Bridges are essential components of transportation infrastructure, yet aging bridge networks and increasing traffic demands have heightened the need for effective structural health monitoring (SHM) systems. Conventional inspection methods are labor-intensive, subjective, and incapable of providing continuous assessment, making them inadequate for early damage detection and predictive maintenance. This study aims to develop an Artificial Intelligence (AI)-Based Structural Health Monitoring framework for sustainable bridge infrastructure by integrating Internet of Things (IoT) sensors, edge computing, cloud computing, machine learning, deep learning, digital twin technology, and explainable artificial intelligence (XAI). A quantitative engineering research design was employed using multimodal sensor data collected from five instrumented bridges over an 18-month monitoring period, comprising approximately 3.8 million observations from 210 sensors. Sensor signals were preprocessed through denoising, normalization, feature extraction, and missing-value imputation before training and evaluating several AI models, including Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a proposed hybrid CNN–LSTM–Graph Neural Network (GNN) model enhanced with XAI. The proposed framework achieved the highest damage detection accuracy of 99.1%, outperforming conventional machine learning and deep learning models while maintaining localization errors below 8 cm and high remaining useful life prediction accuracy (R² = 0.971–0.993). Sustainability evaluation demonstrated a 31% reduction in maintenance costs, a 46% reduction in emergency repairs, a 38% reduction in bridge downtime, a 27% reduction in maintenance-related carbon emissions, and an estimated extension of bridge service life by 18 years. These findings demonstrate that integrating IoT, hybrid deep learning, graph neural networks, digital twin technology, and explainable AI provides a robust, interpretable, and scalable solution for predictive bridge maintenance, contributing significantly to safer, more resilient, and sustainable transportation infrastructure.
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