Influence of spatial fiber distribution parameters on mechanical properties of hybrid fiber reinforced self compacting concrete I.P. Mervin Sanjith, T. Ch. Madhavi, G. Prabhakar Case Studies in Construction Materials, 2025 The mechanical behavior of Fiber Reinforced Concrete (FRC) is highly dependent on the dispersion and orientation of fibers within the matrix. This study investigates the influence of fiber distribution characteristics on the strength properties of Hybrid Fiber Reinforced Self-Compacting Concrete (HFRSCC) using steel, polypropylene, and basalt fibers in varying volume proportions, while maintaining similar aspect ratios. Compressive, split tensile, and flexural strengths were evaluated experimentally. Image processing and the Euclidean distance transform method were used to assess fiber dispersion and orientation factors. A novel parameter, Zonal Skew Index (ZSI), was introduced to capture asymmetry in fiber alignment across sectional zones. Additionally, the percentage of fiber occupancy in each zone was quantified. To predict the mechanical strengths based on fiber distribution behavior, second-degree polynomial regression models supported by Response Surface Analysis (RSA) were developed using fiber distribution factors and ZSI as key predictors. The predicted values showed strong agreement with experimental results, with R² values of 0.97, 0.91, and 0.92 for flexural, split tensile, and compressive strengths, respectively, surpassing the predictive accuracy reported in previous studies. The models achieved low error indices with RMSE not exceeding 1.62 MPa, MAE limited to 1.20 MPa, and MAPE restricted to 2.74%, confirming their predictive reliability. The findings emphasize the critical role of ZSI in mechanical performance, often outweighing the contribution of dispersion alone, especially in tensile-related behaviors. This study underscores the importance of spatial fiber alignment in optimizing mechanical performance of HFRSCC and demonstrates the efficacy of integrating image-based distribution metrics into strength prediction models. • Proposed a Zonal Skew Index (ZSI) to quantify vertical asymmetry in fiber distribution, complementing conventional dispersion metrics. • Utilized image processing with Euclidean distance transform to accurately measure fiber dispersion (η d ) and orientation (η θ ). • Developed response surface-based regression models to predict strength from ηd, ηθ, and ZSI. • Achieved high prediction accuracy (R² up to 0.97) linking fiber distribution to mechanical performance. • Established a framework to optimize HFRSCC mix design through spatial fiber distribution.
Image analysis for dispersion of fibres and strength assessment of hybrid fiber reinforced SCC using artificial intelligence Mervin Sanjith I. P., T. Ch. Madhavi, G. Prabhakar Journal of Adhesion Science and Technology, 2025 Incorporating fibers in concrete improves its ductility, making it a viable option for large construction projects. The mechanical behavior of Fiber Reinforced Concrete is determined by the fibers ability to bridge cracks and their orientation in the fracture plane. Thus, assessing the distribution characteristics of the fibers is essential for predicting the strength of concrete. The dispersion of fibers plays a critical role in both fresh and hardened properties of concrete. This study investigates the influence of adjusting mix proportions on the orientation and dispersion of fibers in Hybrid Fiber Reinforced Self-Compacting Concrete (HFRSCC). The workability, compressive and split tensile strength of HFRSCC were initially tested for various fiber combinations, followed by image-based analysis and distance transform method to quantify fiber dispersion and orientation within the cement matrix. The dispersion factor, orientation factor, and mechanical test results were subsequently utilized to develop three machine learning models including artificial neural network (ANN), Support Vector Regression (SVR) and Linear Regression (LR). These models effectively predicted the compressive and split tensile strengths of HFRSCC, emphasizing the role of fiber distribution and orientation as critical input parameters. The results revealed that combining shorter and longer fibers significantly improved fiber dispersion, workability, and mechanical properties. The ANN model demonstrated high accuracy in strength predictions, highlighting the benefits of optimizing fiber proportions with varying aspect ratios. This study uniquely integrates experimental testing, image-based fiber analysis, and ANN modelling, focusing on fiber aspect ratios rather than only volume fractions.
Flexural behavior of warp knitted textile reinforced concrete impregnated with cementitious binder Mohan A, T.Ch Madhavi Case Studies in Construction Materials, 2024 Textile reinforced concrete is a novel and high performance material which is gaining popularity in the recent days due to its high potential to effectively improve the strength and durability of concrete structures. Owing to absence of coarse aggregate and presence of high cement paste content, TRC has properties that are different from conventional concrete. In this paper, performance of three different types of binders ie., Organic binder, Inorganic Geopolymer binder and epoxy binders for compressive and split tensile strengths was presented and were compared to assess their effectiveness in enhancing properties by testing 12 mortar cubes of size 70.6 mm and 6 mortar cylinders of size 75 × 150 mm. The efficaciousness of weft insertion warp-knitted textile fabrics of ARG and Basalt on the Compressive strength , flexural strength , ductility, toughness, and failure modes were evaluated experimentally by investigations carried out on 4 RCC beams. The effect of the thickness of Textile reinforcement is also studied by using textile layers of thicknesses of 5, 10, and 15 mm. The results indicated that application of basalt textiles fabric using cementitious binder exhibit higher strength and ductility. Basalt textile is more preferable due to its eco-friendly properties. The experimental findings are validated through analytical equations, ensuring the reliability of the study.
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