Integrated Smart Geotechnical Monitoring Using Ensemble Transfer Learning and Multi-Agent Systems - Pande, Prashant; Raut, Jayant; Bhagat, Rajesh; Bohoriya, Boskey; Tatode, Amol (Engineering Access, 2026)
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.