Integrated Smart Geotechnical Monitoring Using Ensemble Transfer Learning and Multi-Agent Systems

Prashant  Pande; Jayant Raut; Rajesh Bhagat; Boskey Bohoriya; Amol Tatode

Authors

Prashant Pande Yeshwantrao Chavan College of Engineering, India
Jayant Raut Smt Kishoritai Bhoyar College of Pharmacy, India
Rajesh Bhagat Yeshwantrao Chavan College of Engineering, India
Boskey Bohoriya Yeshwantrao Chavan College of Engineering, India
Amol Tatode Yeshwantrao Chavan College of Engineering, India

Keywords:

Geotechnical Monitoring, Ensemble Transfer Learning, Autonomous Multi-Agent Systems, IoT Sensors, Predictive Modelling.

Abstract

The growing complexities in modern infrastructure projects necessitate advanced geotechnical monitoring systems to ensure sustainability and safety. Traditional techniques often fall short due to their inability to provide real-time analysis, adapt to new data patterns, and prevent network failures, leading to delayed detection of soil-related issues. This research proposes an integrated smart monitoring system using IoT and AI technologies, featuring three key methodologies: Ensemble Transfer Learning (ETL), Autonomous Multi-Agent Systems (AMAS), and Long Short-Term Memory Networks with Attention Mechanisms (LSTM-AM). ETL improves prediction accuracy by 20% and reduces false positives by 15%. AMAS minimizes network downtime by 40% and data loss by 30%. LSTM-AM increases failure prediction accuracy by 25% and reduces unexpected failures by 30%. Overall, the proposed system enhances monitoring accuracy by 25%, resilience by 35%, and prevention rate by 20%, offering a robust solution for geotechnical challenges in urban infrastructure.

References

Ponomaryov, A.B., Zakharov, A.V., Tatyannikov, D.A. et al. Geotechnical Monitoring in the Urban Construction Environment. Soil Mech Found Eng 60, 452–458 (2023). https://doi.org/10.1007/s11204-023-09914-y

Fahle, L., Holley, E.A., Walton, G. et al. Analysis of SLAM-Based Lidar Data Quality Metrics for Geotechnical Underground Monitoring. Mining, Metallurgy & Exploration 39, 1939–1960 (2022). https://doi.org/10.1007/s42461-022-00664-3

Reddy, K.R., Janga, J.K. & Kumar, G. Sustainability and Resilience: A New Paradigm in Geotechnical and Geoenvironmental Engineering. Indian Geotech J (2024). https://doi.org/10.1007/s40098-024-00899-5

Liu, H., Su, H., Sun, L. et al. State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering. Artif Intell Rev 57, 196 (2024). https://doi.org/10.1007/s10462-024-10836-w

Wu, X., Yang, F. & Huang, S. Predicting CBR values using gaussian process regression and meta-heuristic algorithms in geotechnical engineering. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00428-0

Yaghoubi, E., Yaghoubi, E., Khamees, A. et al. A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering. Neural Comput & Applic 36, 12655–12699 (2024). https://doi.org/10.1007/s00521-024-09893-7

Chenna, H.N.P., Chouksey, S.K. Comparison of Geotechnical Properties of Landfill Waste in Different Indian Cities. Indian Geotech J (2024). https://doi.org/10.1007/s40098-024-01003-7

Sundaram, R., Gupta, S. & Gupta, S. Advances in the State-of-Practice of Geotechnical Investigation in India. Indian Geotech J 54, 109–133 (2024). https://doi.org/10.1007/s40098-023-00750-3

Hemdan, E.ED., Al-Atroush, M.E. An efficient IoT-based soil image recognition system using hybrid deep learning for smart geotechnical and geological engineering applications. Multimed Tools Appl 83, 66591–66612 (2024). https://doi.org/10.1007/s11042-024-18230-y

Tang, D., Wang, C., Hu, B. et al. Calibration of Geotechnical Parameters Based on the Stochastic Discrete-continuum Coupling Method and Bayesian Theory. KSCE J Civ Eng 27, 1054–1065 (2023). https://doi.org/10.1007/s12205-022-1449-7

Zhang, W., Liu, Y., Li, J. et al. ESPFEM2D: A MATLAB 2D explicit smoothed particle finite element method code for geotechnical large deformation analysis. Comput Mech 74, 467–484 (2024). https://doi.org/10.1007/s00466-024-02441-z

