Abstract:
Reinforced concrete (RC) structures are well-known for their high durability; however, they remain vulnerable to natural hazards and extreme events that can impact their performance over time. In aggressive environments, there is a high likelihood of increased maintenance, rehabilitation, and repair actions that constitute a significant portion of the total lifecycle spending. Monitoring systems have been implemented during the last decades to collect periodically or continuously essential data about the durability performance of the structures in real operation. However, the effectiveness of these systems is impacted by sensor efficacy, influenced in turn by environmental factors, sensor durability, and power outages, leading to intermittent or permanent data gaps. This study proposes a methodology to address the problem of missing data of a Structural Health Monitoring (SHM) system, specifically aiming to provide more accurate and continuous information from concrete resistivity and temperature sensors to support the early detection of corrosion. The proposed methodology was applied to a repaired RC structure with over fourteen years of data, where significant gaps in the measurements were present. The approach combines several techniques to fill these gaps: deep machine learning for air temperature, generalized linear models for concrete temperature, and pattern recognition for concrete resistivity. To the best of the authors’ knowledge, this is the first time a methodology has been proposed for imputing missing data from resistivity sensors in SHM systems, which are increasingly being implemented. This approach is innovative and offers potential benefits for SHM system managers, providing more information on long-term sensor data that could aid in early corrosion detection and maintenance planning. The application of the proposed methodology to a real case study indicated a successful imputation of 43.4% of missing data although some challenges persist for sensors located in areas characterized by high measurements variability.
