Artificial intelligence-driven predictive modeling of multi-functional carbon nanotube infused smart cement for structural reinforcement and real-time damage sensing

Abstract

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 0.1 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.

Graphical abstract

Keywords

smart cement / carbon nanotubes / structural monitoring / self-healing concrete / predictive maintenance modeling

References

[1] An S H , Kim K Y , Chung C W , Lee J U . Development of cement nanocomposites reinforced by carbon nanotube dispersion using superplasticizers. Carbon Letters, 2024, 34(5): 1481–1494

[2] Awol J F , Hu Y G , Hui Y . Modeling the influence of microstructural variations on the Young’s modulus of carbon nanotube-reinforced cement composites. Acta Mechanica, 2025, 236(1): 105–123

[3] Buasiri T , Kothari A , Habermehl-Cwirzen K , Krzeminski L , Cwirzen A . Monitoring temperature and hydration by mortar sensors made of nanomodified Portland cement. Materials and Structures, 2024, 57(1): 1

[4] Chadha V , Singla S . A review on classification and effect of nanoparticles on workability, mechanical properties, durability, and microstructure of cement composites. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2024, 48(5): 3365–3388

[5] Chandran G , Muruganandam L , Biswas R . A review on adsorption of heavy metals from wastewater using carbon nanotube and graphene-based nanomaterials. Environmental Science and Pollution Research International, 2023, 30(51): 110010–110046

[6] El-Feky M S , Badawy A H , Seddik K M , Yahia S . Evaluation of polyester high-tenacity fabric and carbon nanotube reinforcements for improving flexural response in concrete beams. Scientific Reports, 2024, 14(1): 26907

[7] Hamdy T M . Evaluation of compressive strength, surface microhardness, solubility and antimicrobial effect of glass ionomer dental cement reinforced with silver doped carbon nanotube fillers. BMC Oral Health, 2023, 23(1): 777

[8] Jang D , Yang B , Cho G . Effects of electrodes type and design on electrical stability of conductive cement as exposed to various weathering conditions. Carbon Letters, 2024, 34(3): 1015–1020

[9] Kantovitz K R , Carlos N R , Silva I A P S , Braido C , Costa B C , Kitagawa I L , Nociti-Jr F H , Basting R T , de Figueiredo F K P , Lisboa-Filho P N . TiO₂ nanotube-based nanotechnology applied to high-viscosity conventional glass ionomer cement: ultrastructural analyses and physicochemical characterization. Odontology, 2023, 111(4): 916–928

[10] Kumar A , Sinha S . Multiwalled carbon nanotube aided fly ash-based subgrade soil stabilization for low-volume rural roads. International Journal of Geosynthetics and Ground Engineering, 2023, 9(2): 17

[11] Kumar A , Sinha S . Role of multiwalled carbon nanotube in the improvement of compaction and strength characteristics of fly ash stabilized soil. International Journal of Pavement Research and Technology, 2024, 17(4): 868–889

[12] Kumar A , Sinha S . Support vector machine-based prediction of unconfined compressive strength of multi-walled carbon nanotube doped soil-fly ash mixes. Multiscale and Multidisciplinary Modeling, Experiments and Design, 2024, 7(6): 5365–5386

[13] Liu J , Cui B , Pang B . Preparation and properties of magnesium oxysulfide cement based foam board absorbing material. Journal of Wuhan University of Technology. Materials Science Edition, 2024, 39(1): 118–125

[14] Liu Y , Yang Q , Wang Y , Liu S , Huang Y , Zou D , Fan X , Zhai H , Ding Y . Effect of CSH-PCE nanocomposites on early hydration of the ternary binder containing Portland cement, limestone, and calcined coal gangue. Journal of Thermal Analysis and Calorimetry, 2024, 149(22): 12685–12695