Kommanamanchi, V., Duddu, S.R. & Chennarapu, H. Evaluation of geotechnical characterization of recycled sand as a sustainable replacement for natural sand. J Mater Cycles Waste Manag 26, 2445–2463 (2024). https://doi.org/10.1007/s10163-024-01981-z

Liu, Q., Lei, Y., Yin, X. et al. Development and application of a novel probabilistic back-analysis framework for geotechnical parameters in shield tunneling based on the surrogate model and Bayesian theory. Acta Geotech. 18, 4899–4921 (2023). https://doi.org/10.1007/s11440-023-01850-3

Saha, A., Sekharan, S. Potential Application of Superabsorbent Hydrogel Composite in Geotechnical Engineering Focusing Sustainability: State-of-the-Art Review. Indian Geotech J 54, 1095–1116 (2024). https://doi.org/10.1007/s40098-024-00867-z

Duan, M., Xiao, X. Enhancing soil pile-bearing capacity prediction in geotechnical engineering using optimized decision tree fusion. Multiscale and Multidiscip. Model. Exp. and Des. 7, 2861–2876 (2024). https://doi.org/10.1007/s41939-024-00375-w

Kumar, R., Kumari, S. Geotechnical properties of materials used in landfill clay liner: A critical review. Sādhanā 48, 64 (2023). https://doi.org/10.1007/s12046-023-02124-0

Gayathri, V.L., Vangla, P. & Dey, S. A versatile apparatus for assessing the shear behaviour of geotechnical interfaces coupled with imaging and acoustic capabilities. Acta Geotech. (2024). https://doi.org/10.1007/s11440-023-02220-9

Al Khazaleh, M., Bisharah, M. ANN-based prediction of cone tip resistance with Tabu-Search optimization for geotechnical engineering applications. Asian J Civ Eng 24, 3037–3054 (2023). https://doi.org/10.1007/s42107-023-00693-3

Satyam, N., Reddy, K.R. State-of-the-Art Review on Geotechnical Engineering Towards Sustainability. Indian Geotech J 54, 721–724 (2024). https://doi.org/10.1007/s40098-024-00972-z

Yin, Zy., Wang, Hl. & Geng, Xy. Physical model testing in geotechnical engineering. J. Zhejiang Univ. Sci. A 23, 845–849 (2022). https://doi.org/10.1631/jzus.A22PMTGE

Z. Songan et al., "Three-Dimensional Information Modeling Based on Incomplete Data for Anchor Engineering," in IEEE Access, vol. 11, pp. 122526-122540, 2023, doi: 10.1109/ACCESS.2023.3329067.

Y. Xiao, J. Guo, S. Chen, L. Liu and B. Chen, "Digitalization of Rock Fracture Signal Identification From Tunnel Microseismic Data," in IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 3002605, doi: 10.1109/LGRS.2024.3399271.

Z. Liu, G. Cai, J. Wang, X. Liu and R. Tang, "Landslide Monitoring Suitability of Sensor-Enabled Piezoelectric Geocables Based on Drawing Tests," in IEEE Sensors Journal, vol. 23, no. 20, pp. 25433-25439, 15 Oct.15, 2023, doi: 10.1109/JSEN.2023.3310589.

X. Zhu, B. Chen, X. Wang and T. Li, "Time Series Segmentation Clustering: A New Method for S-Phase Picking in Microseismic Data," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 7504605, doi: 10.1109/LGRS.2021.3098809.

B. Hu, X. Zhang, Y. Meng, J. Li, Z. Wang and W. Ding, "Research on Application of Fiber Bragg Grating Seepage Pressure Sensor in Soil Test," in IEEE Sensors Journal, vol. 23, no. 15, pp. 16840-16850, 1 Aug.1, 2023, doi: 10.1109/JSEN.2023.3279490.

E. Hidalgo Fort et al., "Wireless and Low-Power System for Synchronous and Real-Time Structural-Damage Assessment," in IEEE Sensors Journal, vol. 23, no. 12, pp. 13648-13658, 15 June15, 2023, doi: 10.1109/JSEN.2023.3270961.

S. Pellegrini, G. Rizzelli, M. Barla and R. Gaudino, "Algorithm Optimization for Rockfalls Alarm System Based on Fiber Polarization Sensing," in IEEE Photonics Journal, vol. 15, no. 3, pp. 1-9, June 2023, Art no. 7100709, doi: 10.1109/JPHOT.2023.3281670.

