A Gaussian processes-based approach for damage detection of concrete structure using temperature-induced strain. (1st October 2022)

• Record Type: Journal Article
• Authors: Fu, Wenwei; Sun, Bochao; Wan, HuaPing; Luo, Yaozhi; Zhao, Weijian
• Is Part Of: Engineering structures. Volume 268(2022)
• Publication Date: 2022-10-01
• Subjects: Temperature-induced strain -- Damage detection -- Gaussian processes -- Concrete structure -- Freeze-thaw cycles; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114740
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Developed a non-destructive and real-time approach for concrete damage detection based on Gaussian processes. Established a reliable damage index considering the probability distribution of model residuals using the K-S test. Assessed the effectiveness of the Gaussian processes-based approach using strain and temperature in three different cases. The damage occurrence time of concrete during 100 F-T cycles was accurately detected by the proposed approach. Abstract: Concrete structures in cold regions are highly susceptible to internal cracks due to the periodic variation of temperature and the alternation of drying and wetting in the long term. Conventional damage detecting techniques for concrete are often destructive and need tedious manual operation. Concrete strain monitoring is a non-destructive measurement technique, which provides continuously streaming strain data and has the potential of detecting damage automatically based on a data-driven approach. Temperature-induced strains are highly responsive to damage to concrete structures under the freeze-thaw (F-T) cycles. In this regard, we propose a novel damage detection approach based on temperature-induced strains. The proposed method separates the temperature-induced component from the measured data based on the independent component analysis, which improves the modeling accuracy of the Gaussian processes modeling (GPM) approach for detecting damage. The principle of damage detection is extracting the features related to the damage from the measured strain data. The model residuals of temperature-induced strains are extracted as features that are sensitive to concrete damage. A novel damage index is established to determine the presence of structural damage based on the Kolmogorov Smirnov (KS) test, which estimates the probability distribution of residuals. To increase the reliability of damage detection and decrease the pseudo fault alarm rate, the general extreme value (GEV) theory is considered to determine a reliable threshold limit. A moving window strategy is introduced to perform damage detection and identify the damage occurrence time effectively. Monitoring data of the F-T cycle experiment are utilized to validate the proposed method. The results of three different damage cases demonstrate the effectiveness of the proposed data-driven method in terms of detecting the damage and identifying the damage occurrence time. The variation rules of the concrete temperature characteristics are revealed through the damage detection results under F-T cycles.