Smart Structural Monitoring in India: Integrating Machine Learning with Conventional Seismic Safety Practices

India’s diverse seismic profile, spanning Zones II to V as defined by the Bureau of Indian Standards (BIS), necessitates robust and adaptive approaches to ensure structural safety and resilience. Traditional structural health monitoring (SHM) techniques in India primarily rely on visual inspections, periodic assessments, and code-based evaluations, which, while effective, often lack real-time responsiveness and predictive capabilities. In contrast, recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) offer data-driven solutions for continuous monitoring, early damage detection, and predictive maintenance of structures subjected to seismic activity.

This study presents a comparative analysis of conventional SHM methods and modern ML-based approaches in the context of earthquake-resilient buildings in India. The methodology integrates seismic data from past events, including the 2001 Gujarat earthquake, along with simulated structural response datasets. Traditional methods are evaluated based on reliability and compliance with BIS codes, while ML models such as Random Forest and Long Short-Term Memory (LSTM) networks are assessed for accuracy, efficiency, and real-time applicability.

The results indicate that ML-based SHM systems significantly enhance damage detection accuracy and enable predictive insights, particularly in densely populated urban regions and high-risk seismic zones. However, challenges such as data availability, infrastructure cost, and technical expertise remain critical in the Indian context.

The study concludes that a hybrid framework integrating traditional engineering practices with ML techniques offers a promising pathway toward sustainable and earthquake-resilient infrastructure in India.