Ductility characteristics of strain-hardening ultra-high-performance concrete: ANN and empirical approaches

Strain-hardening ultra-high-performance concrete (SHUHPC) is prized for its exceptional ductility and strength, offering diverse applications. Understanding the intricate relationships between fiber constituents, geometric parameters, and the matrix has been challenging. This study employs the Connection Weight Approach (CWA) with two calibrated Artificial Neural Network (ANN) models to elucidate these complexities. This study presents a novel approach by integrating ANN and CWA techniques to explore the complex, non-linear interactions of SHUHPC that enhance the understanding of its mechanical properties and offer insights beyond previous research. The aim is to predict SHUHPC's energy absorption capacity (g) and strain at peak stress (epc) under direct tensile stress. Findings reveal that a 15% increase in the fiber reinforcement index enhances energy absorption, yet excessive levels limit epc. Determined through rigorous testing, optimal mixes include an 8% silica fume dosage, resulting in a notable 12% increase in compressive strength. Deformed steel fibers, particularly twisted variants, significantly boost energy absorption metrics by 18%. Fiber-matrix interactions play a pivotal role in achieving these results. This study clarifies ANN model predictions' ambiguity, offering actionable insights driven by data. These findings advance SHUHPC understanding and propose strategies for its optimized applications.