A Comparative Machine Learning Algorithms for Integrated Structural Health Monitoring in Smart Infrastructure maintenance and predication

Abstract

The convergence of civil infrastructure and electrical power systems within smart city frameworks necessitates robust, cross-domain monitoring strategies. While machine learning (ML) has shown promise in isolated Structural Health Monitoring (SHM) and Predictive Maintenance (PdM), comparative evaluations across both domains remain fragmented. This study presents a comprehensive comparative analysis of four prominent ML algorithms Random Forest (RF), Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and XGBoost applied to multimodal sensor data. We utilized a synthesized dataset comprising vibration signatures from civil structures (bridge decks) and thermal-electrical load profiles from substation transformers. Our findings indicate that while LSTM networks excel in capturing temporal dependencies in electrical load forecasting (achieving an F1-score of 0.94), tree-based ensemble methods, specifically XGBoost, demonstrate superior efficacy in classifying structural damage from high-dimensional vibration features (accuracy of 96.2%). Furthermore, RF offered the most computationally efficient inference, making it highly suitable for edge-deployment in resource-constrained IoT nodes. This paper provides a practical decision-making framework for civil and electrical engineers selecting ML architectures for integrated smart infrastructure monitoring.