The increasing demands of this modern infrastructure require greater structural performance and long-term sustainability while being cost-effective.For a long time,the quest for such construction materials required du...The increasing demands of this modern infrastructure require greater structural performance and long-term sustainability while being cost-effective.For a long time,the quest for such construction materials required durable,intelligent,and cost-effective construction materials.The traditional cementitious materials are very common;however,they have some innate drawbacks:they crack rather easily,cannot self-heal,and lack some damage-monitoring mechanisms for its real-time assessment.Current solutions for structural health monitoring involve extrinsic sensors and wiring that are invasive and costly and do not provide integrated self-healing and damage detection predictivity.This research introduces the work on multi-functional carbon nanotube(CNT)infused smart cement capable of presenting enhanced mechanical performances,in situ damage sensing,and autonomous self-healing capabilities.Key methods used include:1)chemical functionalization of CNT for better dispersion,bonding,and conductivity,which improves mechanical strength by 30% and electrical conductivity 10-fold;2)CNT catalyzing microencapsulated self-healing system:more than 85%crack closure efficiency for cracks up to 0.5 mm;3)three-dimensional printing with CNT infused cement,enabling the creation of complex geometries with embedded sensors,porosity control,and 20%greater structural integrity;4)wireless damage monitoring using CNT-based antennas for crack detection below O.1 mm and signal transmission over 50 m;and 5)artificial intelligence(AI)-enhanced predictive maintenance,achieving a prediction accuracy of 95%-98%in crack propagation and reducing maintenance costs by 30%.This novel integration of functionalized CNT,self-healing agents,wireless sensing,and AI-driven analytics simultaneously strengthens structural integrity while permitting sustainable,non-invasive,and scalable monitoring.What these results indicate is enhanced performance,cost-effectiveness,and longevity,making the technology transformative for the next generations of construction materials.展开更多
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The increasing demands of this modern infrastructure require greater structural performance and long-term sustainability while being cost-effective.For a long time,the quest for such construction materials required durable,intelligent,and cost-effective construction materials.The traditional cementitious materials are very common;however,they have some innate drawbacks:they crack rather easily,cannot self-heal,and lack some damage-monitoring mechanisms for its real-time assessment.Current solutions for structural health monitoring involve extrinsic sensors and wiring that are invasive and costly and do not provide integrated self-healing and damage detection predictivity.This research introduces the work on multi-functional carbon nanotube(CNT)infused smart cement capable of presenting enhanced mechanical performances,in situ damage sensing,and autonomous self-healing capabilities.Key methods used include:1)chemical functionalization of CNT for better dispersion,bonding,and conductivity,which improves mechanical strength by 30% and electrical conductivity 10-fold;2)CNT catalyzing microencapsulated self-healing system:more than 85%crack closure efficiency for cracks up to 0.5 mm;3)three-dimensional printing with CNT infused cement,enabling the creation of complex geometries with embedded sensors,porosity control,and 20%greater structural integrity;4)wireless damage monitoring using CNT-based antennas for crack detection below O.1 mm and signal transmission over 50 m;and 5)artificial intelligence(AI)-enhanced predictive maintenance,achieving a prediction accuracy of 95%-98%in crack propagation and reducing maintenance costs by 30%.This novel integration of functionalized CNT,self-healing agents,wireless sensing,and AI-driven analytics simultaneously strengthens structural integrity while permitting sustainable,non-invasive,and scalable monitoring.What these results indicate is enhanced performance,cost-effectiveness,and longevity,making the technology transformative for the next generations of construction materials.
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.
