Abstract
Residual strength of corroded textile-reinforced concrete (TRC) is evaluated using the deep learning-based method, whose feasibility is demonstrated by experiment. Compared to the traditional method, the proposed method does not need to know the climatic conditions in which the TRC exists. Firstly, the information about the faster region-based convolutional neural networks (Faster R-CNN) is described briefly, and then procedures to prepare datasets are introduced. Twenty TRC specimens were fabricated and divided into five groups that were treated to five different corrosion degrees corresponding to five different residual strengths. Five groups of images of microstructure features of these TRC specimens with five different residual strengths were obtained with portable digital microscopes in various circumstances. With the obtained images, datasets required to train, validate, and test the Faster R-CNN were prepared. To enhance the precision of residual strength evaluation, parameter analysis was conducted for the adopted model. Under the best combination of considered parameters, the mean average precision for the residual strength evaluation of the five groups of the TRC is 98.98%. The feasibility of the trained model was finally verified with new images and the procedures to apply the presented method were summarized. The paper provides new insight into evaluating the residual strength of structural materials, which would be helpful for safety evaluation of engineering structures.
Highlights
Textile-reinforced concrete (TRC), which is a new type of composite cement-based material, has received great attention due to its high tensile strength and excellent performance in alkali resistance.Many studies have been carried out for investigating the basic mechanical properties of the TRC.Some scholars investigated the effects of such parameters as the loading rate, temperature, and the arrangement of textile layers on the bending behavior of members made of the TRC through three-point or four-point bending experiments [1,2,3,4]
Though the deep learning-based approaches have been applied in many aspects of material science and engineering, up to now no such approaches have been reported to be used to evaluate the residual strength of the TRC in the literature, and the present study offers a first attempt to evaluate the residual strength of materials from a deep learning perspective
Previous models to predict the residual strength of textile-reinforced concrete need to know the climatic conditions in which the TRC exists, which is difficult in practice
Summary
Textile-reinforced concrete (TRC), which is a new type of composite cement-based material, has received great attention due to its high tensile strength and excellent performance in alkali resistance.Many studies have been carried out for investigating the basic mechanical properties of the TRC.Some scholars investigated the effects of such parameters as the loading rate, temperature, and the arrangement of textile layers on the bending behavior of members made of the TRC through three-point or four-point bending experiments [1,2,3,4]. Textile-reinforced concrete (TRC), which is a new type of composite cement-based material, has received great attention due to its high tensile strength and excellent performance in alkali resistance. Many studies have been carried out for investigating the basic mechanical properties of the TRC. Some scholars investigated the effects of such parameters as the loading rate, temperature, and the arrangement of textile layers on the bending behavior of members made of the TRC through three-point or four-point bending experiments [1,2,3,4]. Some investigated the effects of some parameters, including the prestress levels, steel fiber properties, and freezing-thawing cycles, on the tensile performance of members made of the TRC [5,6]. Some numerical models were developed for predicting the bending and tensile behaviors of TRC sandwich beams and verified with
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