This study presents a multi-scale deep-learning framework that integrates several advanced neural models to optimize hybrid three dimensional(3D)printed self-sensing nano-carbon cementitious composites.The first step ...This study presents a multi-scale deep-learning framework that integrates several advanced neural models to optimize hybrid three dimensional(3D)printed self-sensing nano-carbon cementitious composites.The first step was undertaken by Multi-Scale Graph Neural Network,where special conductive pathways were taught ensuring the uniform work on nano-carbon learning patterns,improving electrical conductivity by 25%–35%.four-dimensional Spatiotemporal Transformer Network decoded printing parameters achievements with an interlayer conductivity improvement of 40%–50%,avoiding anisotropic print by aiming for defects prediction on print Induced anisotropic behavior.High-fidelity artificial microstructures have been generated with Physics Informed Generative Adversarial Networks;these synthetic methods realize an experimental cost-cutting of about 50%while conserving about 98%fidelity to the characteristics of real microstructures.Fifth,Self-Supervised Contrastive Learning automatically classifies small macro and microdefects with over 95%detection reliability.There has been reduction of as much as 35%in the number of false positives.Predicted kinetics of hydration and long-term electrical stability can now be predicted with speed improvements of 15%and resistance drift reduction by 20%over six months.This approach for the first time combines different hybrid models of deep learning for the self-sensing cementitious composites,thus significantly increasing percolation of electrical networks,accuracy in crack detection,and predictions on long-term durability.The developed framework creates a new paradigm in the real-time structural health monitoring world,providing enhanced reliability in structures while also reducing costs at a level for the next generation of smart infrastructure sets.展开更多
文摘This study presents a multi-scale deep-learning framework that integrates several advanced neural models to optimize hybrid three dimensional(3D)printed self-sensing nano-carbon cementitious composites.The first step was undertaken by Multi-Scale Graph Neural Network,where special conductive pathways were taught ensuring the uniform work on nano-carbon learning patterns,improving electrical conductivity by 25%–35%.four-dimensional Spatiotemporal Transformer Network decoded printing parameters achievements with an interlayer conductivity improvement of 40%–50%,avoiding anisotropic print by aiming for defects prediction on print Induced anisotropic behavior.High-fidelity artificial microstructures have been generated with Physics Informed Generative Adversarial Networks;these synthetic methods realize an experimental cost-cutting of about 50%while conserving about 98%fidelity to the characteristics of real microstructures.Fifth,Self-Supervised Contrastive Learning automatically classifies small macro and microdefects with over 95%detection reliability.There has been reduction of as much as 35%in the number of false positives.Predicted kinetics of hydration and long-term electrical stability can now be predicted with speed improvements of 15%and resistance drift reduction by 20%over six months.This approach for the first time combines different hybrid models of deep learning for the self-sensing cementitious composites,thus significantly increasing percolation of electrical networks,accuracy in crack detection,and predictions on long-term durability.The developed framework creates a new paradigm in the real-time structural health monitoring world,providing enhanced reliability in structures while also reducing costs at a level for the next generation of smart infrastructure sets.
