Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations
Ultimate bearing capacity (UBC) is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation. The most reliable means of determining UBC is through experiment, but it is costly and time-consuming which has led to the development of various mod...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
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/S1674775522000981 |
| _version_ | 1811154431612289024 |
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| author | Abiodun Ismail Lawal Sangki Kwon |
| author_facet | Abiodun Ismail Lawal Sangki Kwon |
| author_sort | Abiodun Ismail Lawal |
| collection | DOAJ |
| description | Ultimate bearing capacity (UBC) is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation. The most reliable means of determining UBC is through experiment, but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions. The outcomes of the models are usually validated with the experimental results, but a large gap usually exists between them. Therefore, a model that can give a close prediction of the experimental results is imperative. This study proposes a grasshopper optimization algorithm (GOA) and salp swarm algorithm (SSA) to optimize artificial neural networks (ANNs) using the existing UBC experimental database. The performances of the proposed models are evaluated using various statistical indices. The obtained results are compared with the existing models. The proposed models outperformed the existing models. The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements. |
| first_indexed | 2024-04-10T04:15:30Z |
| format | Article |
| id | doaj.art-e4e10054cfe1441590140373005e9250 |
| institution | Directory Open Access Journal |
| issn | 1674-7755 |
| language | English |
| last_indexed | 2024-04-10T04:15:30Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj.art-e4e10054cfe1441590140373005e92502023-03-12T04:20:40ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552023-03-01153747759Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundationsAbiodun Ismail Lawal0Sangki Kwon1Department of Energy Resources Engineering, Inha University, Incheon, South Korea; Department of Mining Engineering, Federal University of Technology, Akure, NigeriaDepartment of Energy Resources Engineering, Inha University, Incheon, South Korea; Corresponding author.Ultimate bearing capacity (UBC) is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation. The most reliable means of determining UBC is through experiment, but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions. The outcomes of the models are usually validated with the experimental results, but a large gap usually exists between them. Therefore, a model that can give a close prediction of the experimental results is imperative. This study proposes a grasshopper optimization algorithm (GOA) and salp swarm algorithm (SSA) to optimize artificial neural networks (ANNs) using the existing UBC experimental database. The performances of the proposed models are evaluated using various statistical indices. The obtained results are compared with the existing models. The proposed models outperformed the existing models. The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.http://www.sciencedirect.com/science/article/pii/S1674775522000981Ultimate bearing capacity (UBC)GeotechnicsGrasshopper optimization algorithm (GOA)Salp swarm algorithm (SSA)Soft computing (SC) method |
| spellingShingle | Abiodun Ismail Lawal Sangki Kwon Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations Journal of Rock Mechanics and Geotechnical Engineering Ultimate bearing capacity (UBC) Geotechnics Grasshopper optimization algorithm (GOA) Salp swarm algorithm (SSA) Soft computing (SC) method |
| title | Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations |
| title_full | Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations |
| title_fullStr | Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations |
| title_full_unstemmed | Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations |
| title_short | Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations |
| title_sort | development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations |
| topic | Ultimate bearing capacity (UBC) Geotechnics Grasshopper optimization algorithm (GOA) Salp swarm algorithm (SSA) Soft computing (SC) method |
| url | http://www.sciencedirect.com/science/article/pii/S1674775522000981 |
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