PreprintArticleVersion 1This version is not peer-reviewed
Predictive Assessment and Compressive Strength Test of Geopolymer Concrete Mix as a Partial Replacement to Conventional Concrete Mix with Artificial Neural Network Application
Version 1
: Received: 28 July 2024 / Approved: 30 July 2024 / Online: 30 July 2024 (11:03:57 CEST)
How to cite:
Labiano, H. B. D.; Cruz, I. R. P.; Quizon, L. G. C.; Marcos, C. J. L. Predictive Assessment and Compressive Strength Test of Geopolymer Concrete Mix as a Partial Replacement to Conventional Concrete Mix with Artificial Neural Network Application. Preprints2024, 2024072412. https://doi.org/10.20944/preprints202407.2412.v1
Labiano, H. B. D.; Cruz, I. R. P.; Quizon, L. G. C.; Marcos, C. J. L. Predictive Assessment and Compressive Strength Test of Geopolymer Concrete Mix as a Partial Replacement to Conventional Concrete Mix with Artificial Neural Network Application. Preprints 2024, 2024072412. https://doi.org/10.20944/preprints202407.2412.v1
Labiano, H. B. D.; Cruz, I. R. P.; Quizon, L. G. C.; Marcos, C. J. L. Predictive Assessment and Compressive Strength Test of Geopolymer Concrete Mix as a Partial Replacement to Conventional Concrete Mix with Artificial Neural Network Application. Preprints2024, 2024072412. https://doi.org/10.20944/preprints202407.2412.v1
APA Style
Labiano, H. B. D., Cruz, I. R. P., Quizon, L. G. C., & Marcos, C. J. L. (2024). Predictive Assessment and Compressive Strength Test of Geopolymer Concrete Mix as a Partial Replacement to Conventional Concrete Mix with Artificial Neural Network Application. Preprints. https://doi.org/10.20944/preprints202407.2412.v1
Chicago/Turabian Style
Labiano, H. B. D., Lanz Ghranyel C. Quizon and Christ John L. Marcos. 2024 "Predictive Assessment and Compressive Strength Test of Geopolymer Concrete Mix as a Partial Replacement to Conventional Concrete Mix with Artificial Neural Network Application" Preprints. https://doi.org/10.20944/preprints202407.2412.v1
Abstract
Geopolymers, made from aluminosilicate raw materials, offer significant environmental advantages over conventional concrete (CC) mixes. This study explores the compressive strength, setting time, and durability of geopolymer concrete (GPC) cylinders, some of which include an additional ordinary portland cement (OPC) mix. Consistent material composition and testing methods are emphasized to achieve reliable results. MATLAB was utilized to develop and assess a neural network model predicting the compressive strength of GPC based on key variables. The model was validated using 16 experimental samples and 1047 samples from various studies. The performance plot displayed the Mean Squared Error (MSE) over iterations, identifying the best training performance at epoch 658 with an MSE of 5.3408. The Error Histogram highlighted distribution errors between predicted and actual compressive strength values, with the highest range from -1.015 to 0.3587, meaning that the predicted values were mostly within a small margin of error from the actual values. This narrow error range indicates that the ANN model has high accuracy in its predictions, as most errors are small and clustered around zero, demonstrating reliable performance in estimating compressive strength. Regression analysis showed varying correlation coefficients (training set R=0.98373, test set R=0.88164, and whole set R=0.96982), indicating adequate predictive accuracy. The results demonstrate that properly trained neural networks can effectively predict the compressive strength of GPC, highlighting its potential as a sustainable construction material.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
