Data Fusion Techniques for Infrastructure Monitoring

Abstract

Infrastructure monitoring is critical for ensuring the safety, reliability, and longevity of essential structures such as bridges, buildings, roads, and dams. Traditional monitoring methods often rely on single-source data, which may be limited in scope and accuracy. Data fusion techniques offer a promising approach by integrating information from multiple heterogeneous sources to provide a comprehensive and accurate assessment of infrastructure health. This paper reviews various data fusion methodologies applied to infrastructure monitoring, including sensor data integration, statistical and machine learning approaches, and real-time data processing. We explore the advantages and challenges associated with each technique and present case studies demonstrating their effectiveness. The findings highlight the potential of data fusion to enhance predictive maintenance, early fault detection, and decision-making processes in infrastructure management. Future research directions are proposed to address existing limitations and to further leverage emerging technologies such as the Internet of Things (IoT) and artificial intelligence (AI) in infrastructure monitoring.