Estimation of the Bond Strength of Fiber-Reinforced Polymer Bars in Concrete Using Artificial Intelligence Systems

Buildings, Journal Year: 2024, Volume and Issue: 14(2), P. 369 - 369

Published: Jan. 29, 2024

Fiber-reinforced polymer (FRP) bars have recently been introduced to the market as an alternative steel for internal reinforcement concrete construction exposed situations that could cause corrosion. The bond behavior of FRP varies from bars, mostly due variations in material properties and surface textures. Because unexpected nature crucial FRP–concrete interfacial (FCI) strength, strength between cannot be exactly determined. Numerous experimental investigations conducted with related empirical models established attempt resolve this problem. These were found a restricted capacity generalization small sample sizes experiments. Therefore, more powerful numerical technique capable processing large data sets all possible parameters may affect relationship considering nonlinearity tendency is needed. In study, artificial neural networks adaptive neuro-fuzzy inference system utilized predict based on 238 points collected different studies literature. performance ANN ANFIS predicting bonding was compared other published literature codes. results showed gave higher prediction than models, slight advantage model. For instance, R-squared values proposed 0.94 0.92, respectively, 20 not used develop models. Based sensitivity analysis, diameter compressive most effective both contrast, bar position texture had lower importance index.

Language: Английский

---

Gene‐expression programming‐based models for comprehensive evaluation of bond strength and critical slip of various types of FRP bars in concrete

Structural Concrete, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Abstract This study aims to present a novel approach assess the bond behavior of various types FRP bars in concrete, based on an extensive dataset. The novelty this research lies evaluating both strength and critical slip, which forms basis for creating model between concrete. Furthermore, is constructed using advanced machine learning techniques, specifically gene‐expression programming (GEP), its reliability ensured by relying more diverse experimental dataset compared previous studies. To train validate GEP model, data were collected from experiments involving 793 test specimens, categorized into five groups surface treatments: smooth (Sm), sand‐coated (SC), helically wrapped (HW), grooved (Gr), hybrid (Hbr) achieved combining SC HW. also considers wide range other parameters, including compressive concrete diameter, tensile strength, elastic modulus, embedment length bars. verification results separate testing demonstrate that proposed models accurately predict slip achieving R 2 values 0.794 0.88, respectively. Comprehensive parametric analyses are performed clarify influence key parameters analysis confirm role type modulus enhancing strength; particularly, among different treatment methods, HW exhibits highest followed HBr, Gr, types.

Language: Английский

---

AI-Powered Optimization of Engineered Cementitious Composites Properties and CO₂ Emissions for Sustainable Construction

Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04405 - e04405

Published: Feb. 1, 2025

Language: Английский

---

Forecasting interfacial bond strength in FRP-reinforced concrete using soft computing techniques

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 473, P. 140827 - 140827

Published: March 30, 2025

Language: Английский

---