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

1 Introduction

Advanced materials are needed to meet the growing needs for resilient, sustainable, and intelligent infrastructure [1,2]. This means there is an added need to meet the structural degradation and real-time monitoring issues simultaneously [3]. Even though cement-based materials are at the basis of modern construction, they happen to be intrinsically brittle, microcrack-prone, and lack any inherent mechanisms for damage repair or early warning signs of failure [4–6]. Conventional techniques using external mounted sensors along with wired systems are either non-scalable, inaccurate, or not strong enough to tolerate harsh environmental conditions [7,8]. These issues demand an integration material solution, improvement in structural capacity, and support for real time, non-invasive damage assessment and self-healing capability [9–11]. It has been observed that the outstanding mechanical, electrical, and thermal properties make carbon nanotube (CNT) one of the promising materials to enhance cementitious systems [12]. By its nanoscale structures, it provides effective load transfer, bridging within the cracks of cement matrix besides the improvement in the conductivity. However, the major challenge in the effective exploitation of CNT in cement is dispersion challenges, weak interfacial bonding, and agglomeration behavior [13–16]. These factors have largely limited the scope of CNT in real-world applications in construction, which necessitates innovation in the process of functionalization and integration [17]. However, although these concepts of self-healing systems, wireless monitoring, and artificial intelligence (AI)-based predictive models have potential separately in construction, their overall integrated application in a holistic material framework is unknown [18–20]. A new multi-functional CNT infused smart cement will be proposed that fills in the existing gap [21]. Utilizing chemically functionalized carbon nanotube (f-CNT), microencapsulated self-healing agents, wireless damage monitoring systems, and AI-driven analytics will result in enhanced mechanical reinforcement along with autonomous crack healing and real-time structural heath monitoring capabilities in the material [22–24]. This solution is a holistic one, providing durability, cost efficiency, and sustainable performance levels for modern infrastructure sets.

This work is motivated by the growing limitations of traditional cement-based systems in responding to multifaceted demands of modern infrastructure. Traditional cementitious materials do not have the intrinsic abilities to sense or repair damage, which turns into expensive and often delayed efforts in maintenance. Even though some researchers used external sensors and wired structural heath monitoring systems, such systems are usually intrusive, expensive, and susceptible to failure under extreme environments. Similarly, all the existing self-healing technologies are promising but not scalable and integrable with real-time monitoring frameworks. Such gaps do not allow intelligent construction materials to strengthen structures and self-repair their health in an autonomous fashion. This work bridges the gaps by developing a multi-functional material that integrates the best of CNT-based reinforcement, autonomous healing, and state-of-the-art structural heath monitoring technologies. This work represents a holistic framework for smart cement that addresses critical challenges in dispersion, interfacial bonding, and system integration. In this work, chemically f-CNT enhances the material’s mechanical performance and conductivity. Catalyzed by microencapsulated healing agents with CNT, the rapid crack closure guarantees long-term durability. High-performance three-dimensional (3D) printing provides accuracy in manufacturing with controllable porosity; CNT-based wireless antenna systems allow the external wiring to be avoided which thus means less expensive setup cost and scalable to a higher level, while AI-enhanced predictive models transform real-time sensor data into actionable insights that may lead to proactive maintenance by eliminating catastrophic failures. Simply put, all these aspects are an integrated approach toward new construction materials that provide significant strength, sustainability, and efficiency. This research deals with long-standing challenges and offers a basis for future smart infrastructure systems.

2 Review of existing models for cement strength enhancement and analysis

This gradual incorporation of CNTs into cementitious materials is indeed a major milestone in research involving construction materials, especially in the area of developing performance-oriented nanocomposites for civil infrastructure sets. A starting point would be the investigation of the dispersion mechanisms of this CNT in cementitious matrices. An et al. [1] had sought a significant potential solution by the incorporation of superplasticizers for the effective dispersion of CNT, leading to impressive mechanical behavior in cement nanocomposites. It serves as a foundation to understand what is meant by interfacial interaction in this sense-an interfacial interaction among CNT and cement matrix sets.

At this stage, attention would now be placed on computational modeling of macroscopic mechanical behavior. Awol et al. [2] introduced a modeling approach that correlates microstructural heterogeneity to the effective Young’s modulus of CNT-reinforced cement, thus, providing a defensive prediction of mechanical performance. This is an important aspect of analytical modeling, which is quite essential in design-stage evaluation. Supporting the analysis, Buasiri et al. [3] worked on nanomodified cement sensors for real-time monitoring of hydration and temperature changes. Their studies reveal how the CNT serve both as mechanical enhancers as well as functional sensors.

