Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression
The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was traine...
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Springer Nature
2023
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| Online Access: | http://irep.iium.edu.my/106977/7/106977_Predicting%20California%20bearing%20ratio%20of%20HARHA%E2%80%91treated%20expansive.pdf http://irep.iium.edu.my/106977/8/106977_Predicting%20California%20bearing%20ratio%20of%20HARHA%E2%80%91treated%20expansive_Scopus.pdf http://irep.iium.edu.my/106977/ https://www.nature.com/articles/s41598-023-40903-1 https://doi.org/10.1038/s41598-023-40903-1 |
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| author | Ahmad, Mahmood A. Al‑Zubi, Mohammad Kubińska‑Jabcoń, Ewa Majdi, Ali Al-Mansob, Ramez Al-Ezzi Abduljalil Sabri, Mohanad Muayad Sabri Ali, Enas Abdulrabb Naji, Jamil Elnaggar, AshrafY Zamin, Bakht |
| author_facet | Ahmad, Mahmood A. Al‑Zubi, Mohammad Kubińska‑Jabcoń, Ewa Majdi, Ali Al-Mansob, Ramez Al-Ezzi Abduljalil Sabri, Mohanad Muayad Sabri Ali, Enas Abdulrabb Naji, Jamil Elnaggar, AshrafY Zamin, Bakht |
| author_sort | Ahmad, Mahmood |
| building | IIUM Library |
| collection | Institutional Repository |
| content_provider | International Islamic University Malaysia |
| content_source | IIUM Repository (IREP) |
| continent | Asia |
| country | Malaysia |
| description | The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA—a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR. |
| format | Article |
| id | my.iium.irep-106977 |
| institution | Universiti Islam Antarabangsa Malaysia |
| language | en en |
| publishDate | 2023 |
| publisher | Springer Nature |
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| spelling | my.iium.irep-1069772023-11-21T06:01:59Z http://irep.iium.edu.my/106977/ Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression Ahmad, Mahmood A. Al‑Zubi, Mohammad Kubińska‑Jabcoń, Ewa Majdi, Ali Al-Mansob, Ramez Al-Ezzi Abduljalil Sabri, Mohanad Muayad Sabri Ali, Enas Abdulrabb Naji, Jamil Elnaggar, AshrafY Zamin, Bakht TA401 Materials of engineering and construction TA705 Engineering geology. Rock mechanics. Soil mechanics The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA—a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR. Springer Nature 2023-09-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/106977/7/106977_Predicting%20California%20bearing%20ratio%20of%20HARHA%E2%80%91treated%20expansive.pdf application/pdf en http://irep.iium.edu.my/106977/8/106977_Predicting%20California%20bearing%20ratio%20of%20HARHA%E2%80%91treated%20expansive_Scopus.pdf Ahmad, Mahmood and A. Al‑Zubi, Mohammad and Kubińska‑Jabcoń, Ewa and Majdi, Ali and Al-Mansob, Ramez Al-Ezzi Abduljalil and Sabri, Mohanad Muayad Sabri and Ali, Enas and Abdulrabb Naji, Jamil and Elnaggar, AshrafY and Zamin, Bakht (2023) Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression. Scientific Reports, 13 (1). pp. 1-11. ISSN 2045-2322 https://www.nature.com/articles/s41598-023-40903-1 https://doi.org/10.1038/s41598-023-40903-1 |
| spellingShingle | TA401 Materials of engineering and construction TA705 Engineering geology. Rock mechanics. Soil mechanics Ahmad, Mahmood A. Al‑Zubi, Mohammad Kubińska‑Jabcoń, Ewa Majdi, Ali Al-Mansob, Ramez Al-Ezzi Abduljalil Sabri, Mohanad Muayad Sabri Ali, Enas Abdulrabb Naji, Jamil Elnaggar, AshrafY Zamin, Bakht Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression |
| title | Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression |
| title_full | Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression |
| title_fullStr | Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression |
| title_full_unstemmed | Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression |
| title_short | Predicting California bearing ratio of HARHA‑treated expansive soils using Gaussian process regression |
| title_sort | predicting california bearing ratio of harha‑treated expansive soils using gaussian process regression |
| topic | TA401 Materials of engineering and construction TA705 Engineering geology. Rock mechanics. Soil mechanics |
| url | http://irep.iium.edu.my/106977/7/106977_Predicting%20California%20bearing%20ratio%20of%20HARHA%E2%80%91treated%20expansive.pdf http://irep.iium.edu.my/106977/8/106977_Predicting%20California%20bearing%20ratio%20of%20HARHA%E2%80%91treated%20expansive_Scopus.pdf http://irep.iium.edu.my/106977/ https://www.nature.com/articles/s41598-023-40903-1 https://doi.org/10.1038/s41598-023-40903-1 |
| url_provider | http://irep.iium.edu.my/ |