[15] Liu B , Vu-Bac N , Zhuang X , Lu W , Fu X , Rabczuk T . Al-DeMat: A web-based expert system platform for computationally expensive models in materials design. Advances in Engineering Software, 2023, 176: 103398

[16] Liu B , Lu W . Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design. International Journal of Hydromechatronics, 2022, 5(4): 336–365

[17] Liu B , Vu-Bac N , Rabczuk T . A stochastic multiscale method for the prediction of the thermal conductivity of polymer nanocomposites through hybrid machine learning algorithms. Composite Structures, 2021, 273: 11426

[18] Liu B , Vu-Bac N , Zhuang X , Fu X , Rabczuk T . Stochastic full-range multiscale modeling of thermal conductivity of polymeric carbon nanotubes composites: A machine learning approach sets. Composite Structures, 2022, 289: 115393

[19] Liu B , Vu-Bac N , Zhuang X , Fu X , Rabczuk T . Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites. Composites Science and Technology, 2022, 224: 109425

[20] Liu B , Lu W , Olofsson T , Zhuang X , Rabczuk T . Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of polymeric graphene-enhanced composites. Composite Structures, 2024, 327: 117601

[21] Liu B , Wang Y , Rabczuk T , Olofsson T . Multi-scale modeling in thermal conductivity of Polyurethane incorporated with phase change materials using physics-informed neural networks. renewable energy, 2024, 220: 119565

[22] Liu B , Vu-Bac N , Zhuang X , Rabczuk T . Stochastic multiscale modeling of heat conductivity of polymeric clay nanocomposites. mechanics of materials, 2020, 142: 103280

[23] Liu B , Penaka S R , Lu W , Feng K , Rebbling A , Olofsson T . Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern Sweden. Technology in Society, 2023, 75: 102347

[24] Mahmoodi M J , Khamehchi M , Safi M . A comprehensive probabilistic prediction and Monte-Carlo simulation of the flexural strength of hybrid graphene oxide/carbon nanotube cementitious nanocomposite. Acta Mechanica, 2023, 234(11): 5819–5839

[25] Matos R A , Nascimento Filho L C , Guilhem I , Freitas V , Moura J , Mesquita E . An electrical modeling approach for analysis of the behavior of carbon nanotubes cement-based composite. Journal of Building Pathology and Rehabilitation, 2023, 8(1): 53

[26] Wei L , Liu G , Qian S , Zhao J W , Jiao G , Zhang G Y . Molecular dynamics study of the mechanical properties of hydrated calcium silicate enhanced by functionalized carbon nanotubes. Journal of Molecular Modeling, 2024, 30(2): 48

[27] Yang S , Bieliatynskyi A , Trachevskyi V , Shao M , Ta M . Research of nano-modified plain cement concrete mixtures and cement-based concrete. International Journal of Concrete Structures and Materials, 2023, 17(1): 50

[28] Yoon H N , Hong W T , Jung J , Park C , Jang D , Yang B . Investigation of freeze–thaw deterioration effects on electrical properties and electric-heating capability of CNT-CF incorporated cement mortar. Carbon Letters, 2024, 34(7): 1949–1959

[29] Zhu Y , Sun M , Li Z , Liu Y , Fang Y . Influence of plasma modified carbon nanotubes on the resistance sensitiveness of cement. Journal of Wuhan University of Technology–Materials Science Edition, 2023, 38(1): 136–140

[30] Vairagade V S , Dhale S A , Joshi K V , Waje M G . Leveraging an integrated multivariate analytical approach towards strength enhancement of fly ash-based concrete. Multiscale and Multidisciplinary Modeling, Experiments and Design, 2025, 8(1): 127

[31] Vairagade V S , Bahoria B V , Isleem H F , Shelke N , Mungle N P . Strength and durability predictions of ternary blended nano-engineered high-performance concrete: Application of hybrid machine learning techniques with bio-inspired optimization. Engineering Applications of Artificial Intelligence, 2025, 148: 110470

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