Abstract:

The safety and durability of multi-storey reinforced concrete (RC) buildings are critical concerns, particularly in seismically active regions. With rapid urbanization and increasing high-rise construction in India, ensuring structural resilience against dynamic loads and seismic events has become imperative. This study investigates the dynamic response characteristics and seismic vulnerability of multi-storey RC buildings using vibration monitoring techniques combined with numerical modeling. Field-based ambient vibration measurements were conducted on selected buildings to capture modal properties such as natural frequencies, mode shapes, and damping ratios. The experimental data were used to validate finite element models, enabling accurate simulation of dynamic behavior under seismic excitations. Key parameters influencing seismic performance, including storey stiffness, mass distribution, and damping characteristics, were analyzed to identify potential weak points and regions susceptible to damage. The study further explores the integration of vibration-based structural health monitoring (SHM) systems, highlighting how continuous monitoring can provide early detection of structural degradation, inform maintenance decisions, and optimize retrofit strategies. Findings suggest that combining field vibration data with numerical models significantly enhances the reliability of seismic vulnerability assessments and enables proactive management of building safety. The proposed framework emphasizes cost-effective implementation, data-driven decision making, and the use of predictive indicators to extend the service life of multi-storey RC buildings while safeguarding occupant safety. This research offers actionable insights for engineers, urban planners, and policymakers seeking to enhance the seismic resilience of urban infrastructure in India.

Keywords:Vibration Monitoring, Structural Health Monitoring, Seismic Vulnerability, Multi-Storey RC Buildings,Modal Analysis, Dynamic Response, Finite Element Modeling, Predictive Maintenance