Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete
Natural fiber-reinforced concrete (NFRC) is gaining attention for its sustainability, cost-effectiveness, and biodegradability, making it a promising material for construction and repair. Betel nut husk fiber (BNHF) is being incorporated into concrete due to its eco-friendly, nontoxic, and biodegrad...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institution of Structural Engineers
2025
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45610/ |
| _version_ | 1848827465030434816 |
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| author | Hasan, Mehedi Hasan, Md Soumike Hasan, Kamrul Tushar, Fazlul Hoque Khan, Majid Putra Jaya, Ramadhansyah |
| author_facet | Hasan, Mehedi Hasan, Md Soumike Hasan, Kamrul Tushar, Fazlul Hoque Khan, Majid Putra Jaya, Ramadhansyah |
| author_sort | Hasan, Mehedi |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Natural fiber-reinforced concrete (NFRC) is gaining attention for its sustainability, cost-effectiveness, and biodegradability, making it a promising material for construction and repair. Betel nut husk fiber (BNHF) is being incorporated into concrete due to its eco-friendly, nontoxic, and biodegradable properties. However, traditional methods for measuring slump, compressive strength (CS), and split tensile strength (STS) are often time-consuming, labor-intensive, and costly. To address this, machine learning (ML) models offer an efficient alternative for predicting these properties, enabling faster and more economical adjustments to BNHF-reinforced concrete mixes. This study explores the use of five ensemble ML models: Random Forest (RF), XGBoost, CatBoost, AdaBoost, and LightGBM to predict slump, CS, and STS. Model performance was evaluated using five metrics: coefficient of determination (R²), root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the CatBoost model performed best in predicting slump and CS, with R² values of 0.989 and 0.918, and RMSEs of 2.116 mm and 1.986 MPa, respectively. For STS, XGBoost outperformed other models, achieving an R² of 0.862 and an RMSE of 0.406 MPa on the test set. SHapley Additive exPlanations (SHAP) analysis indicated that BNHF percentage and fiber content had the greatest influence on slump, while curing time was the most significant factor affecting both CS and STS. The findings demonstrate that CatBoost and XGBoost can accurately predict the mechanical properties, offering a practical alternative to extensive laboratory testing and enabling time and cost savings in construction. |
| first_indexed | 2025-11-15T04:01:08Z |
| format | Article |
| id | ump-45610 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:01:08Z |
| publishDate | 2025 |
| publisher | Institution of Structural Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-456102025-09-17T07:31:00Z https://umpir.ump.edu.my/id/eprint/45610/ Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete Hasan, Mehedi Hasan, Md Soumike Hasan, Kamrul Tushar, Fazlul Hoque Khan, Majid Putra Jaya, Ramadhansyah TA Engineering (General). Civil engineering (General) TH Building construction Natural fiber-reinforced concrete (NFRC) is gaining attention for its sustainability, cost-effectiveness, and biodegradability, making it a promising material for construction and repair. Betel nut husk fiber (BNHF) is being incorporated into concrete due to its eco-friendly, nontoxic, and biodegradable properties. However, traditional methods for measuring slump, compressive strength (CS), and split tensile strength (STS) are often time-consuming, labor-intensive, and costly. To address this, machine learning (ML) models offer an efficient alternative for predicting these properties, enabling faster and more economical adjustments to BNHF-reinforced concrete mixes. This study explores the use of five ensemble ML models: Random Forest (RF), XGBoost, CatBoost, AdaBoost, and LightGBM to predict slump, CS, and STS. Model performance was evaluated using five metrics: coefficient of determination (R²), root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the CatBoost model performed best in predicting slump and CS, with R² values of 0.989 and 0.918, and RMSEs of 2.116 mm and 1.986 MPa, respectively. For STS, XGBoost outperformed other models, achieving an R² of 0.862 and an RMSE of 0.406 MPa on the test set. SHapley Additive exPlanations (SHAP) analysis indicated that BNHF percentage and fiber content had the greatest influence on slump, while curing time was the most significant factor affecting both CS and STS. The findings demonstrate that CatBoost and XGBoost can accurately predict the mechanical properties, offering a practical alternative to extensive laboratory testing and enabling time and cost savings in construction. Institution of Structural Engineers 2025 Article PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/45610/1/Interpretable%20machine%20learning%20approach.pdf Hasan, Mehedi and Hasan, Md Soumike and Hasan, Kamrul and Tushar, Fazlul Hoque and Khan, Majid and Putra Jaya, Ramadhansyah (2025) Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete. Structures, 81 (110152). pp. 1-27. ISSN 2352-0124. (Published) https://doi.org/10.1016/j.istruc.2025.110152 https://doi.org/10.1016/j.istruc.2025.110152 https://doi.org/10.1016/j.istruc.2025.110152 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TH Building construction Hasan, Mehedi Hasan, Md Soumike Hasan, Kamrul Tushar, Fazlul Hoque Khan, Majid Putra Jaya, Ramadhansyah Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete |
| title | Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete |
| title_full | Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete |
| title_fullStr | Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete |
| title_full_unstemmed | Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete |
| title_short | Interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete |
| title_sort | interpretable machine learning approach for predicting the workability and mechanical properties of betel nut husk fiber-reinforced concrete |
| topic | TA Engineering (General). Civil engineering (General) TH Building construction |
| url | https://umpir.ump.edu.my/id/eprint/45610/ https://umpir.ump.edu.my/id/eprint/45610/ https://umpir.ump.edu.my/id/eprint/45610/ |