This investigation introduces advanced predictive models for estimating axial strains in Carbon Fiber-Reinforced Polymer (CFRP) confined concrete cylinders, addressing critical aspects of structural integrity in seismic environments. By synthesizing insights from a substantial dataset comprising 708 experimental observations, we harness the power of Artificial Neural Networks (ANNs) and General Regression Analysis (GRA) to refine predictive accuracy and reliability. The enhanced models developed through this research demonstrate superior performance, evidenced by an impressive R-squared value of 0.85 and a Root Mean Square Error (RMSE) of 1.42, and significantly advance our understanding of the behavior of CFRP-confined structures under load. Detailed comparisons with existing predictive models reveal our approaches' superior capacity to mimic and forecast axial strain behaviors accurately, offering essential benefits for designing and reinforcing concrete structures in earthquake-prone areas. This investigation sets a new benchmark in the field through meticulous analysis and innovative modeling, providing a robust framework for future engineering applications and research.

Potholes are a significant hazard, causing severe damage to vehicles and potentially leading to fatal accidents. The automatic detection of potholes is cru

Concrete remains the most widely used construction material globally; however, its intrinsic limitations—low tensile strength, brittle behavior, and susceptibility to microcracking—necessitate performance enhancement for demanding structural applications. Hybrid fiber-reinforced concrete (HFRC) offers a promising solution, yet the optimal balance of steel fibers (SF) and polypropylene fibers (PF) for structural elements such as slabs remains insufficiently understood. This study experimentally investigates the flexural behavior of 42 reinforced concrete slabs (21 one-way and 21 two-way) incorporating systematically varied SF–PF volumetric ratios, advancing current knowledge by identifying performance-optimal hybrid configurations for each slab type. One-way slabs were tested under four-point bending and two-way slabs under three-point bending, with structural responses evaluated in terms of load capacity, cracking behavior, deflection characteristics, and failure modes. The results demonstrate that fiber dosage does not proportionally enhance strength, as excessive content leads to fiber balling and reduced workability—highlighting the need for optimized hybrid proportions rather than indiscriminate addition. Quantitative findings confirm significant performance gains with properly tuned hybrid mixes. For one-way slabs, the optimal combination of 0.7% SF + 0.9% PF achieved 115% of the ultimate load of the control specimen, demonstrating a substantial improvement in flexural resistance. Two-way slabs exhibited even greater enhancements: first-crack load increased by up to 213%, and ultimate load improved by 40.36%, while deflection capacity rose by 44.81% at first crack and 39.80% at ultimate load with the optimal 0.9% SF + 0.1% PF mix. Comparatively, two-way slabs outperformed one-way slabs across all metrics, benefiting from multidirectional stress distribution that enabled more effective fiber engagement. Overall, this study provides new insight into hybrid fiber synergy in RC slabs and establishes quantified optimal SF–PF combinations that significantly enhance load capacity, ductility, and crack resistance for both one-way and two-way systems.

DLB can highly improve the parallel solution of nonlinear finite element problems. However, to integrate DLB algorithms into finite element problems, the solution method must be amenable to the DLB integration. In this work, a hybrid solution approach to parallel nonlinear finite element is introduced and evaluated. Results show that the proposed solution algorithm is highly efficient, with superlinear speedups observed in most test cases. However, solution efficiency of a given problem varies according to three main factors: problem complexity, number of processors, and communication cost. Each of these factors is dictated by numerous variables. However, acknowledging these factors paves the way for developing an integrated DLB that minimizes their effects in order to attain even higher speedups and efficiencies. Such system can highly improve the performance of current finite element solvers for nonlinear problems.