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Communication Dans Un Congrès Année : 2024

Understanding Corrosion in Restored Concrete Zones Through Sensor Data Analysis

Résumé

Despite reinforced concrete bridges being highly durable, they remain susceptible to natural ageing and disasters, potentially impairing their performance over time. This drives an increase in maintenance, rehabilitation, and repair expenditure, accounting for half of the construction sector spending in developed nations. Structural and durability monitoring becomes crucial to establish long-term maintenance schedules and ensure safety throughout their lifespan. Typically, maintenance schemes rely on sensor systems that periodically or continuously collect information, utilizing various sensors such as electrical, optical, chemical, and sound sensors. Sensor reliability is subject to environmental factors, its own durability and potential power outages, which can temporarily or permanently interrupt data acquisition making difficult or even impossible damage detection. This study focuses on analyzing the evolution of concrete electrical resistivity over time in five repaired concrete zones instrumented with durability sensors to estimating corrosion progression. Based on a compiled and filled database by the authors, this research utilizes degradation models and correlation indices to estimate concrete cover deterioration. Leveraging over fourteen years of sensor data from a reinforced concrete structure, the study focuses on comprehending concrete resistivity evolution in repaired zones to gain insights into the loss of protective concrete cover characteristics to reinforcement corrosion. The study deliberates its findings and extends recommendations for applying this approach to other sensor analysis, enabling a more profound understanding of concrete structure health and longevity.
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Dates et versions

hal-04644317 , version 1 (10-07-2024)

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Luis Rincon, Emilio Bastidas-Arteaga, José Matos, Yina Moscoso, Elsa Eustaquio. Understanding Corrosion in Restored Concrete Zones Through Sensor Data Analysis. 20th International Probabilistic Workshop, May 2024, Guimaraes., Portugal. pp.84-91, ⟨10.1007/978-3-031-60271-9_6⟩. ⟨hal-04644317⟩
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