An intelligent model for predicting the behavior of soil conditions depending on external weather conditions
The study focuses on the development of an intelligent system for monitoring and forecasting the condition of road surfaces in Eastern Siberia, addressing challenges posed by extreme climate fluctuations. Seasonal variations in temperature and soil moisture critically impact the load-bearing capacit...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_02012.pdf |
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| _version_ | 1850195531206230016 |
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| author | Antamoshkin Oleslav Mikhalev Anton Menshenin Andrey Lukishin Alexander |
| author_facet | Antamoshkin Oleslav Mikhalev Anton Menshenin Andrey Lukishin Alexander |
| author_sort | Antamoshkin Oleslav |
| collection | DOAJ |
| description | The study focuses on the development of an intelligent system for monitoring and forecasting the condition of road surfaces in Eastern Siberia, addressing challenges posed by extreme climate fluctuations. Seasonal variations in temperature and soil moisture critically impact the load-bearing capacity of road structures, leading to accelerated wear, deformations, and safety risks. This research integrates advanced machine learning models, including LSTM, Transformer, TCN, and XGBoost, to predict changes in road conditions based on meteorological and soil data. Field measurements of soil elasticity modules were analyzed to assess seasonal impacts, with LSTM demonstrating the highest accuracy (MSE: 0.025, MAE: 0.0045). The findings confirm that freezing increases soil stability during winter, while spring thawing causes significant weakening due to over-saturation. Strengthening road bases with 30% sludge improved their durability and resilience under heavy loads. The proposed system combines real-time monitoring with predictive analytics, offering a practical tool for infrastructure management in extreme climates. Key outcomes include optimized maintenance schedules, recommendations for spring traffic restrictions, and strategies to mitigate road degradation. This work highlights the potential of machine learning in enhancing the efficiency and safety of road infrastructure, contributing to sustainable transportation in cold regions. |
| format | Article |
| id | doaj-art-99f0ca55106e4cbea4dee20f6c0b8d5c |
| institution | OA Journals |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-99f0ca55106e4cbea4dee20f6c0b8d5c2025-08-20T02:13:44ZengEDP SciencesITM Web of Conferences2271-20972025-01-01720201210.1051/itmconf/20257202012itmconf_hmmocs-III2024_02012An intelligent model for predicting the behavior of soil conditions depending on external weather conditionsAntamoshkin Oleslav0Mikhalev Anton1Menshenin Andrey2Lukishin Alexander3Siberian Federal UniversityReshetnev Siberian State University of Science and TechnologySiberian Federal UniversitySiberian Federal UniversityThe study focuses on the development of an intelligent system for monitoring and forecasting the condition of road surfaces in Eastern Siberia, addressing challenges posed by extreme climate fluctuations. Seasonal variations in temperature and soil moisture critically impact the load-bearing capacity of road structures, leading to accelerated wear, deformations, and safety risks. This research integrates advanced machine learning models, including LSTM, Transformer, TCN, and XGBoost, to predict changes in road conditions based on meteorological and soil data. Field measurements of soil elasticity modules were analyzed to assess seasonal impacts, with LSTM demonstrating the highest accuracy (MSE: 0.025, MAE: 0.0045). The findings confirm that freezing increases soil stability during winter, while spring thawing causes significant weakening due to over-saturation. Strengthening road bases with 30% sludge improved their durability and resilience under heavy loads. The proposed system combines real-time monitoring with predictive analytics, offering a practical tool for infrastructure management in extreme climates. Key outcomes include optimized maintenance schedules, recommendations for spring traffic restrictions, and strategies to mitigate road degradation. This work highlights the potential of machine learning in enhancing the efficiency and safety of road infrastructure, contributing to sustainable transportation in cold regions.https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_02012.pdf |
| spellingShingle | Antamoshkin Oleslav Mikhalev Anton Menshenin Andrey Lukishin Alexander An intelligent model for predicting the behavior of soil conditions depending on external weather conditions ITM Web of Conferences |
| title | An intelligent model for predicting the behavior of soil conditions depending on external weather conditions |
| title_full | An intelligent model for predicting the behavior of soil conditions depending on external weather conditions |
| title_fullStr | An intelligent model for predicting the behavior of soil conditions depending on external weather conditions |
| title_full_unstemmed | An intelligent model for predicting the behavior of soil conditions depending on external weather conditions |
| title_short | An intelligent model for predicting the behavior of soil conditions depending on external weather conditions |
| title_sort | intelligent model for predicting the behavior of soil conditions depending on external weather conditions |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_02012.pdf |
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