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High-performance fiber reinforced concrete as a repairing material to normal concrete structures: Experiments, numerical simulations and a machine learning-based prediction model
بتن مسلح با فیبر با کارایی بالا به عنوان یک ماده ترمیم کننده سازه های بتونی عادی: آزمایش ها ، شبیه سازی عددی و یک مدل پیش بینی مبتنی بر یادگیری ماشین-2019 High-performance fiber reinforced concrete (HPFRC) has been reported as a repairing material to normal
concrete (NC) structures due to its predominant mechanical performance. Here, we investigate the
debonding behavior between HPFRC and NC subjected to direct shear loading. HPFRC specimens are fabricated
and experimentally calibrated to determine the compressive and bending (i.e., flexural) strengths.
HPFRC-NC samples are fabricated using two bonding strategies, i.e., mechanical surface treatments with
and without chemical agent. Direct shear loading is applied to test the HPFRC-NC debonding behavior. A
finite element (FE) model is developed to predict the direct shear debonding response. The FE model is
validated by the experimental observations and then used to characterize the debonding behavior with
various geometric and material parameters, as well as bonding interface treatments. Subsequently, a
robust machine learning model is developed to formulate the shear debonding strength of HPFRC-NC
with those influencing parameters. Design examples are presented to illustrate the efficiency of the proposed
machine learning model in describing the debonding response of HPFRC-NC. A sensitivity analysis
is further conducted to investigate the contribution of the chosen predictors to the debonding behavior of
HPFRC-NC. The reported HPFRC and machine learning-based prediction model provide powerful tools to
address repairing issues in various existing normal concrete structures. Keywords: High-performance fiber reinforced concrete | (HPFRC) | Normal concrete (NC) | Debonding behavior | Machine learning | Prediction model | Direct shear test |
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