Durability of reinforced geopolymer concrete using coral aggregates and seawater under accelerated corrosion

2025-09-25T08:26:50+00:00

This study evaluates the corrosion resistance of geopolymer concrete made with coral sand, coral rock, and seawater, materials abundant on offshore islands. Three mixtures were designed using varying proportions of fly ash and blast furnace slag as binders. Compressive strengths after 28 days were 20.11 MPa, 25.32 MPa, and 30.15 MPa for GPS-1, GPS-2, and GPS-3, respectively. Accelerated corrosion tests (NT Build 356) revealed that higher strength samples exhibited lower average current intensity before cracking (5.49 mA for GPS-1 vs. 4.68 mA for GPS-3) and longer times to cracking (204 h vs. 324 h). Chloride ion concentrations near the steel at the onset of cracking were similar across mixtures (0.53 % – 0.58 % by dry concrete weight), but initial chloride contents were lower in high-strength samples (0.08 % in GPS-3 vs. 0.22 % in GPS-1), indicating better resistance to ion penetration. Microstructural analyses (SEM, FTIR, XRD) confirmed that GPS-3 had a denser matrix, more developed geopolymer gel, and reduced presence of unreacted crystalline phases. These results demonstrate that optimizing binder content and microstructure enhances both mechanical performance and corrosion resistance, making geopolymer concrete with marine-derived materials a viable solution for durable coastal infrastructure.

Vehicle and time specific crash modelling on selected rural highway curves using geometric and speed parameters: A transformed linear regression approach

2025-10-28T09:13:52+00:00

This study develops vehicle and time-specific crash rate prediction models for rural highway curves using high-resolution geometric and speed data. A 30km segment of State Highway-1 in Karnataka, India, encompassing 32 horizontal curves, served as the study site. Detailed data collection included 10 years of crash records, traffic volume count, LiDAR-based geometric features, and spot speeds recorded from laser speed cameras. Distinct models were built for motorized two-wheelers (MTW), passenger cars (CAR), heavy commercial vehicles (HCV), and for both daytime and nighttime conditions. The study offers a novel contribution by incorporating nighttime crash rate modelling rarely addressed due to challenges in data availability, and by developing disaggregated models for multiple vehicle classes. A backward stepwise regression (BSR) approach with square root transformation was employed, ensuring model transparency and interpretability. Sight-distance deficiency consistently emerged as the most influential predictor of crash rate, highlighting the critical role of visibility on curved segments. Validation through Leave One Out Cross Validation (LOOCV) confirmed acceptable predictive performance (R² = 0.43-0.80), with residuals exhibiting normal distribution. The findings underscore the importance of curve geometry and visibility in crash risk and provide actionable insights for design audits and safety interventions on rural highways.

Journal of Science and Transport Technology

Durability of reinforced geopolymer concrete using coral aggregates and seawater under accelerated corrosion

Vehicle and time specific crash modelling on selected rural highway curves using geometric and speed parameters: A transformed linear regression approach