Big Data–Driven Structural Health Monitoring of High-Rise Buildings Using IoT Sensor Networks

Abstract

Large amounts of diverse sensor data are frequently difficult for conventional structural health monitoring (SHM) techniques to handle in real time. Because of their increasing complexity of structures and susceptibility to dynamic loads including wind, seismic activity, & occupancy-induced vibrations, high-rise buildings' long-term performance and safety are major challenges in contemporary urban environments. This paper suggests an IoT sensor network-based Big Data-Driven Structural Health Monitoring architecture for high-rise structures in order to overcome these constraints. In order to effectively store, process, and analyse high-velocity structural response data, the suggested system combines distributed Internet of Things (IoT) sensors for continuous data collecting with big data analytics platforms. To identify irregularities, evaluate the state of the structure, and anticipate possible damage patterns, advanced data machine learning and analytics approaches are used. The framework improves structural safety and lowers lifetime maintenance costs by enabling continuous tracking, early damage detection, and predictive maintenance. When compared to traditional SHM techniques, the suggested big data-driven strategy greatly increases monitoring accuracy, adaptability, and decision-making efficiency, according to experimental evaluation utilising simulated & real-time sensor datasets. The findings demonstrate how data analysis and IoT technology can be combined to create intelligent, robust, and smart infrastructures for high-rise buildings.