Modeling and sensitivity analysis of the compressive strength of recycled brick aggregates concrete using GMDH, GEP and RSM methods
In order to develop the use of alternative materials in concrete and the dependence of its strength on various factors, it is necessary to have relationships to estimate the compressive strength of recycled brick concrete (RBC). This study employed three approaches Group Method of Data Handling (GMD...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025009673 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In order to develop the use of alternative materials in concrete and the dependence of its strength on various factors, it is necessary to have relationships to estimate the compressive strength of recycled brick concrete (RBC). This study employed three approaches Group Method of Data Handling (GMDH), Gene Expression Programming (GEP), and Response Surface Methodology (RSM) to predict the compressive strength of RBC. The input parameters studied include the standard compressive strength of cement paste (fcem), the coarse aggregate water absorption ratio (ωmwa), the effective water-to-cement ratio (weff/c), the sand-to-aggregate ratio (s/a), and the volumetric replacement ratio of recycled brick aggregates (ηRBA). The cylindrical compressive strength (fcy) was considered as the output of the models. A total of 347 collected data were used to predict the compressive strength of RBC. The results showed that the R, MSE, and RMSE values of the RSM model were 0.88, 18.418, and 4.291, respectively. The R, MSE, and RMSE values of the GMDH model were 0.84, 22.07, and 4.69, respectively. Although the RSM method results in a lower error rate compared to other methods due to the significant ''lack of fit'' parameter, which means that potential overfitting and weaker generalization to new data, the GMDH model was identified as was introduced as the most appropriate model for predicting RBC compressive strength. Sensitivity analyses showed that the most important parameters affecting RBC compressive strength were ωmwa, weff/c, s/a, ηRBA, and fcem, respectively. |
|---|---|
| ISSN: | 2590-1230 |