Structural damage detection based on stochastic subspace identification and statistical pattern recognition: II. Experimental validation under varying temperature

Abstract

Although most vibration-based damage detection methods can acquire satisfactory verification on analytical or numerical structures, most of them may encounter problems when applied to real-world structures under varying environments. The damage detection methods that directly extract damage features from the periodically sampled dynamic time history response measurements are desirable but relevant research and field application verification are still lacking. In this second part of a two-part paper, the robustness and performance of the statistics-based damage index using the forward innovation model by stochastic subspace identification of a vibrating structure proposed in the first part have been investigated against two prestressed reinforced concrete (RC) beams tested in the laboratory and a full-scale RC arch bridge tested in the field under varying environments. Experimental verification is focused on temperature effects. It is demonstrated that the proposed statistics-based damage index is insensitive to temperature variations but sensitive to the structural deterioration or state alteration. This makes it possible to detect the structural damage for the real-scale structures experiencing ambient excitations and varying environmental conditions.

Recent progress and future trends on damage identification methods for bridge structures

“…Comanducci et al conducted the vibration‐based damage detection using multivariate statistical techniques, and they used the concept of local principal component analysis, which allows one to overcome the assumption of linear correlation between output variables. Ren et al and Lin et al established a damage‐sensitive but environment‐insensitive damage index through the covariance‐driven identification based on the stochastic subspace together with the statistical pattern recognition technology; the damage index is insensitive to the temperature variation; numerical results show it can detect 20% stiffness reduction at a position near three‐fouth span of the first span of a continuous beam; moreover, the method proves capable of detecting 17% prestressed force loss in a laboratory prestressed reinforced concrete beam, as well as the damage supposed by the renewal stage change in a real box‐type reinforced concrete arch bridge under varying temperature.…”

Structural Health Monitoring of a Prestressed Concrete Bridge Based on Statistical Pattern Recognition of Continuous Dynamic Measurements Over 14 Years

“…Afterwards, statistical hypothesis tests are carried out to judge whether new data can still be explained by the initial model [36]. Lin and Ren validate its efficiency in a rehabilitation process of a full-scale arch bridge under varying environments [37]. Döhler et al employ both the modal parameters and the statistical null space-based damage detection methods to detect the artificial progressive damage of a prestressed concrete bridge.…”

Modal and thermal analysis of Les Arches unstable rock column (Vercors massif, French Alps)

“…Discriminating reversible temperature effects from irreversible damaging effects is then required for resonance frequency monitoring. Although several techniques have been proposed for civil engineering structures such as reinforced concrete beams or buildings (Yuean & Kuok 2010;Lin et al 2011), there is currently no easy and validated method to tackle this problem.…”