Resumo em Inglês:

Abstract This study presents an experimental–numerical investigation on one-way geopolymer concrete (GPC) slab strips internally reinforced with steel or GFRP bars and externally strengthened using a glass fabric-reinforced cementitious matrix (FRCM) system. Six full-scale specimens were tested under four-point bending to characterize cracking response, stiffness evolution, strengthening efficiency, and governing failure mechanisms. The steel-reinforced control slab exhibited a ductile flexure-controlled failure. With glass-FRCM strengthening, the ultimate load increased by approximately 31% for one layer and 59% for two layers, accompanied by improved crack distribution and enhanced post-cracking stiffness, while maintaining flexural dominance. In contrast, the GFRP-reinforced control slab failed in shear, highlighting the limited stress redistribution associated with the lower elastic modulus and linear-elastic behavior of GFRP bars. Glass-FRCM strengthening improved tensile stiffness and crack control; however, the strengthened GFRP slabs remained shear-governed, indicating that flexural strengthening alone may be insufficient to prevent premature diagonal cracking. Overall, the results demonstrate that strengthening effectiveness is strongly dependent on the internal reinforcement type, and that additional shear strengthening measures are required for GFRP-reinforced GPC slab systems.

Resumo em Inglês:

Abstract Accurate prediction of occupant injury responses is essential for the effective design and optimization of vehicle occupant restraint systems (ORS). To address the complexity and time-consuming nature of injury prediction in existing occupant restraint system (ORS) design workflows, this study proposes a deep learning-based method tailored for frontal collision scenarios. The proposed method enables rapid injury prediction by simultaneously processing crash waveform signals and ORS parameters as inputs. It enables accurate prediction of time-dependent biomechanical response curves across multiple occupant body regions, effectively capturing the complex, nonlinear interactions between dynamic impact conditions and restraint system characteristics. The proposed method requires only 2.7 seconds to predict the occupant's head acceleration–time curve and thorax compression–time curve, as well as to compute the corresponding injury assessment indicators (C-NCAP 2024). The predicted curves achieve a similarity score of over 0.86, and the accuracy of the selected injury assessment indicators exceeds 0.80. Compared with multi-rigid body simulations, the computational efficiency is improved by 533 times. These results demonstrate the model’s potential for intelligent, data-driven, and time-efficient ORS design.