Slant shear tests and fuzzy logic integration for evaluating shear bond strength in SCC and FRSCC repair applications
Vahid Shafaie, Oveys Ghodousian, Amin Ghodousian, Mahdi Homayounfar, Majid Movahedi Rad
Abstract
This study examines the interfacial bond characteristics of twenty mix proportions, comprising ten self-compacting concrete (SCC) and ten fiber-reinforced self-compacting concrete (FRSCC) formulations, the latter enhanced with 0.1% polypropylene fibers for repair applications. Initially, experiments such as slump flow, 28-day compressive strength, and tensile strength tests were conducted to evaluate the mechanical properties of the repair layers intended for use in slant shear tests. The primary focus of the research then shifted to determining shear bond strength (SBS) and calculating interfacial cohesion and friction angles using slant shear tests across various inclination angles on these mix proportions applied over a normal vibrated concrete (NVC) substrate. Notably, FRSCC mixtures with 10% microsilica exhibited notable enhancements, showing increased cohesion of 8.28 MPa and a tensile strength increase of 24.50% compared to their SCC counterparts. Additionally, a general trend was observed where FRSCC mixtures demonstrated higher cohesion values compared to SCC, underscoring the effectiveness of fiber reinforcement. Furthermore, the research introduces a novel predictive model employing a fuzzy system with a generalized Mamdani’s interference engine and Hamacher family of t-norms to accurately predict the SBS, achieving a predictive accuracy with an R 2 value up to 0.94. Employing the fuzzy model, characterized by its high predictive accuracy, can significantly reduce the frequency of experimental tests required in the field, thereby lowering construction testing costs and enhancing repair efficiency. These findings not only advance our understanding of SCC and FRSCC behaviors in repair scenarios but also contribute significantly to the development of more reliable and sustainable construction practices by improving the precision of SBS predictions in theoretical modeling and empirical testing.