Flexural Behavior of a Novel Textile-Reinforced Polymer Concrete

Textile reinforced concrete (TRC) has gained attention from the construction industry due to its light weight, high tensile strength, design flexibility, corrosion resistance, and remarkably long service life. Some structural applications that utilize TRC components include precast panels, structural repair, waterproofing elements, and façades. TRC is produced by incorporating textile fabrics into thin cementitious concrete panels. Premature debonding between the textile fabric and concrete due to improper cementitious matrix impregnation of the fibers was identified as a failure-governing mechanism. To overcome this performance limitation, in this study, a novel type of TRC is proposed by replacing the cement binder with a polymer resin to produce textile reinforced polymer concrete (TRPC). The new TRPC is created using a fine-graded aggregate, methyl methacrylate polymer resin, and basalt fiber textile fabric. Four different specimen configurations were manufactured by embedding 0, 1, 2, and 3 textile layers in concrete. Flexural performance was analyzed and compared with reference TRC specimens with similar compressive strength and reinforcement configurations. Furthermore, the crack pattern intensity was determined using an image processing technique to quantify the ductility of TRPC compared with conventional TRC. The new TRPC improved the moment capacity compared with TRC by 51%, 58%, 59%, and 158%, the deflection at peak load by 858%, 857%, 3264%, and 3803%, and the toughness by 1909%, 3844%, 2781%, and 4355% for 0, 1, 2, and 3 textile layers, respectively. TRPC showed significantly improved flexural capacity, superior ductility, and substantial plasticity compared with TRC.

Investigation on the Displacement Ductility Coefficient of Reinforced Concrete Columns Strengthened with Textile-Reinforced Concrete

To evaluate the seismic performance of reinforced concrete (RC) columns strengthened with textile-reinforced concrete (TRC), based on the ABAQUS numerical analysis results of 15 TRC-strengthened RC columns, the grey correlation theory was used to determine the input variables of the model, and the accuracy of the numerical simulation results is verified by some experiments. Then, according to FEM data, a neural network prediction model was established for the displacement ductility coefficients of TRC-strengthened columns, and a formula was proposed for calculating the displacement ductility coefficient. The results showed that the BP (backpropagation) neural network model had good rationality and accuracy and that the ductility coefficients of the strengthened columns calculated by the model agreed well with the experimental values. Therefore, the model can be applied for predicting the displacement ductility coefficients of TRC-strengthened columns and can be used as a reference for engineering design.