Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition
IntroductionOver the past few decades, China has vigorously advanced its strategy to build a powerful transportation network, constructing and maintaining numerous slope engineering projects. However, frequent major safety incidents caused by slope failures highlight the urgent need for automated id...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1531857/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832593921311506432 |
|---|---|
| author | Nana Han Nana Han Wending Miao Mingzhi Li Mohd Ashraf Mohamad Ismail Qiang Hu Liyuan Duan Liyuan Duan Jintao Tang |
| author_facet | Nana Han Nana Han Wending Miao Mingzhi Li Mohd Ashraf Mohamad Ismail Qiang Hu Liyuan Duan Liyuan Duan Jintao Tang |
| author_sort | Nana Han |
| collection | DOAJ |
| description | IntroductionOver the past few decades, China has vigorously advanced its strategy to build a powerful transportation network, constructing and maintaining numerous slope engineering projects. However, frequent major safety incidents caused by slope failures highlight the urgent need for automated identification of failure events during the operational phase of slopes.MethodsThis study integrates rainfall, surface displacement, and vertical displacement monitoring data, and proposes an automatic failure mode identification method based on deep convolutional autoencoder technology. The model is trained on monitoring data collected during the normal operational phase of slopes, extracting features from normal data to reconstruct the original data. The trained model is then utilized for structural anomaly detection by leveraging the characteristic that reconstruction errors for failure mode samples are significantly higher than for normal samples.ResultsA case study was conducted on a specific slope where, on 24 May 2024, the displacement development rate in some areas increased significantly, ultimately leading to collapse. The proposed model accurately identified the time and evolution of the landslide, demonstrating its capability to detect failure events effectively.DiscussionValidation results confirm that the model can effectively distinguish previously unseen abnormal modes, offering significant practical value for identifying similar structural anomalies. This approach provides a reliable tool for slope monitoring and anomaly detection, enhancing safety in slope engineering projects. |
| format | Article |
| id | doaj-art-4440996c733b4eaf8040edb8e0c3f0dd |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-4440996c733b4eaf8040edb8e0c3f0dd2025-01-20T07:20:14ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-01-011310.3389/feart.2025.15318571531857Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognitionNana Han0Nana Han1Wending Miao2Mingzhi Li3Mohd Ashraf Mohamad Ismail4Qiang Hu5Liyuan Duan6Liyuan Duan7Jintao Tang8School of Civil Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, MalaysiaFaculty of Architectural Engineering, Guizhou Equipment Manufacturing Polytechnic, Guiyang, Guizhou, ChinaGuizhou Transportation Planning Survey and Design Academe Co., Ltd., Guiyang, Guizhou, ChinaGuangxi Communications Design Group Co., Ltd., Nanning, Guangxi, ChinaSchool of Civil Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, MalaysiaGuizhou Transportation Planning Survey and Design Academe Co., Ltd., Guiyang, Guizhou, ChinaSchool of Physics, Universiti Sains Malaysia, Minden, Penang, MalaysiaFaculty of Architectural Engineering, Guizhou Light Industry Technical College, Guiyang, Guizhou, ChinaYunnan Provincial Transportation Planning and Design Institute Co., Ltd., Kunming, Yunna, ChinaIntroductionOver the past few decades, China has vigorously advanced its strategy to build a powerful transportation network, constructing and maintaining numerous slope engineering projects. However, frequent major safety incidents caused by slope failures highlight the urgent need for automated identification of failure events during the operational phase of slopes.MethodsThis study integrates rainfall, surface displacement, and vertical displacement monitoring data, and proposes an automatic failure mode identification method based on deep convolutional autoencoder technology. The model is trained on monitoring data collected during the normal operational phase of slopes, extracting features from normal data to reconstruct the original data. The trained model is then utilized for structural anomaly detection by leveraging the characteristic that reconstruction errors for failure mode samples are significantly higher than for normal samples.ResultsA case study was conducted on a specific slope where, on 24 May 2024, the displacement development rate in some areas increased significantly, ultimately leading to collapse. The proposed model accurately identified the time and evolution of the landslide, demonstrating its capability to detect failure events effectively.DiscussionValidation results confirm that the model can effectively distinguish previously unseen abnormal modes, offering significant practical value for identifying similar structural anomalies. This approach provides a reliable tool for slope monitoring and anomaly detection, enhancing safety in slope engineering projects.https://www.frontiersin.org/articles/10.3389/feart.2025.1531857/fullmulti-source data fusiondeep convolutional autoencoderslope displacementrainfallhealth monitoring |
| spellingShingle | Nana Han Nana Han Wending Miao Mingzhi Li Mohd Ashraf Mohamad Ismail Qiang Hu Liyuan Duan Liyuan Duan Jintao Tang Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition Frontiers in Earth Science multi-source data fusion deep convolutional autoencoder slope displacement rainfall health monitoring |
| title | Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition |
| title_full | Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition |
| title_fullStr | Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition |
| title_full_unstemmed | Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition |
| title_short | Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition |
| title_sort | integrating multi source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition |
| topic | multi-source data fusion deep convolutional autoencoder slope displacement rainfall health monitoring |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1531857/full |
| work_keys_str_mv | AT nanahan integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition AT nanahan integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition AT wendingmiao integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition AT mingzhili integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition AT mohdashrafmohamadismail integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition AT qianghu integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition AT liyuanduan integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition AT liyuanduan integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition AT jintaotang integratingmultisourcemonitoringdataanddeepconvolutionalautoencodertechnologyforslopefailurepatternrecognition |