Chadha and Singla [4] joined in writing a detailed review covering the types of nanoparticles discussed so far and how they improve mechanical, durability, and microstructural properties. This, in fact, brings forth the comprehensive benefits of nano-reinforcement, particularly with respect to microstructural densification. At the same time, Chandran et al. [5] evaluated the role of CNT and graphene-based materials in heavy metal adsorption from wastewater to put such input into a more global environmental frame. Even though not directly applying to cement composites, they nonetheless underline the versatility and environmental utility of carbon nanomaterials. Tab.1 shows the review of existing literature.

The extension to structural application with regard to CNT is made by El-Feky et al. [6] through the assessment of polyester fabric and CNT as hybrid reinforcements in concrete beams. This study seems to show improvements to flexural response and, therefore, suggests promising hybrid combinations. Similarly, Hamdy [7] recognized the use of CNT in dental cements in a biomedical context. Although in entirely different applications, the behavior in strength and microhardness certainly reflects the general performance increase given by CNT.

The multifunctional behavior of CNT is further analyzed by Jang et al. [8], who also examined the comparative performances of different electrode configurations on the stability of conductive cement composites under environmental exposure. The results make it evident as to what extent design parameters influence long-term durability. Evaluating the applicability of nanotubes in glass ionomer cements within dental applications is reported by Kantovitz et al. [9] to optimize cross-applicability between various types of nanotubes.

Kumar and Sinha made several contributions [10–12] relating to the applications of CNT in geotechnical engineering, especially fly ash-based soil stabilization.

A broad array of investigations was presented by Liu et al. [13–23] exploring nanomaterial effects, multiscale modeling, and surrogate machine learning approaches. The study on magnesium oxysulfide cement [13] widens the alternative dimensions to replace Portland cement and include nanomodified binders. Nanocomposites were then evaluated by Liu et al. [14] on the early hydration part in blended systems, known as a critical point in accelerating construction dates. The subsequent effort of the team [15–22] will now pivot toward developing computationally efficient and probabilistic modeling frameworks. These efforts capture variability within the microstructure and behavior while breaching experimental limitations made with their predictions based on data. Interpretable machine learning for graphene-enhanced composites captures the emerging focus on explaining artificial intelligence-integrated material modeling [20].

Mahmoodi et al. [24] performed probabilistic Monte Carlo predictions on flexural strength in CNT/graphene oxide hybrids, which captured the building computational to as well as uncertainty quantification. Matos et al. [25] continued this computational emphasis by presenting an electrical model for CNT-based cement composites, which would further understand and analyze conductivity behavior for complex matrices.

Wei et al. [26] performed molecular dynamics simulations to capture atomistic interactions inside hydrated calcium silicate matrices that were enhanced with f-CNT. Their insights into interfacial bonding mechanisms feed important input into coarse-grained models. In experimental domains, Yang et al. [27] studied nano-modified plain cement concrete and the benefits of using nanoscale additives with regular concrete mixes. Yoon et al. [28] studied the deterioration in CNT cement mortars after freeze–thaw cycles, giving indication on applications for the severe climates with an energy efficiency potential via electrical heating. Zhu et al. [29] investigated plasma-modified CNT in relation with improving the resistance sensitivity of cement composites: here, surface functionalization was shown to optimize sensing performance for cementitious systems, and therefore highlights one of the greatest advances in smart infrastructure sets.

3 An integrated model for multiple functional carbon nanotube infused smart cement

To overcome issues of low efficiency and high complexity present in existing methods, the section is discussing design of an integrated model for multiple functional CNT infused smart cement for structural reinforcement and real-time damage sensing operations. First, as per Fig.1, the chemical functionalization of CNTs is of utmost importance to improve the dispersion, interfacial bonding, and compatibility of CNTs within the cementitious matrix. Pristine CNT has a tendency to agglomerate due to van der Waals forces and significantly reduce their reinforcing and sensing capabilities. Functionalization through chemical oxidation introduces polar groups, such as carboxylic and amine, onto the CNT surfaces. The process is modeled using reaction kinetics, where the functional group coverage θ over temporal instances is given via Eq. (1):

