AN INTELLIGENT IOT-ENABLED FRAMEWORK FOR STRUCTURAL HEALTH MONITORING OF HIGH-RISE BUILDINGS

Abstract

Structural Health Monitoring (SHM) has become a critical requirement for ensuring the safety and longevity of high-rise buildings subjected to dynamic loads and environmental effects. Traditional inspection methods are often labor-intensive, time-consuming, and incapable of providing continuous monitoring. This research presents an intelligent IoT-enabled framework for real-time structural health monitoring of high-rise buildings. The proposed system integrates smart sensors, wireless communication, cloud-based data processing, and artificial intelligence techniques for early damage detection and condition assessment. Sensor data such as vibration, strain, and displacement are continuously collected and analyzed using machine learning models to identify abnormal structural behavior. The framework enables predictive maintenance and improves decision-making for building management authorities. Experimental validation demonstrates improved accuracy, scalability, and real-time responsiveness. The results confirm that the proposed approach enhances structural safety while reducing maintenance costs and manual inspection efforts.