Material-Aware BIM-FEA Integration for Performance-Based Assessment of Advanced Construction Materials

Abstract

Although emerging cementitious and fiber-reinforced composite materials offer enhanced strength and durability, their nonlinear, rate-dependent, and heterogeneous behavior is not consistently transferred into analysis-ready representations in conventional building information modeling–finite-element analysis (BIM-FEA) workflows. This study introduces a material-informed framework that integrates BIM geometry with a Python-assisted (version 3.11) automated building information modeling-finite-element analysis integration engine (ABFIE) coupled to nonlinear ANSYS (version 2025 R1) simulation, enabling direct incorporation of experimentally calibrated constitutive data into finite-element models. Within the validation cases considered here, ABFIE reproduces stiffness degradation, neutral-axis migration, and crack-initiation loads with prediction errors of 5%–9% relative to reported experimental benchmarks while reducing model-preparation time by more than 60% for the benchmark workflow and showing reduced operator-to-operator variation under the tested preprocessing settings. Supplementary ANSYS checks indicate stable mesh behavior across the 50–25-mm benchmark range, with peak-load variation below 3% once the critical-region element size reaches approximately 20–25 mm. These results suggest that a material-aware BIM-FEA workflow can improve predictive consistency and modeling efficiency for performance-based assessment of advanced construction materials within the tested validation scope.