Machine Learning Integration for Predictive Health Monitoring in Earthquake-Resistant Structures
Keywords:
Machine learning, Predictive health monitoring, Structural health monitoring, Earthquake-resistant structures, Socio-technical systemAbstract
This study aims to explore the integration of machine learning into predictive health monitoring for earthquake-resistant structures, with a focus on how field actors understand, interpret, and use predictive systems in structural safety management. The study used a qualitative case study approach conducted from November 2025 to February 2026 in earthquake-prone areas in Indonesia. Data were collected through semi-structured interviews, field observations, and document analysis involving structural engineers, sensor technicians, facility managers, construction consultants, machine learning system developers, and building managers. The findings reveal five main themes: readiness of monitoring infrastructure and sensor data, trust and interpretability of machine learning outputs, organizational barriers in predictive monitoring implementation, the role of human expertise in mediating data and decisions, and predictive monitoring as a socio-technical safety system. The study shows that the successful use of machine learning does not depend only on algorithmic accuracy, but also on data integration, explainable outputs, staff competence, clear procedures, and organizational commitment to preventive maintenance. These findings contribute to structural health monitoring literature by extending the discussion from technical model performance to socio-technical implementation in real building management contexts. The study implies that predictive monitoring systems should be designed as decision-support tools that strengthen expert judgment, support risk-based maintenance, and improve safety governance in earthquake-resistant structures. Future research should compare multiple structural contexts and examine ethical, legal, and data security aspects of machine learning-based monitoring.
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