(1)\frac{\mathrm{d}\theta }{\mathrm{d}t}=k_1\times C_{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{n}\mathrm{t}}\times \left(1-\theta \right)-k_2\times \theta ,

where k₁ and k₂ are forward and backward reaction rate constants, and C_reagent is the concentration of the oxidizing agents. The functionalization optimized the bonding between CNT and calcium silicate hydrate (C-S-H), which is quantified using the work of adhesion Wa via Eq. (2):

(2)Wa={\gamma }_{\mathrm{C}\mathrm{N}\mathrm{T}}+{\gamma }_{\mathrm{C}-\mathrm{S}-\mathrm{H}}-{\gamma }_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{e}},

where γ values represent surface energies. The improvement in dispersion is analyzed using the zeta potential ζ via Eq. (3):

(3)\zeta =\eta \times \frac{V}{\epsilon },

where η is the viscosity, V is the electrophoretic mobility, and ε is the permittivity sets. A more negative ζ value confirms improved dispersion stability that matches with the enhancement of compressive strength (σ_c) via Eq. (4).

(4){\sigma }_{\mathrm{c}}={\sigma }_{\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{x}}+{\eta }_{\mathrm{C}\mathrm{N}\mathrm{T}}\times V_{\mathrm{C}\mathrm{N}\mathrm{T}}\times \frac{\mathrm{\Delta }\sigma }{\mathrm{\Delta }\epsilon },

where η_CNT is the reinforcement efficiency, V_CNT is the CNT volume fraction, and Δσ/Δε is the stress–strain enhancement ratio sets.

This system iteratively uses self-healing properties via the CNT-catalyzed microencapsulation mechanisms. This when combined with the healing agent released through localized heating or electrical stimulation operations. Via Eq. (5) the model depicts the modeling of Joule heating, which generates heat within networks of CNT.

(5)Q=I^2\times R\times \mathrm{\Delta }t,

where Q is the heat generated, I is the current, R is the resistance, and Δt is the time interval sets. This heat is used to activate the microcapsules, which in turn assists in releasing healing agents like calcium carbonate or polymeric epoxy sets. The diffusion of the healing agent through cracks is governed by Fick’s second law via Eq. (6):

(6)\frac{\mathrm{\partial }C}{\mathrm{\partial }t}=D{\mathrm{\nabla }}^{2}C,

where C is the healing agent concentration, t is timestamp, and D is the diffusion coefficient sets. Healing efficiency (H) is evaluated using the crack closure ratio via Eq. (7):

(7)H=\left(\frac{A_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{d}}}{A_{\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l}}}\right)\times 100,

where A_healed and A_initial are the healed and initial crack areas, respectively in the process. For cracks up to 0.5 mm, H exceeds 85%, with a recovery of 80% in mechanical strength levels. Iteratively, next as per Fig.2, in 3D printing, the rheological behavior of CNT infused cement paste is crucial for achieving printable consistency levels.

The shear-thinning property is expressed by the power-law model via Eq. (8):

(8)\eta =K\times {\dot{\gamma }}^{n-1},

where η is the apparent viscosity, K is the consistency index, \dot{\gamma } is the shear rate, and n the flow behavior index (n < 1 for shear-thinning fluids) in the process. The structural integrity of printed layers is analyzed using a stress–strain relationship via Eq. (9):

(9)\sigma =E\times \epsilon +\frac{2G}{3}\times \epsilon ,

where σ is the stress, E is the elastic modulus, G is the shear modulus, and ε the strain for this process. Electrical conductivity in printed structures is evaluated by the percolation threshold model via Eq. (10):

(10){\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}={\sigma }_{\mathrm{C}\mathrm{N}\mathrm{T}}\times \frac{\varphi -{\varphi }_{\mathrm{c}}}{{\varphi }_{{\mathrm{c}}^t}},

where σ_eff is the effective conductivity, ϕ is the CNT volume fraction, ϕ_c is the percolation threshold, and t is the critical exponent for this process. This model validates a 20% improvement in structural integrity and anisotropic sensing. Iteratively, next according to Fig.3, wireless damage monitoring uses CNT-based antennas incorporated in cement for the identification of microcracks. The frequency of resonance fr of the antenna depends upon crack-induced alterations in its inductance L and capacitance C via Eq. (11):

(11)fr=\frac{1}{2\pi \sqrt{L\times C}}.

