Evaluation of the Effects of Microsilica and Rice Husk Ash on the Mechanical Bond Properties of Repair Layers Made of High-Performance Fiber-Reinforced Concrete with Normal Concrete Using Artificial Neural Networks

Document Type : Article extracted From phd dissertation

Authors
Yasser Jalalian 1
Ali Delnavaz 2
Amir Hossein Hashemi 2
Reza Farokhzad 2
1 Department of Civil Engineering and Surveying, Qa.C., Islamic Azad University, Qazvin, Iran.
2 Department of Civil Engineering and Surveying, Qa.C., Islamic Azad University Qazvin, Iran.
10.22034/he.2026.571409.1190
Abstract
In this study, high-performance fiber-reinforced concrete was used as the repair layer. To develop the model, 17 mix designs in three distinct categories were employed. High-performance concrete mixtures were prepared with three dosage levels of microsilica (SF) and rice husk ash (RHA), at 5%, 10%, and 15% by weight of cement. Fiber-reinforced concrete mixtures were produced using two types of fibers: steel fibers (StF) and polypropylene fibers (PPF). For specimen preparation, the substrate concrete was first cast in specific molds for the pull-off test; after 28 days, the specimen surfaces were overlaid with other mix designs under two surface preparation conditions, namely grooved and wire-brushed surfaces. After setting and demolding, curing was carried out under two conditions: water ponding and open-air exposure. The maximum pull-off strength was obtained in specimens containing microsilica, and grooved surface preparation showed a positive effect on achieving higher pull-off strength. Artificial neural networks were employed using the results of the pull-off and slant shear tests to predict the mechanical behavior of the repair layer. A total of 408 data points were used as inputs for the neural network, including 286 samples for training and 61 samples for final testing. The network inputs were classified into five categories, including specimen ages of 28 and 120 days, two surface preparation methods (grooved and wire-brushed), and the type of mix design, while the output consisted of the test result. For this purpose, two types of neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF), were used. Prediction and validation using the artificial neural networks confirmed that ANN performance in predicting the experimental results is reliable and cost-effective in terms of both time and expense.
Keywords
High-performance concrete
fiber-reinforced concrete
steel and polypropylene fibers
microsilica (SF) and rice husk ash (RHA)
pull-off test
Subjects
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