High Embankment Dam Stability Analysis Using Artificial Neural Networks
Regular surveillance, data acquisition, and visual observation of high embankment dams are extremely important for the stability analysis of these structures. The stability issues that could occur during a dam's lifetime are mainly related to slope instability and internal erosion. The aim of c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/408404 |
| _version_ | 1827282012787965952 |
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| author | Milica Markovic Novak Radivojevic Miona Andrejevic Stosovic Jelena Markovic Brankovic Srdjan Zivkovic |
| author_facet | Milica Markovic Novak Radivojevic Miona Andrejevic Stosovic Jelena Markovic Brankovic Srdjan Zivkovic |
| author_sort | Milica Markovic |
| collection | DOAJ |
| description | Regular surveillance, data acquisition, and visual observation of high embankment dams are extremely important for the stability analysis of these structures. The stability issues that could occur during a dam's lifetime are mainly related to slope instability and internal erosion. The aim of continuous dam security monitoring and field measurement is to identify priority flow paths in the dam body, i.e. cracks and the erosion process. A key parameter for embankment dam stability assessment is the pore water pressure (PWP) response in the clay core. Increasing pore water pressure results in shear strength reduction and can cause dam instability. In this paper, four different models based on artificial neural networks will be developed for pore water pressure prediction in an embankment dam clay core, based on meteorological, hydrological, and geotechnical data. These models will be compared and the model that gives the smallest prediction error will be presented. In the light of climate change, the main objective of this paper is to find the model that can be used for embankment dam stability prediction in extreme weather events. |
| first_indexed | 2024-04-24T09:10:54Z |
| format | Article |
| id | doaj.art-d59b147c84984d1cb95569cac1aff57c |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:10:54Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-d59b147c84984d1cb95569cac1aff57c2024-04-15T17:57:26ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-012951733174010.17559/TV-20211011140249High Embankment Dam Stability Analysis Using Artificial Neural NetworksMilica Markovic0Novak Radivojevic1Miona Andrejevic Stosovic2Jelena Markovic Brankovic3Srdjan Zivkovic4Faculty of Civil Engineering and Architecture, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Republic of SerbiaFaculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Republic of SerbiaFaculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Republic of SerbiaFaculty of Civil Engineering and Architecture, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Republic of SerbiaFaculty of Civil Engineering and Architecture, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Republic of SerbiaRegular surveillance, data acquisition, and visual observation of high embankment dams are extremely important for the stability analysis of these structures. The stability issues that could occur during a dam's lifetime are mainly related to slope instability and internal erosion. The aim of continuous dam security monitoring and field measurement is to identify priority flow paths in the dam body, i.e. cracks and the erosion process. A key parameter for embankment dam stability assessment is the pore water pressure (PWP) response in the clay core. Increasing pore water pressure results in shear strength reduction and can cause dam instability. In this paper, four different models based on artificial neural networks will be developed for pore water pressure prediction in an embankment dam clay core, based on meteorological, hydrological, and geotechnical data. These models will be compared and the model that gives the smallest prediction error will be presented. In the light of climate change, the main objective of this paper is to find the model that can be used for embankment dam stability prediction in extreme weather events.https://hrcak.srce.hr/file/408404artificial neural networkembankment dampore water pressurestability analysis |
| spellingShingle | Milica Markovic Novak Radivojevic Miona Andrejevic Stosovic Jelena Markovic Brankovic Srdjan Zivkovic High Embankment Dam Stability Analysis Using Artificial Neural Networks Tehnički Vjesnik artificial neural network embankment dam pore water pressure stability analysis |
| title | High Embankment Dam Stability Analysis Using Artificial Neural Networks |
| title_full | High Embankment Dam Stability Analysis Using Artificial Neural Networks |
| title_fullStr | High Embankment Dam Stability Analysis Using Artificial Neural Networks |
| title_full_unstemmed | High Embankment Dam Stability Analysis Using Artificial Neural Networks |
| title_short | High Embankment Dam Stability Analysis Using Artificial Neural Networks |
| title_sort | high embankment dam stability analysis using artificial neural networks |
| topic | artificial neural network embankment dam pore water pressure stability analysis |
| url | https://hrcak.srce.hr/file/408404 |
| work_keys_str_mv | AT milicamarkovic highembankmentdamstabilityanalysisusingartificialneuralnetworks AT novakradivojevic highembankmentdamstabilityanalysisusingartificialneuralnetworks AT mionaandrejevicstosovic highembankmentdamstabilityanalysisusingartificialneuralnetworks AT jelenamarkovicbrankovic highembankmentdamstabilityanalysisusingartificialneuralnetworks AT srdjanzivkovic highembankmentdamstabilityanalysisusingartificialneuralnetworks |