| dc.creator | Torre Carrillo, Ana Victoria | |
| dc.creator | Espinoza Haro, Pedro | |
| dc.creator | Ramirez Curi, Sorin Gudberto | |
| dc.creator | Moromi Nakata, Isabel | |
| dc.creator | Shuan Lucas, Luisa Esther | |
| dc.creator | Jesús Aldair, Matías Ramos | |
| dc.date | 2024-12-17 | |
| dc.date.accessioned | 2025-04-07T14:59:21Z | |
| dc.date.available | 2025-04-07T14:59:21Z | |
| dc.identifier | https://revistadelaconstruccion.uc.cl/index.php/RDLC/article/view/60241 | |
| dc.identifier | 10.7764/RDLC.23.3.568 | |
| dc.identifier.uri | https://revistaschilenas.uchile.cl/handle/2250/251225 | |
| dc.description | Cement is the fundamental binder of concrete, and its manufacture has a significant impact on the environment; therefore, it is necessary to look for eco-sustainable alternatives, including additions such as natural pozzolana, which affect the internal matrix of concrete and therefore the compressive strength and capillary absorption of concrete. In this context, prediction models for capillary absorption and compressive strength of concrete with pozzolana additions have been determined by applying linear multiple regression tools and artificial neural networks which will help reduce laboratory testing costs and times. For this purpose, 16 types of mixtures were designed with w/c ratios of 0.40, 0.45, 0.50 and 0. 55 and addition of 10, 15 and 20% of pozzolana; 160 cylindrical samples were manufactured and tested in laboratory, the values of capillary absorption and compressive strength at 28 and 56 days of curing were determined; the effect of each variable on the results obtained indicated that 15% pozzolana significantly improved the properties studied; using the data of the manufacturing variables of each design and the results of capillary absorption and compressive strength, prediction models were obtained for both properties; the best back propagation neural networks (BPNN) structure is [10,20,10,1], with R2compression=0. 9486 and R2capillary absorption=0.9756; while the models obtained with multiple linear regression obtained R2compression = 0.9391 and R2capillary absorption = 0.8693; both techniques showed a high reliability for the prediction of compressive strength and capillary absorption. | en-US |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Escuela de Construcción Civil de la Pontificia Universidad Católica de Chile | en-US |
| dc.relation | https://revistadelaconstruccion.uc.cl/index.php/RDLC/article/view/60241/66912 | |
| dc.rights | Copyright (c) 2024 Ana Victoria Torre Carrillo, Pedro Espinoza Haro, Sorin Gudberto Ramirez Curi, Isabel Moromi Nakata, Luisa Esther Shuan Lucas, Matías Ramos Jesús Aldair | en-US |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0 | en-US |
| dc.source | Revista de la Construcción. Journal of Construction; Vol. 23 No. 3 (2024): Revista de la Construccion. Journal of Construction; 568-586 | en-US |
| dc.source | 0718-915X | |
| dc.subject | Pozzolans, capillary absorption, compression, linear multiple regression, artificial neural network. | en-US |
| dc.title | Prediction of capillary absorption and compressive strength, applying multiple linear regression and artificial neural networks in concrete with natural pozzolana addition | en-US |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | resarchearticle | en-US |
