Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements
Monitoring lateral displacement in deep excavation projects is crucial for structural stability and safety. Traditional methods, like manual inclinometers, are accurate but costly and labor-intensive. Automated systems provide real-time data but face challenges with dense sensor placement and high c...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-08-01
|
| Series: | Underground Space |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967425000406 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849419462052151296 |
|---|---|
| author | Cheng Chen Guan-Nian Chen Song Feng Xiao-Zhen Fan Liang-Tong Zhan Yun-Min Chen |
| author_facet | Cheng Chen Guan-Nian Chen Song Feng Xiao-Zhen Fan Liang-Tong Zhan Yun-Min Chen |
| author_sort | Cheng Chen |
| collection | DOAJ |
| description | Monitoring lateral displacement in deep excavation projects is crucial for structural stability and safety. Traditional methods, like manual inclinometers, are accurate but costly and labor-intensive. Automated systems provide real-time data but face challenges with dense sensor placement and high costs. This study presents a novel prediction method using an extreme learning machine (ELM) optimized by an improved particle swarm optimization (IPSO) algorithm. The IPSO-ELM approach utilizes sparse automated measurements to accurately predict lateral displacement profiles, minimizing the need for dense sensor deployment. A case study of a 30.2-m-deep excavation project in Hangzhou, China, demonstrates the method’s effectiveness. The results demonstrate that the IPSO-ELM model maintains high prediction accuracy, with low root mean square error (RMSE) and mean absolute error (MAE) values, even under conditions of sparse sensor placement. Across the entire test dataset, with a sensor spacing of 5.0 m, the model achieved maximum RMSE values ranging from 0.94 to 2.79 mm and maximum MAE values ranging from 0.77 to 2.18 mm, thereby showcasing its robustness and reliability in predicting lateral displacement. A detailed discussion was conducted on the errors associated with various sensor spacing intervals when implementing the proposed method. This study underscores the potential of IPSO-ELM as a cost-effective and reliable tool for automatic monitoring in increasingly complex urban excavation projects. |
| format | Article |
| id | doaj-art-8faa5804c330458ebe152f9dc4999f4d |
| institution | Kabale University |
| issn | 2467-9674 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj-art-8faa5804c330458ebe152f9dc4999f4d2025-08-20T03:32:04ZengKeAi Communications Co., Ltd.Underground Space2467-96742025-08-012312514510.1016/j.undsp.2025.02.004Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurementsCheng Chen0Guan-Nian Chen1Song Feng2Xiao-Zhen Fan3Liang-Tong Zhan4Yun-Min Chen5School of Engineering, Hangzhou City University, Hangzhou 310015, China; MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China; Corresponding author at: School of Engineering, Hangzhou City University, Hangzhou 310015, China.School of Civil & Environmental Engineering and Geography Science, Ningbo University, Ningbo 315211, ChinaCollege of Civil Engineering, Fuzhou University, Fuzhou 350108, ChinaSchool of Engineering, Hangzhou City University, Hangzhou 310015, ChinaMOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, ChinaMOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, ChinaMonitoring lateral displacement in deep excavation projects is crucial for structural stability and safety. Traditional methods, like manual inclinometers, are accurate but costly and labor-intensive. Automated systems provide real-time data but face challenges with dense sensor placement and high costs. This study presents a novel prediction method using an extreme learning machine (ELM) optimized by an improved particle swarm optimization (IPSO) algorithm. The IPSO-ELM approach utilizes sparse automated measurements to accurately predict lateral displacement profiles, minimizing the need for dense sensor deployment. A case study of a 30.2-m-deep excavation project in Hangzhou, China, demonstrates the method’s effectiveness. The results demonstrate that the IPSO-ELM model maintains high prediction accuracy, with low root mean square error (RMSE) and mean absolute error (MAE) values, even under conditions of sparse sensor placement. Across the entire test dataset, with a sensor spacing of 5.0 m, the model achieved maximum RMSE values ranging from 0.94 to 2.79 mm and maximum MAE values ranging from 0.77 to 2.18 mm, thereby showcasing its robustness and reliability in predicting lateral displacement. A detailed discussion was conducted on the errors associated with various sensor spacing intervals when implementing the proposed method. This study underscores the potential of IPSO-ELM as a cost-effective and reliable tool for automatic monitoring in increasingly complex urban excavation projects.http://www.sciencedirect.com/science/article/pii/S2467967425000406Automated monitoringExtreme learning machineLateral displacementDeep excavationParticle swarm optimizationPredictive modeling |
| spellingShingle | Cheng Chen Guan-Nian Chen Song Feng Xiao-Zhen Fan Liang-Tong Zhan Yun-Min Chen Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements Underground Space Automated monitoring Extreme learning machine Lateral displacement Deep excavation Particle swarm optimization Predictive modeling |
| title | Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements |
| title_full | Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements |
| title_fullStr | Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements |
| title_full_unstemmed | Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements |
| title_short | Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements |
| title_sort | predicting excavation induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements |
| topic | Automated monitoring Extreme learning machine Lateral displacement Deep excavation Particle swarm optimization Predictive modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2467967425000406 |
| work_keys_str_mv | AT chengchen predictingexcavationinducedlateraldisplacementusingimprovedparticleswarmoptimizationandextremelearningmachinewithsparsemeasurements AT guannianchen predictingexcavationinducedlateraldisplacementusingimprovedparticleswarmoptimizationandextremelearningmachinewithsparsemeasurements AT songfeng predictingexcavationinducedlateraldisplacementusingimprovedparticleswarmoptimizationandextremelearningmachinewithsparsemeasurements AT xiaozhenfan predictingexcavationinducedlateraldisplacementusingimprovedparticleswarmoptimizationandextremelearningmachinewithsparsemeasurements AT liangtongzhan predictingexcavationinducedlateraldisplacementusingimprovedparticleswarmoptimizationandextremelearningmachinewithsparsemeasurements AT yunminchen predictingexcavationinducedlateraldisplacementusingimprovedparticleswarmoptimizationandextremelearningmachinewithsparsemeasurements |