Properties of unreinforced concrete and cement-based matrix are well understood. One of the issues with the cement-based matrix is its inherently brittle failure when exposed to loading. As such, steel fibers were proposed to enhance the ductility of cement-based and concrete materials. Ever since, Fiber-Reinforced Concrete (FRC) has become a commonly used building material in many construction activities such as bridges, airport pavements, shotcrete, and many others. According to previous research, the addition of steel fibers, typically from 20 to 50 kg/m3 into the conventional concrete, can significantly enhance many of the desired engineering properties of hardened concrete such as flexural strength, tensile strength, micro-cracks as well as splitting. This research presents a study aimed to numerically investigate the influence of steel fibers on the dynamic behavior of Plain Concrete (PC) exposed the tensile loading at medium strain-rate. The influence of steel fibers is investigated using different fiber volume fractions ranging from 0.0 to 4.5%. The Modified Split-Hopkinson-Bar (MSHB) apparatus is employed to investigate the dynamic tensile behavior of PC and Steel Fiber-Reinforced Concrete (SFRC). Validation of the finite element model and constitutive material behavior is carried out with the comparison of computed and measured experimental pull-back velocities of the specimen’s free end. The results showed that impact properties of steel fibers exhibit significant improvement in the toughness and the dynamic tensile strength of concrete and higher fiber volume fraction is more effective in enhancing the mechanical properties of SFRC composite.

This research explores the dynamic responses of reinforced concrete (RC) slabs under high-velocity impacts. The study utilizes advanced numerical simulations to investigate the effects of varied impact loading and slab depths, unveiling complex rate-dependent behaviors (i.e., impact forces, reactions, accelerations, and displacements) across a diverse range of velocities. The alignment of simulation results with experimental data validates the robustness and accuracy of the employed approach. Additionally, an analytical model predicting the load-carrying capacity and deflection of these slabs under high-velocity loads is proposed. The results indicated that higher loading rates correlate with increased forces and damage until perforation. Analytical models exhibit strong performance within a ±10% error margin, and response surface analysis quantifies the impactor velocity's influence on load for a constant thickness. Overall, this investigation sheds light on the dynamic complexities of RC slabs subjected to high-velocity impacts, providing valuable insights for structural design considerations.

The development of sustainable concrete capable of trading off the mechanical performance and cost remains a persistent scientific and engineering challenge. Although previous research has employed multi-objective optimization for binary and ternary cement blends, the simultaneous optimization of quaternary-blended systems, incorporating multiple supplementary cementitious materials, has received little systematic attention. This study addresses this gap by introducing an interpretable artificial intelligence (AI)-driven approach that integrates the Category Boosting (CatBoost) algorithm with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to model and optimize the compressive strength (CS) and total cost of quaternary-blended concretes. A curated database of 810 experimentally documented mixtures was used to train and validate the model. CatBoost achieved superior predictive performance (R² = 0.987, MAE = 1.574 MPa), while Shapley additive explanations identified curing age, water-to-binder ratio, and Portland cement content as the dominant parameters governing CS. Multi-objective optimization produced Pareto-optimal elite mixtures achieving CS of 51–80 MPa, with a representative 60 MPa mix requiring approximately 62% less cement than conventional designs. The findings establish a scientifically grounded, interpretable methodology for data-driven design of low-carbon, high-performance concretes and demonstrate, for the first time, the viability of AI-assisted multi-criteria optimization for complex quaternary-blended systems. This framework offers both methodological innovation and practical guidance for implementing sustainable construction materials.