Development and validation of transfer function in seismic-based structural health monitoring for high rise structures

The proliferation of high-rise buildings in seismically active regions underscores the urgent need for advanced Structural Health Monitoring (SHM) systems to ensure safety and resilience. Traditional SHM approaches, often reliant on post-event evaluations and dense sensor networks, fail to provide real-time predictive capabilities, creating a gap in proactive structural monitoring. This study develops a data-driven transfer function tailored for seismic-based SHM in high-rise steel buildings. Leveraging seismic data from the Centre for Engineering Strong Motion Data (CESMD) across 1,000 events, a multiple linear regression model was developed to link key predictors, including building dimensions, seismic event attributes, and intermediate sensor spectral accelerations, to structural response measures such as peak and spectral accelerations. Spectral acceleration data from three strategically placed sensors formed the foundation of the analysis. Results indicate that five statistically significant variables explain approximately 70% of the variability in top-floor accelerations (R 2 = 0.705). Mid-level sensor spectral acceleration (β = 0.414, p < 0.05) emerged as the strongest predictor, highlighting the role of cumulative floor-by-floor demand in response amplification. Building dimensions captured structural stiffness and lateral demand effects, while observed negative correlations with local seismic magnitude suggest complex interaction mechanisms requiring multi-level response assessment. The validated regression-based transfer function enables reliable prediction of seismic response at uninstrumented upper floors, reducing dependence on dense sensor deployment. This framework offers a scalable and cost-effective enhancement to seismic SHM for high-rise structures, supporting improved resilience and proactive structural assessment.