WAE method allowed our approach to outperform all of the other models. |
Patrocinador/es: | This research was funded by MCIN/AEI/10.13039/501100011033, grant PID2021-123627OB-C55 and by “ERDF A way of making Europe”. |
URI: | http://hdl.handle.net/10045/131832 |
ISSN: | 2076-3417 |
DOI: | 10.3390/app13031953 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/app13031953 |
Aparece en las colecciones: | INV - TECMATER - Artículos de Revistas INV - gCAPyP - Artículos de Revistas |
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