Construction Materials

Glass fibre-reinforced polymer (GFRP) offers a durable, high-tensile strength alternative to steel rebar in reinforced concrete (RC). However, the inherent lack of ductility in GFRP limits its structural applications, which has led to the development of hybrid GFRP–steel RC systems. The composite nature of these systems requires an accurate understanding of the bond interaction between GFRP rebar and concrete. Existing bond models often fall short of accurately representing the distinct mechanical properties and surface characteristics of GFRP bars, particularly within finite element (FE) analysis environments. To address this gap, the present study proposes a computational method that employs a feedforward neural network (FFNN) trained on experimental data encompassing a specific range of parameters (bar diameters 8–16 mm, concrete strengths 18–50 MPa), including bar diameter, bond length, concrete strength, and cover thickness. Unlike conventional models that typically focus on peak bond strength, the developed FFNN accurately predicts the complete bond–slip relationship. The developed bond model is then integrated into the FE analysis. The simulation results demonstrate strong agreement with experimental data (average R² = 0.93) and effectively capture key behavioral aspects such as crack initiation and propagation.

Accurate prediction of the mechanical performance of fiber-reinforced cement mortars (FRCM) is challenging because fiber geometry and properties vary widely and interact with the cement matrix in a non-trivial way. In this study, we propose an interpretable, computationally light framework that combines principal component analysis (PCA) with multiple linear regression (MLR) to predict compressive strength (Cs) and flexural strength (Fs) from mix proportions and fiber parameters. The literature-based dataset of 52 mortar mixes reinforced with polypropylene, steel, coconut, date palm, and hemp fibers was compiled and analyzed, covering Cs = 4.4–78.6 MPa and Fs = 0.75–16.7 MPa, with fiber volume fraction V_f = 0–15% and fiber length F_l = 4.48–60 mm. PCA performed on the full dataset showed that PC1–PC2 explain 53.4% of the total variance; a targeted variable-selection strategy increased the captured variance to 73.0% for the subset used for regression model development. MLR models built using PC1 and PC2 achieved good accuracy in the low-to-mid strength range, while prediction errors increased for higher-strength mixes (approximately Cs ≳ 60 MPa and Fs ≳ 10 MPa). On an independent validation dataset (n = 10), the refined model achieved mean absolute percentage errors of 11.3% for Fs and 18.5% for Cs. The proposed PCA-MLR approach provides a transparent alternative to more complex data-driven predictors, and it can support preliminary screening and optimization of fiber-reinforced mortar designs for durable structural and repair applications.

Thermal properties, such as thermal conductivity (λ) and heat capacity (C_v), are important in understanding heat transport and the urban heat island (UHI) effect. While many studies focus on surface materials rather than roadbed materials, this study targeted roadbed materials using recycled concrete aggregates mixed with autoclaved aerated concrete (AAC) grains to experimentally measure and to predict the λ and C_v under varied moisture conditions. The results showed that both λ and C_v of all tested samples increased linearly with increasing volumetric water content (θ), and the increment of AAC was effective in reducing the λ values in the whole range of θ. The addition of AAC, on the other hand, did not affect the measured C_v significantly and gave a linear increase in C_v with the increase in θ. The performance of predictive models showed that Archie’s-second-law-based model captured the measured λ values for all tested samples well by modifying the saturation exponent (n = 0.7), and the classic de Vries model predicted the measured C_v well, suggesting that Archie’s-second-law-based model would be useful to evaluate heat transport parameters for roadbed materials in this study.