The impedance (Z) variation caused by crack propagation is represented via Eq. (12):

(12)Z=R+\mathrm{j}\omega L,

where R is resistance, ω is the angular frequency, and j is the imaginary unit in this process. The system achieves > 95% signal transmission efficiency and a sensitivity of 0.05 mm for crack detection operations. Next, as per Fig.3, AI-enhanced predictive maintenance processes data from CNT sensors to model crack propagation operations. The Paris’ law for crack growth is integrated with time-dependent stress cycles via Eq. (13):

(13)\mathrm{\Delta }K=\pi a\sigma \beta ,

where a is the crack length, σ is the applied stress, and β is a geometric factor for this process. Crack growth rate (da/dt) over temporal instances sets is evaluated via Eq. (14):

(14)\frac{\mathrm{d}a}{\mathrm{d}t}=C\times {\mathrm{\Delta }K}^m.

Integrating this from initial crack size (a₀) to critical crack size (a_c) gives the failure time (tf) via Eq. (15).

(15)tf={\int }_{a_0}^{a_{\mathrm{c}}}\left(\frac{1}{C}\times {\mathrm{\Delta }K}^m\right)\mathrm{d}a.

This AI-driven approach predicts failure with 95%–98% accuracy, reducing maintenance costs by 30%. The synergy among these methods ensures enhanced structural performance, autonomous crack healing, real-time monitoring, and predictive maintenance, transforming traditional cementitious materials into self-sufficient smart systems.

Each component complements the others, thus giving a strong and scalable solution for modern infrastructure sets. Finally, we discuss the efficiency of the proposed model in terms of different metrics and compare it with existing models under different scenarios.

4 Comparative result analysis

This data setup for this study aimed at incorporating chemical functionalization of CNTs, their dispersion in the cementitious matrix, and their mechanical, electrical, and self-healing properties of the composite material before testing. The pristine CNT was chemically functionalized with a concentrated nitric-sulfuric acid mixture of 70:30 v/v to introduce carboxyl (-COOH) groups that enhance dispersion as well as interfacial bonding within the cement matrix sets. The functionalization process was conducted under controlled conditions at a temperature of 120 °C for 4 h with constant stirring. Then, after washing properly, it was dried at 80 °C for 24 h. The f-CNT was dispersed in deionized water using PCE as a superplasticizer in a concentration of 0.5% by weight of cement, to ensure suspension stability. This suspension was ultrasonicated at 40 kHz for 60 min to attain uniform dispersion. Portland cement was the binder, and water-to-cement ratios of 0.4 and 0.5 were chosen to assess mechanical and electrical performance under various conditions. The specimens were prepared by mixing f-CNT suspensions with cement paste in a high-shear mixer at 2000 r/min for 10 min. Cast and cured samples, in cylindrical (50 mm diameter, 100 mm height) and prismatic shapes (40 mm × 40 mm × 160 mm), in a controlled environment humidity chamber at 95% relative humidity and 23 °C for 28 d for the process. The data sets used in this work are the publicly available Concrete Compressive Strength Data set from the Machine Learning Repository and experimental data sets conducted in the laboratory to capture the specific behavior of CNT. The Concrete Compressive Strength Data set contains 1030 samples with eight input variables that describe the proportions of cement, water, fine aggregate, coarse aggregate, fly ash, superplasticizer, and age of the sample in days. These are the variables used to project the compressive strength in megapascals in concrete. For the sets of experimental data, parameters are: functionalized CNT contents from 0.1 to 1 per cent (by weight of cement) and water-to-cement ratios of 0.4 and 0.5. The microcrack width is varied between 0.05 and 0.5 mm as well to determine their roles in mechanical strength, electrical resistivity, and self-healing efficiency. The additional features included real-time electrical resistance measurements, strain data, and environmental conditions recorded during accelerated durability tests conducted at 20–50 °C with 60%–95% humidity. These sets of comprehensive data formed a rich platform for training the AI-enriched predictive models and validating experimental results against various real-life scenarios encountered in structural monitoring and maintenance operations.

