Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach
Open caissons are increasingly utilized for underground construction due to the increasing demand for aboveground structures, which employ the principle of submersion using the self-weight of the edge cutting face and the applied bearing pressure to mitigate the vertical soil reaction. This paper ex...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025004049 |
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| author | Wittaya Jitchaijaroen Rungroad Suppakul Mohammad Khajehzadeh Suraparb Keawsawasvong Pitthaya Jamsawang Peem Nuaklong Fahimeh Ghasemian |
| author_facet | Wittaya Jitchaijaroen Rungroad Suppakul Mohammad Khajehzadeh Suraparb Keawsawasvong Pitthaya Jamsawang Peem Nuaklong Fahimeh Ghasemian |
| author_sort | Wittaya Jitchaijaroen |
| collection | DOAJ |
| description | Open caissons are increasingly utilized for underground construction due to the increasing demand for aboveground structures, which employ the principle of submersion using the self-weight of the edge cutting face and the applied bearing pressure to mitigate the vertical soil reaction. This paper examines the bearing capacity factor of the edge cutting face in anisotropic clays, approximated using the finite element limit analysis (FELA) method and considering the average results between the upper and lower bounds. The influence of the adhesion factor at the interface of the cutting edge (α), the ratio between the depth of the internal embedment and the embedded width (H/B), the ratio between the radius and the embedded width (R/B), the anisotropic shear strength (re), and the cutting face angle (β) is investigated. The results indicate a significant influence of the anisotropic shear strength on the adhesion factor at the interface of the cutting edge. An increase in re denotes a decrease in the undrained shear strength obtained from the triaxial compression test, resulting in an increase in the value of N. An increase in α influences β, such that when β <90°, the value of N remains constant when β = 90°. In addition, a highly efficient hybrid model called DNN-PBT was established utilizing a deep neural network (DNN) and a population based training (PBT) approach, specifically for the purpose of accurately predicting the bearing capacity factor of circular open caissons positioned in undrained clay. Both computational and comparative outcomes demonstrate that the proposed DNN-PBT can precisely forecast the bearing capacity, achieving an R2 value higher than 0.999 and a mean squared error (MSE) <0.007. These findings highlight the accuracy and efficiency of the suggested approach. Furthermore, the sensitivity analysis results demonstrated that the anisotropic shear strength (re) is the most important input variable for estimating the bearing capacity factor of the edge cutting face. |
| format | Article |
| id | doaj-art-4f21e6dc70d24d9b87c49ce9ed9ccfac |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-4f21e6dc70d24d9b87c49ce9ed9ccfac2025-08-20T02:13:28ZengElsevierResults in Engineering2590-12302025-03-012510432310.1016/j.rineng.2025.104323Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approachWittaya Jitchaijaroen0Rungroad Suppakul1Mohammad Khajehzadeh2Suraparb Keawsawasvong3Pitthaya Jamsawang4Peem Nuaklong5Fahimeh Ghasemian6Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, ThailandResearch Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, ThailandResearch Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand; Department of Civil Engineering, Anar Branch, Islamic Azad University, Anar, IranResearch Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand; Corresponding author.Soil Engineering Research Center, Department of Civil Engineering, King Mongkut's University of Technology North Bangkok, Bangkok 10800, ThailandResearch Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, ThailandComputer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, IranOpen caissons are increasingly utilized for underground construction due to the increasing demand for aboveground structures, which employ the principle of submersion using the self-weight of the edge cutting face and the applied bearing pressure to mitigate the vertical soil reaction. This paper examines the bearing capacity factor of the edge cutting face in anisotropic clays, approximated using the finite element limit analysis (FELA) method and considering the average results between the upper and lower bounds. The influence of the adhesion factor at the interface of the cutting edge (α), the ratio between the depth of the internal embedment and the embedded width (H/B), the ratio between the radius and the embedded width (R/B), the anisotropic shear strength (re), and the cutting face angle (β) is investigated. The results indicate a significant influence of the anisotropic shear strength on the adhesion factor at the interface of the cutting edge. An increase in re denotes a decrease in the undrained shear strength obtained from the triaxial compression test, resulting in an increase in the value of N. An increase in α influences β, such that when β <90°, the value of N remains constant when β = 90°. In addition, a highly efficient hybrid model called DNN-PBT was established utilizing a deep neural network (DNN) and a population based training (PBT) approach, specifically for the purpose of accurately predicting the bearing capacity factor of circular open caissons positioned in undrained clay. Both computational and comparative outcomes demonstrate that the proposed DNN-PBT can precisely forecast the bearing capacity, achieving an R2 value higher than 0.999 and a mean squared error (MSE) <0.007. These findings highlight the accuracy and efficiency of the suggested approach. Furthermore, the sensitivity analysis results demonstrated that the anisotropic shear strength (re) is the most important input variable for estimating the bearing capacity factor of the edge cutting face.http://www.sciencedirect.com/science/article/pii/S2590123025004049Open caissonCutting edgeCutting angleDeep neural networkPopulation Based Training |
| spellingShingle | Wittaya Jitchaijaroen Rungroad Suppakul Mohammad Khajehzadeh Suraparb Keawsawasvong Pitthaya Jamsawang Peem Nuaklong Fahimeh Ghasemian Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach Results in Engineering Open caisson Cutting edge Cutting angle Deep neural network Population Based Training |
| title | Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach |
| title_full | Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach |
| title_fullStr | Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach |
| title_full_unstemmed | Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach |
| title_short | Bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach |
| title_sort | bearing capacity prediction of open caissons in anisotropic clays utilizing a deep neural network coupled with a population based training approach |
| topic | Open caisson Cutting edge Cutting angle Deep neural network Population Based Training |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025004049 |
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