Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores
Accurate estimation of the uniaxial compressive strength (UCS) of carbonate rocks underpins safe design and stability assessment in karst-influenced geotechnical projects. This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures—radial basis fu...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/10/5609 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850257951536709632 |
|---|---|
| author | Jose W. Naal-Pech Leonardo Palemón-Arcos Youness El Hamzaoui |
| author_facet | Jose W. Naal-Pech Leonardo Palemón-Arcos Youness El Hamzaoui |
| author_sort | Jose W. Naal-Pech |
| collection | DOAJ |
| description | Accurate estimation of the uniaxial compressive strength (UCS) of carbonate rocks underpins safe design and stability assessment in karst-influenced geotechnical projects. This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures—radial basis function (RBF), Bayesian regularized (BR), scaled conjugate gradient (SCG), and Levenberg–Marquardt (LM)—to predict UCS from three readily measured variables: water content, interconnected porosity, and real density. Fifty core specimens from the Seybaplaya quarry in Campeche, Mexico, were split into training and testing subsets under uniform preprocessing. Each model’s predictive performance was assessed over 30 independent runs using mean absolute error, root mean squared error, and coefficient of determination, with statistical differences tested via nonparametric hypothesis testing. The RBF network achieved the highest median R<sup>2</sup> and significantly outperformed the other variants, while the BR model demonstrated robust generalization. SCG and LM converged faster and efficiently but with slightly lower accuracy. Sensitivity analysis identified interconnected porosity as the primary predictor of UCS. These results establish RBF-based ANNs with appropriate regularization and feature importance assessment as a novel, practical, and reliable framework for UCS prediction in heterogeneous carbonate formations. |
| format | Article |
| id | doaj-art-1876e169bc02424a895ea603672f5a8a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1876e169bc02424a895ea603672f5a8a2025-08-20T01:56:17ZengMDPI AGApplied Sciences2076-34172025-05-011510560910.3390/app15105609Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock CoresJose W. Naal-Pech0Leonardo Palemón-Arcos1Youness El Hamzaoui2Engineering College, Carmen Autonomous University, Campus III, Avenida Central S/N, Esq. con Fracc. Mundo Maya, Carmen City C.P. 24115, Campeche, MexicoEngineering College, Carmen Autonomous University, Campus III, Avenida Central S/N, Esq. con Fracc. Mundo Maya, Carmen City C.P. 24115, Campeche, MexicoEngineering College, Carmen Autonomous University, Campus III, Avenida Central S/N, Esq. con Fracc. Mundo Maya, Carmen City C.P. 24115, Campeche, MexicoAccurate estimation of the uniaxial compressive strength (UCS) of carbonate rocks underpins safe design and stability assessment in karst-influenced geotechnical projects. This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures—radial basis function (RBF), Bayesian regularized (BR), scaled conjugate gradient (SCG), and Levenberg–Marquardt (LM)—to predict UCS from three readily measured variables: water content, interconnected porosity, and real density. Fifty core specimens from the Seybaplaya quarry in Campeche, Mexico, were split into training and testing subsets under uniform preprocessing. Each model’s predictive performance was assessed over 30 independent runs using mean absolute error, root mean squared error, and coefficient of determination, with statistical differences tested via nonparametric hypothesis testing. The RBF network achieved the highest median R<sup>2</sup> and significantly outperformed the other variants, while the BR model demonstrated robust generalization. SCG and LM converged faster and efficiently but with slightly lower accuracy. Sensitivity analysis identified interconnected porosity as the primary predictor of UCS. These results establish RBF-based ANNs with appropriate regularization and feature importance assessment as a novel, practical, and reliable framework for UCS prediction in heterogeneous carbonate formations.https://www.mdpi.com/2076-3417/15/10/5609uniaxial compressive strengthcarbonate rock coresartificial neural networksradial basis functionBayesian regularizationsensitivity analysis |
| spellingShingle | Jose W. Naal-Pech Leonardo Palemón-Arcos Youness El Hamzaoui Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores Applied Sciences uniaxial compressive strength carbonate rock cores artificial neural networks radial basis function Bayesian regularization sensitivity analysis |
| title | Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores |
| title_full | Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores |
| title_fullStr | Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores |
| title_full_unstemmed | Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores |
| title_short | Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores |
| title_sort | comparative evaluation of feed forward neural networks for predicting uniaxial compressive strength of seybaplaya carbonate rock cores |
| topic | uniaxial compressive strength carbonate rock cores artificial neural networks radial basis function Bayesian regularization sensitivity analysis |
| url | https://www.mdpi.com/2076-3417/15/10/5609 |
| work_keys_str_mv | AT josewnaalpech comparativeevaluationoffeedforwardneuralnetworksforpredictinguniaxialcompressivestrengthofseybaplayacarbonaterockcores AT leonardopalemonarcos comparativeevaluationoffeedforwardneuralnetworksforpredictinguniaxialcompressivestrengthofseybaplayacarbonaterockcores AT younesselhamzaoui comparativeevaluationoffeedforwardneuralnetworksforpredictinguniaxialcompressivestrengthofseybaplayacarbonaterockcores |