For electrical and self-healing evaluations, controlled microcracks were induced in the prismatic samples using a three-point bending test with a loading rate of 0.5 mm/min. Self-healing agents, micro capsulated polymeric epoxy in silica shells, were mixed at a ratio of 5% by weight of cement in the matrix. During testing, electrical conductivity and crack sensitivity were measured using a four-probe setup with a digital multimeter capable of recording resistance changes in the range of 0.01 to 10 Ω. In wireless damage monitoring experiments, specimens are embedded with CNT-based antennas to transmit data to a receiver placed 20 to 50 m away. Analysis of the transmitted signals helps in detecting microcracks of up to 0.05 mm. This includes the training of an AI-based predictive model with real-time resistance data and historical crack propagation data sets under various environmental stress factors like temperature and humidity sets. The data sets will contain 1000 samples produced at various conditions of stress, temperatures, and load to mimic field applications. For example, compressive strength values in the data range from 40 to 60 MPa, electrical resistivity from 10² to 10³ Ω·m, and crack widths from 0.05 to 0.5 mm. Predictive model validation was performed at 95% accuracy with the capability of forecasting crack propagation and proposing proactive repair schedules. These highly controlled experiments ensured that material properties and sensor performance were systematically evaluated and optimized for next-generation smart construction applications. Results from the experiments clearly show that the proposed CNT Infused cement composite has major advantages over traditional systems in enhancing mechanical performance, electrical sensitivity, self-healing capability, freeze–thaw durability, wireless damage monitoring, and predictive maintenance accuracy. A comparative analysis of the three existing methods of plasma-modified CNT dispersion [29], CNT-superplasticizer dispersion [2], and Monte Carlo-based hybrid graphene oxide/CNT composites [24] presents the better performance of the suggested model. Detailed results have been presented and discussed below in the tables. Further discussions are also included based on the implications and impacts of the findings. It then compared compressive strength improvements for a CNT of 0.5.% wt in cement by an appropriate water-to-cement ratio at 0.4 on the iterative Tab.2 as follows.

The compressive strength of the proposed model is 65 MPa, which surpasses the comparative methods. It, therefore, indicates an effective 30% improvement over plain cement mixture sets. This enhancement is achieved due to the superior dispersion and bonding of f-CNT in the cement matrix, which assists in leading to improved load transfer sets. Plasma modification [29] achieved a notable strength of 58 MPa, but its lower bonding efficiency limits further improvement. The results prove the possibility of using the model for applications in high-strength infrastructure, with a decrease in structural material usage while enhancing the safety margins. Tab.3 is used to check the electrical resistivity and crack sensitivity for 0.5% CNT contents.

The proposed model shows the lowest resistivity at 100 Ω·m, with the highest crack sensitivity that could detect cracks as small as 0.05 mm at a 95% signal variation, showing better electrical properties. These results indicate that the suggested composite is viable for high sensitivity, real-time structural monitoring. In the plasma-modified method [29], performance was good, but modification was a process that required multiple steps to be done and, thus, could be cumbersome to execute. Early detection of structural damage enables prompt repairs to reduce long-term maintenance costs. Tab.4 iteratively compares the crack-healing efficiency and the time needed to heal cracks of 0.3 mm width sets.

The proposed model demonstrates a better self-healing efficiency of 90% with healing time reduced to 36 h, compared to 80% and 48 h for the plasma-modified method [29]. This is because the CNT’ thermal or electrical triggers are effective in activating the healing agents encapsulated in microcapsules. Due to faster healing times and a higher efficiency, the composite proposed here is especially beneficial for dynamic environments where faster structural restoration is a major necessity level. Tab.5 iteratively evaluates performance up to 300 freeze–thaw cycles in terms of retained strength and electrical stability sets.

The proposed model retains 85% of its original strength and electrical stability of 92% after 300 cycles, as compared to the other methods. The high freeze–thaw durability will be imperative for applications within extreme fluctuations of temperature and reduce more frequent repairs, while the longer lifespan of the structures increases in process. Tab.6 presents the range and crack detection accuracy of wireless monitoring systems.

The proposed model achieves a 95% crack detection accuracy over a 50 m range, thus excelling in wireless damage monitoring. This capability is crucial for large-scale structures, enabling cost-effective and non-invasive structural health monitoring systems. The Tab.7 evaluates the predictive accuracy of AI models and their potential for reducing maintenance costs.

The proposed model has a predictive accuracy of 96% and reduces the maintenance cost by 30%, thus it is quite efficient in terms of integrating AI with CNT-based monitoring systems. The precise prediction of damage progression enables proactive repair strategies, thus reducing structural failures and operational issues in process. The results in Fig.6−Fig.7 indicate that the model developed here consistently outperformed existing methods in all parameters of criticality; hence, it has great potential in next-generation smart construction materials of enhanced durability, sustainability, and cost efficiency. Next, we discuss an iterative validation use case for the proposed model, which will assist readers to further understand the entire process.

