Integration of FEM-based permeation analysis and AI-based predictive models for improved chemical grout permeation assessment in heterogeneous soils
This study proposes an integrated framework combining Finite Element Method (FEM)-based permeation analysis with Artificial Intelligence (AI)-based predictive models for assessing chemical grout permeation behavior in heterogeneous sandy soils containing low-permeability zones. FEM analysis was cond...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025011466 |
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| author | Khin Nyein Chan Kyaw Kuo Chieh Chao Shinya Inazumi |
| author_facet | Khin Nyein Chan Kyaw Kuo Chieh Chao Shinya Inazumi |
| author_sort | Khin Nyein Chan Kyaw |
| collection | DOAJ |
| description | This study proposes an integrated framework combining Finite Element Method (FEM)-based permeation analysis with Artificial Intelligence (AI)-based predictive models for assessing chemical grout permeation behavior in heterogeneous sandy soils containing low-permeability zones. FEM analysis was conducted to investigate grout flow patterns and permeation risks in such soils, revealing that proximity to low-permeability zones significantly influences flow velocity and grout distribution. Simplified regression equations were developed to efficiently predict permeation risks and reduce computational complexity. Additionally, AI models, including neural networks and gradient boosting decision trees, were trained on FEM-derived datasets to predict permeation velocities and ranges with high accuracy (R² = 0.849). Results showed that even with 5.5 % low-permeability content, average fill rates of 94.5 % (FEM) and 96 % (AI) were achieved, with worst-case scenarios dropping to approximately 81 % and 83 %, respectively. The findings highlight the effectiveness of integrating FEM, regression models, and AI-based modeling to optimize chemical grouting strategies and mitigate permeation risks due to soil heterogeneity. The proposed framework offers an effective approach to enhancing chemical grouting practices in heterogeneous ground conditions. |
| format | Article |
| id | doaj-art-b2c6d8c0c9ec4fcb80706bcd273eb315 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-b2c6d8c0c9ec4fcb80706bcd273eb3152025-08-20T03:53:38ZengElsevierResults in Engineering2590-12302025-06-012610507110.1016/j.rineng.2025.105071Integration of FEM-based permeation analysis and AI-based predictive models for improved chemical grout permeation assessment in heterogeneous soilsKhin Nyein Chan Kyaw0Kuo Chieh Chao1Shinya Inazumi2Graduate School of Engineering and Science, Shibaura Institute of Technology, Toyosu Campus 3-7-5 Toyosu, Koto-ku, Tokyo, 135-8548, JapanSchool of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani, 12120, ThailandCollege of Engineering, Shibaura Institute of Technology, Toyosu Campus 3-7-5 Toyosu, Koto-ku, Tokyo, 135-8548, Japan; Corresponding author.This study proposes an integrated framework combining Finite Element Method (FEM)-based permeation analysis with Artificial Intelligence (AI)-based predictive models for assessing chemical grout permeation behavior in heterogeneous sandy soils containing low-permeability zones. FEM analysis was conducted to investigate grout flow patterns and permeation risks in such soils, revealing that proximity to low-permeability zones significantly influences flow velocity and grout distribution. Simplified regression equations were developed to efficiently predict permeation risks and reduce computational complexity. Additionally, AI models, including neural networks and gradient boosting decision trees, were trained on FEM-derived datasets to predict permeation velocities and ranges with high accuracy (R² = 0.849). Results showed that even with 5.5 % low-permeability content, average fill rates of 94.5 % (FEM) and 96 % (AI) were achieved, with worst-case scenarios dropping to approximately 81 % and 83 %, respectively. The findings highlight the effectiveness of integrating FEM, regression models, and AI-based modeling to optimize chemical grouting strategies and mitigate permeation risks due to soil heterogeneity. The proposed framework offers an effective approach to enhancing chemical grouting practices in heterogeneous ground conditions.http://www.sciencedirect.com/science/article/pii/S2590123025011466AI-based predictive modelChemical groutingFEM-based permeation analysisHeterogeneous soilLiquefaction mitigation |
| spellingShingle | Khin Nyein Chan Kyaw Kuo Chieh Chao Shinya Inazumi Integration of FEM-based permeation analysis and AI-based predictive models for improved chemical grout permeation assessment in heterogeneous soils Results in Engineering AI-based predictive model Chemical grouting FEM-based permeation analysis Heterogeneous soil Liquefaction mitigation |
| title | Integration of FEM-based permeation analysis and AI-based predictive models for improved chemical grout permeation assessment in heterogeneous soils |
| title_full | Integration of FEM-based permeation analysis and AI-based predictive models for improved chemical grout permeation assessment in heterogeneous soils |
| title_fullStr | Integration of FEM-based permeation analysis and AI-based predictive models for improved chemical grout permeation assessment in heterogeneous soils |
| title_full_unstemmed | Integration of FEM-based permeation analysis and AI-based predictive models for improved chemical grout permeation assessment in heterogeneous soils |
| title_short | Integration of FEM-based permeation analysis and AI-based predictive models for improved chemical grout permeation assessment in heterogeneous soils |
| title_sort | integration of fem based permeation analysis and ai based predictive models for improved chemical grout permeation assessment in heterogeneous soils |
| topic | AI-based predictive model Chemical grouting FEM-based permeation analysis Heterogeneous soil Liquefaction mitigation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025011466 |
| work_keys_str_mv | AT khinnyeinchankyaw integrationoffembasedpermeationanalysisandaibasedpredictivemodelsforimprovedchemicalgroutpermeationassessmentinheterogeneoussoils AT kuochiehchao integrationoffembasedpermeationanalysisandaibasedpredictivemodelsforimprovedchemicalgroutpermeationassessmentinheterogeneoussoils AT shinyainazumi integrationoffembasedpermeationanalysisandaibasedpredictivemodelsforimprovedchemicalgroutpermeationassessmentinheterogeneoussoils |