journal article·2022-12Open access

Structural monitoring method for RC column with distributed self-sensing BFRP bars

Yongsheng Tang, Taofeng Jiang, Yun Wan

Abstract

Reinforced concrete (RC) columns may be damaged under earthquakes or wind loads, which will severely affect their bearing capacity. Current damage evaluation methods are mainly based on displacement measurement, which makes evaluating local structural damage difficult. Therefore, this study proposes an RC column structural monitoring and evaluation method based on a distributed self-sensing basalt fiber reinforced polymer (BFRP) bar with optical fiber sensors inside. First, the self-sensing BFRP bar is introduced, including the sensing principle, inner structure, and basic sensing performance. It is found that the strain sensing coefficient of the proposed self-sensing BFRP bar presents only 2.2 % difference from that of the bare optical fiber sensor, meaning the BFRP package take no negative effect on the strain sensitivity. Second, the analytical methods of curvature and displacement are established based on the strain distribution measurement, in which the influence of bond-slip on parameter calculations is mainly considered. Finally, the proposed method was well verified by the loading tests of one RC column with self-sensing BFRP bars. Among the results, the absolute displacement measurement error increase with the development of structure damage, which can be even up to several mm after the steel yielding. However, the relative displacement measurement error can be still controlled within 20 % until the structure failure of the RC column. Considering the additional long-term performance of the proposed self-sensing BFRP bar, the proposed method is expected to implement long-term monitoring of RC columns.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Fiber Optic Sensors · Structural Health Monitoring Techniques · Structural Behavior of Reinforced Concrete

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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