Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling
Settlement prediction of geosynthetic-reinforced soil (GRS) abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers. Hence, in this paper, a novel hybrid artificial intelligence (AI)-based model was developed by the combination of art...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775522001093 |
| _version_ | 1811154394004062208 |
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| author | Muhammad Nouman Amjad Raja Syed Taseer Abbas Jaffar Abidhan Bardhan Sanjay Kumar Shukla |
| author_facet | Muhammad Nouman Amjad Raja Syed Taseer Abbas Jaffar Abidhan Bardhan Sanjay Kumar Shukla |
| author_sort | Muhammad Nouman Amjad Raja |
| collection | DOAJ |
| description | Settlement prediction of geosynthetic-reinforced soil (GRS) abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers. Hence, in this paper, a novel hybrid artificial intelligence (AI)-based model was developed by the combination of artificial neural network (ANN) and Harris hawks’ optimisation (HHO), that is, ANN-HHO, to predict the settlement of the GRS abutments. Five other robust intelligent models such as support vector regression (SVR), Gaussian process regression (GPR), relevance vector machine (RVM), sequential minimal optimisation regression (SMOR), and least-median square regression (LMSR) were constructed and compared to the ANN-HHO model. The predictive strength, relalibility and robustness of the model were evaluated based on rigorous statistical testing, ranking criteria, multi-criteria approach, uncertainity analysis and sensitivity analysis (SA). Moreover, the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature. The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models. Therefore, it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments. Finally, the model has been converted into a simple mathematical formulation for easy hand calculations, and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations. |
| first_indexed | 2024-04-10T04:14:50Z |
| format | Article |
| id | doaj.art-536ca52f8fd64d83a206928c5ebc3f61 |
| institution | Directory Open Access Journal |
| issn | 1674-7755 |
| language | English |
| last_indexed | 2024-04-10T04:14:50Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj.art-536ca52f8fd64d83a206928c5ebc3f612023-03-12T04:20:41ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552023-03-01153773788Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modelingMuhammad Nouman Amjad Raja0Syed Taseer Abbas Jaffar1Abidhan Bardhan2Sanjay Kumar Shukla3Department of Civil Engineering, School of Engineering, University of Management and Technology, Lahore, Pakistan; Corresponding author.Department of Civil Engineering, School of Engineering, University of Management and Technology, Lahore, PakistanDepartment of Civil Engineering, National Institute of Technology (NIT) Patna, Patna, Bihar, 800005, IndiaGeotechnical and Geoenvironmental Research Group, School of Engineering, Edith Cowan University, Joondalup, Perth, Australia; Department of Civil Engineering, Delhi Technological University, Delhi, IndiaSettlement prediction of geosynthetic-reinforced soil (GRS) abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers. Hence, in this paper, a novel hybrid artificial intelligence (AI)-based model was developed by the combination of artificial neural network (ANN) and Harris hawks’ optimisation (HHO), that is, ANN-HHO, to predict the settlement of the GRS abutments. Five other robust intelligent models such as support vector regression (SVR), Gaussian process regression (GPR), relevance vector machine (RVM), sequential minimal optimisation regression (SMOR), and least-median square regression (LMSR) were constructed and compared to the ANN-HHO model. The predictive strength, relalibility and robustness of the model were evaluated based on rigorous statistical testing, ranking criteria, multi-criteria approach, uncertainity analysis and sensitivity analysis (SA). Moreover, the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature. The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models. Therefore, it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments. Finally, the model has been converted into a simple mathematical formulation for easy hand calculations, and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.http://www.sciencedirect.com/science/article/pii/S1674775522001093Geosynthetic-reinforced soil (GRS) abutmentsSettlement estimationPredictive modelingArtificial intelligence (AI)Artificial neural network (ANN)-Harris hawks' optimisation (HHO) |
| spellingShingle | Muhammad Nouman Amjad Raja Syed Taseer Abbas Jaffar Abidhan Bardhan Sanjay Kumar Shukla Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling Journal of Rock Mechanics and Geotechnical Engineering Geosynthetic-reinforced soil (GRS) abutments Settlement estimation Predictive modeling Artificial intelligence (AI) Artificial neural network (ANN)-Harris hawks' optimisation (HHO) |
| title | Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling |
| title_full | Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling |
| title_fullStr | Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling |
| title_full_unstemmed | Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling |
| title_short | Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling |
| title_sort | predicting and validating the load settlement behavior of large scale geosynthetic reinforced soil abutments using hybrid intelligent modeling |
| topic | Geosynthetic-reinforced soil (GRS) abutments Settlement estimation Predictive modeling Artificial intelligence (AI) Artificial neural network (ANN)-Harris hawks' optimisation (HHO) |
| url | http://www.sciencedirect.com/science/article/pii/S1674775522001093 |
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