Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm
Abstract In this work, the results of instrumentation over 8 years, including the phases of construction, first impounding, and operation, have been used to analyze the location of the Eyvashan Dam settlement. Mohr–Coulomb behavioral model and numerical model of Plaxis 2D software were used to verif...
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Nature Portfolio
2024-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-60944-4 |
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| author | Behrang Beiranvand Taher Rajaee Mehdi Komasi |
| author_facet | Behrang Beiranvand Taher Rajaee Mehdi Komasi |
| author_sort | Behrang Beiranvand |
| collection | DOAJ |
| description | Abstract In this work, the results of instrumentation over 8 years, including the phases of construction, first impounding, and operation, have been used to analyze the location of the Eyvashan Dam settlement. Mohr–Coulomb behavioral model and numerical model of Plaxis 2D software were used to verify the monitoring results. The results demonstrated that settlement of the dam has increased in the dam's core since the beginning of construction, and they eventually stabilized during the operation phase. After the completion of the construction phase, the maximum settlement of the dam core was recorded as 809 mm, which is equivalent to 1.2% of the height of the dam at the middle level. Also, an approach to interpreting the settlement behavior of earth dams has been presented that is based on spatiotemporal clustering. Also, RF, MARS, and GMDH models were created based on a proposed scenario to predict settlement using points located in a cluster. Therefore, the settlement location of the studied dam was determined using the results of the k-means clustering algorithm in the aforementioned AI models. The high accuracy of the results of the proposed method confirms the proper performance of using AI models in predicting and diagnosing the settlement of earthen dams using the results of k-means spatiotemporal clustering algorithm. The evaluation of the models shows that the ENN model is a more suitable and efficient tool in this field and can be useful in monitoring the settlement of earth dams. |
| format | Article |
| id | doaj-art-f5f19fa4359946ea94ebe7be78e4a082 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-f5f19fa4359946ea94ebe7be78e4a0822025-08-20T02:39:40ZengNature PortfolioScientific Reports2045-23222024-05-0114111510.1038/s41598-024-60944-4Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithmBehrang Beiranvand0Taher Rajaee1Mehdi Komasi2Civil Engineering, Water and Hydraulic Structures, University of QomDepartment of Civil Engineering, University of QomDepartment of Civil Engineering, University of Ayatollah Ozma BoroujerdiAbstract In this work, the results of instrumentation over 8 years, including the phases of construction, first impounding, and operation, have been used to analyze the location of the Eyvashan Dam settlement. Mohr–Coulomb behavioral model and numerical model of Plaxis 2D software were used to verify the monitoring results. The results demonstrated that settlement of the dam has increased in the dam's core since the beginning of construction, and they eventually stabilized during the operation phase. After the completion of the construction phase, the maximum settlement of the dam core was recorded as 809 mm, which is equivalent to 1.2% of the height of the dam at the middle level. Also, an approach to interpreting the settlement behavior of earth dams has been presented that is based on spatiotemporal clustering. Also, RF, MARS, and GMDH models were created based on a proposed scenario to predict settlement using points located in a cluster. Therefore, the settlement location of the studied dam was determined using the results of the k-means clustering algorithm in the aforementioned AI models. The high accuracy of the results of the proposed method confirms the proper performance of using AI models in predicting and diagnosing the settlement of earthen dams using the results of k-means spatiotemporal clustering algorithm. The evaluation of the models shows that the ENN model is a more suitable and efficient tool in this field and can be useful in monitoring the settlement of earth dams.https://doi.org/10.1038/s41598-024-60944-4Spatiotemporal clusteringInstrumentationPlaxis 2DSettlementk-means algorithmAI models |
| spellingShingle | Behrang Beiranvand Taher Rajaee Mehdi Komasi Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm Scientific Reports Spatiotemporal clustering Instrumentation Plaxis 2D Settlement k-means algorithm AI models |
| title | Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm |
| title_full | Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm |
| title_fullStr | Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm |
| title_full_unstemmed | Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm |
| title_short | Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm |
| title_sort | presenting the ai models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k means algorithm |
| topic | Spatiotemporal clustering Instrumentation Plaxis 2D Settlement k-means algorithm AI models |
| url | https://doi.org/10.1038/s41598-024-60944-4 |
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