• Smart Structures and Systems
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• v.1 no.4
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• pp.369-384
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• 2005

The main objectives of this paper are to demonstrate the feasibility of using newly developed smart GFRP reinforcements to effectively monitor reinforced concrete beams subjected to flexural and creep loads, and to develop non-linear numerical models to predict the behavior of these beams. The smart glass fiber-reinforced polymer (GFRP) rebars are fabricated using a modified pultrusion process, which allows the simultaneous embeddement of Fabry-Perot fiber-optic sensors within them. Two beams are subjected to static and repeated loads (until failure), and a third one is under long-term investigation for assessment of its creep behavior. The accuracy and reliability of the strain readings from the embedded sensors are verified by comparison with corresponding readings from surface attached electrical strain gages. Nonlinear finite element modeling of the smart concrete beams is subsequently performed. These models are shown to be effective in predicting various parameters of interest such as crack patterns, failure loads, strains and stresses. The strain values computed by these numerical models agree well with corresponding readings from the embedded fiber-optic sensors.

• Journal of the Korean Geosynthetics Society
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• v.18 no.4
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• pp.129-138
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• 2019

In this study, the rebar rock bolt sensor and GFRP rock bolt sensor, which can be monitored, were embedded in a large model slope, and the behavior of slopes occurred in the early stage of slope collapse was analyzed after performing the field failure test, numerical analysis of the individual element method and finite element method. By comparing and analyzing the field test and numerical analysis results, field applicability of rock slope collapse monitoring on the rebar rock bolt sensor and GFRP rock bolt sensor was investigated. Through this study, smart slope collapse prediction and warning system was developed, which can be used to induce effective evacuation of residents living in the collapsible area by detecting landslide and ground decay precursor information in advance.