<![CDATA[The construction industry requires advanced monitoring systems to ensure infrastructure safety and sustainability. This study develops a real-time structural health monitoring system integrated with the Internet of Things (IoT) and deep learning-based analytics to enhance structural safety during and after construction. The proposed system incorporates multiple smart sensors and employs a Long Short-Term Memory (LSTM) model to detect early structural deformations and predict potential failures. The experimental results demonstrate that the IoT-based monitoring system significantly improves accuracy in tracking humidity (92.4%), temperature (94.8%), pressure (94.1%), and vibration (97.2%) compared to conventional manual inspections. A comparative analysis with global implementations in Singapore and Japan highlights the efficiency of edge computing integration in reducing latency and improving data reliability. The findings underscore the importance of integrating deep learning with IoT to enhance predictive maintenance in the construction industry. This research contributes to the development of a more accurate, real-time, and scalable monitoring system for ensuring infrastructure resilience and sustainability.]]>
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