🌊 Machine Learning-Driven Optimization of 3D-Printed Fiber-Reinforced Concrete for Sustainable Marine Infrastructure
The rising need for sustainable and resilient marine infrastructure has accelerated the adoption of advanced fabrication technologies like 3D printing. Among these, 3D-printed fiber-reinforced concrete (3DPFRC) has emerged as a transformative solution for developing complex and durable marine structures such as sea walls, breakwaters, and underwater pipelines. This innovation combines precision design with environmental responsibility, significantly reducing material waste and production time. However, ensuring both high mechanical performance and low carbon emissions in 3DPFRC remains a pressing research challenge that requires interdisciplinary approaches integrating material science, artificial intelligence, and environmental engineering.
Significance of 3DPFRC in Marine Environments
Marine environments pose extreme challenges such as corrosion, pressure fluctuations, and high salinity, demanding materials with superior strength and durability. 3DPFRC addresses these issues by enhancing the internal structure of concrete through fiber reinforcement and layer-wise fabrication. The adaptability of 3DPFRC also supports complex geometries essential for coastal defense systems and floating structures. This technology not only offers mechanical resilience but also aligns with global goals for sustainable coastal development, minimizing material wastage and on-site labor requirements.
Machine Learning Integration in Material Design
The complexity of optimizing mix proportions in 3DPFRC makes traditional trial-and-error approaches inefficient. Machine learning (ML) models provide a powerful alternative by identifying nonlinear relationships between mix components and performance outcomes. The integration of hybrid ML algorithms—such as CNN-LSTM, RA-PSO, XGBoost-PSO, and SVM-PSO—enables precise prediction of both compressive strength and CO₂ emissions. These models assist researchers in designing eco-efficient concretes tailored to specific performance and environmental criteria, advancing the digitalization of construction materials research.
RA-PSO: A Superior Predictive Model
Among the evaluated models, Randomized Adaptive Particle Swarm Optimization (RA-PSO) demonstrated outstanding predictive performance. Its adaptive parameter tuning and randomized search mechanisms enhance convergence speed and solution diversity, allowing it to effectively capture complex interactions among 3DPFRC mix parameters. With high R² values (0.9819 training and 0.9674 testing for compressive strength), RA-PSO proved to be a robust and reliable tool for dual optimization of mechanical and environmental performance in sustainable construction materials.
Sensitivity Analysis and Key Influencing Parameters
Sensitivity analysis conducted on the RA-PSO model identified water content (34%), silica fume (30%), and water-to-binder ratio (23%) as the most influential factors affecting compressive strength. These parameters significantly impact the hydration process, porosity, and overall structural integrity of the printed layers. Understanding such relationships is vital for developing optimized mix designs that ensure both high strength and low environmental impact, paving the way for standardized 3DPFRC formulations in marine infrastructure projects.
Future Prospects and Sustainable Applications
The findings of this research underscore the potential of machine learning-based optimization in transforming 3D printing applications for marine and civil infrastructure. Future studies can extend this approach to include durability, chloride resistance, and lifecycle analysis. Moreover, the integration of AI-driven GUIs ensures accessibility for engineers, promoting real-world implementation. By merging digital intelligence with sustainable material design, this framework supports the global transition toward carbon-neutral, resilient, and adaptive marine construction systems.
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