H. Zhou, K. Dai, S. Pirasteh, R. Li, J. Xiang and Z. Li, "InSAR Spatial-Heterogeneity Tropospheric Delay Correction in Steep Mountainous Areas Based on Deep Learning for Landslides Monitoring," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14, 2023, Art no. 5215014, doi: 10.1109/TGRS.2023.3307477.

F. Wang, X. Xie, L. Yang, Z. Pei and L. Dong, "Nondestructive Velocity Structure Reconstruction for Complex Rock Mass Using 3-D Acoustic Emission Tomography," in IEEE Sensors Journal, vol. 23, no. 22, pp. 27490-27499, 15 Nov.15, 2023, doi: 10.1109/JSEN.2023.3301671.

M. S. M. Sa’ad et al., "Surface-Mounted Tilt Sensor Using Fiber Bragg Grating Technology for Engineered Slope Monitoring With Temperature Compensation," in IEEE Sensors Journal, vol. 23, no. 12, pp. 12828-12837, 15 June15, 2023, doi: 10.1109/JSEN.2023.3268649.

A. Joshi, S. Agarwal, D. P. Kanungo and R. K. Panigrahi, "Integration of Edge–AI Into IoT–Cloud Architecture for Landslide Monitoring and Prediction," in IEEE Transactions on Industrial Informatics, vol. 20, no. 3, pp. 4246-4258, March 2024, doi: 10.1109/TII.2023.3319671.

W. Zhang, Y. Zhang, F. Zhu and Z. Liang, "Experimental Study on Reflection Superiority of Multisensors Measurement-While-Drilling of Roof Bolter," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022, Art no. 7504911, doi: 10.1109/TIM.2022.3206834.

C. Xiao et al., "Monitoring the Catastrophic Flood With GRACE-FO and Near-Real-Time Precipitation Data in Northern Henan Province of China in July 2021," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 89-101, 2023, doi: 10.1109/JSTARS.2022.3223790.

M. Zeng, H. Zhao, D. Gao, Z. Bian and D. Wu, "Reconstruction of Vehicle-Induced Vibration on Concrete Pavement Using Distributed Fiber Optic," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 24305-24317, Dec. 2022, doi: 10.1109/TITS.2022.3203482.

S. Pellegrini, G. Rizzelli, M. Barla and R. Gaudino, "Polarization-Based Fiber Optic System for Debris Flow Early Warning: On-Field Demonstration," in IEEE Photonics Journal, vol. 16, no. 3, pp. 1-8, June 2024, Art no. 6802208, doi: 10.1109/JPHOT.2024.3403159.

S. Nandy, M. Adhikari, A. Ray, R. Rai and T. N. Singh, "Edge-Centric Intelligent Early Warning System for Residual Soil Stability Prediction in Slope," in IEEE Internet of Things Journal, vol. 11, no. 3, pp. 3832-3839, 1 Feb.1, 2024, doi: 10.1109/JIOT.2023.3293126.

Q. Li, Y. Liu, C. Hong, L. Xiong and Y. Guo, "A Comparative Investigation on Performance of Fiber Bragg Grating Soil Pressure Sensors With Different Configurations," in IEEE Sensors Journal, vol. 23, no. 22, pp. 27397-27405, 15 Nov.15, 2023, doi: 10.1109/JSEN.2023.3322264.

H. Fang, S. Guo, X. Wang, S. Liu, C. Lin and P. Du, "Automatic Urban Scene-Level Binary Change Detection Based on a Novel Sample Selection Approach and Advanced Triplet Neural Network," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023, Art no. 5601518, doi: 10.1109/TGRS.2023.3235917.

P. Zhang et al., "Pixel–Scene–Pixel–Object Sample Transferring: A Labor-Free Approach for High-Resolution Plastic Greenhouse Mapping," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-17, 2023, Art no. 4401717, doi: 10.1109/TGRS.2023.3257293.

A. Joshi, D. P. Kanungo and R. K. Panigrahi, "Multi-Frame Fringing Field Capacitive Soil Moisture Sensor With Enhanced Sensitivity and Penetration Depth," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-13, 2024, Art no. 9509013, doi: 10.1109/TIM.2024.3390215.