Framework for Advanced Sensor Technologies in Non-Destructive Structural Health Monitoring

Abstract

This paper presents a comprehensive framework that leverages IoT sensors, real-time data processing, and deep learning algorithms to provide real-time, accurate diagnostics and prognostics of structural health. The framework integrates high-resolution sensors, robust data acquisition systems, and sophisticated data processing techniques to provide real-time, accurate assessments of structural health. The integration of deep learning and Internet of Things (IoT) technologies offers a powerful approach to structural damage localization in civil infrastructure. Additionally, multiple feature extraction techniques are combined to enhance the accuracy and reliability of the structural health assessments. The proposed system includes data collection, preprocessing, feature extraction, deep learning model training, real-time damage detection, visualization, and continuous monitoring. The framework aims to enhance the maintenance and safety of civil structures through timely and accurate damage localization. The data processing component utilizes machine learning algorithms and signal processing techniques to analyze the sensor data, detect anomalies, and predict potential failures. The effectiveness of the proposed framework is validated through simulations and experimental case studies, demonstrating its potential to enhance the safety, reliability, and longevity of critical civil infrastructure.