4.1 Validation use case scenario analysis

To demonstrate the integrated development and evaluation of the suggested CNT Infused cement composite, a sample application use case is created with sample values and data defined. These outputs represent the step-by-step integration and optimization of CNT functionalities in the cement matrix toward superior mechanical, electrical, and self-healing properties, besides advanced structural health monitoring capabilities. The practical use case analysis validation samples were based on the Concrete Compressive Strength Data set from the UCI Machine Learning Repository supplemented with experimental data collected from controlled laboratory studies. The UCI data set contained 1030 samples of variables like cement content (range: 102–540 kg/m³), water-to-cement ratios (0.36–0.65), fine and coarse aggregate contents, and curing age (1–365 d), which gave an all-around assessment on compressive strength measured in MPa. Combining established data sets with experimental data sets ensured the proper validation of the proposed CNT Infused cement composite and helped make comparisons with existing methods, thus bringing in reliable insights into performance metrics.

As per Tab.8, the chemical functionalization step resulted in 85% functional group coverage on CNT, which significantly improved the dispersion stability of CNT within the cement matrix as a shift in zeta potential from −35 to −45 mV sets. These outputs confirm that f-CNT show better compatibility with the cementitious matrix, which is a critical requirement to achieve superior mechanical and electrical performance.

As per Tab.9, step by step the self-healing system presented high efficiency toward all widths of the tested cracks. For crack width of 0.3 mm healing efficiency is shown to be 90%. The activation energy was at about 42 kJ/mol. The experimentations proved the idea of CNT as an activating agent toward healing agents successfully, allowing for almost entire closure of a crack in not more than 36 h for the described process.

As per Tab.10, the 3D printing process exhibited optimal printability to achieve a compressive strength of 65 MPa sets for a CNT content of 0.5%. The viscosity and the yield stress were well-matched to allow for extrudability and structural integrity of the printed layers, thus validating the approach to the proposed material for this complex architectural design process.

As per Tab.11, it has shown that the wireless damage monitoring system gained its utmost detection accuracy for cracks up to 50 m distance by 95%. This sensitivity as small as 0.05mm along with strong signal strength of −35 dBm depicts the functionality of a CNT-based antenna for efficient non-invasive process of structural health monitoring operations.

Tab.12 as indicated above, the predictive model of maintenance using AI exhibited better results. In a data set consisting of 1000 samples, it showed predictive accuracy at 96%, and with that, maintenance cost reduced by 30%, thus highlighting the potential system in the possibility of allowing proactive intervention and the prevention of structural failures.

Iteratively, as outlined in Tab.13, final outputs present the holistic performance benefit of the proposed CNT-infused cement composites. The material demonstrates superior mechanical, self-healing, and monitoring capabilities with improvements ranging from 13% in predictive accuracy to 21% in freeze–thaw retention over the benchmark methods. Therefore, the results place the material among the robust and multifunctional solutions for modern applications in infrastructure deployments.

4.2 Artificial Intelligence model type and training details

The predictive maintenance framework is developed in an ensemble approach comprising the SVMs, Artificial Neural Networks (ANNs), and Gradient Boosting models to enhance robustness and generalization even in varying conditions. These models were trained on a data set of 1000 structured samples, with input variables being CNT concentration; water-to-cement ratio; crack width; electrical resistivity; and temperature and humidity as environmental parameters. The training process comprised 5 folds cross-validation to accommodate generalization, and model assessment metrics included accuracy (96%), F1 score (0.94), precision (0.95), recall (0.93), and RMSE (3.5 MPa for strength prediction), assuring performance forecast quality both in classification and regressions.

4.3 Data set description and evaluation metrics

While the two data sets used for carrying out the training of the model included the UCI Concrete Compressive Strength Data set, the second was an experimental data created by carefully controlled laboratory studies testing different concentrations of CNT from 0.1% to 1% by weight, and widths of crack from 0.05 to 0.5 mm. The models were further evaluated in terms of mean absolute error, area under the receiver operating characteristic curve and confusion matrices in conjunction with accuracy toward quantifying prediction reliability under different operational stressors for comprehensive validation beyond that of simply classification performance.

4.4 Chemical functionalization procedure

CNTs were chemically functionalized by acid oxidation in nitric acid and sulfuric acid mixture in proportions of 70:30 volume ratio and were boiled for 4 h at 120 °C under vigorous stirring. This introduces carboxylic and hydroxyl groups on the surface of CNT, increasing surface energy and contributing to improved dispersion stability. Post functionalization, there is a shift in zeta potential from −35 to −45 mV indicating improved stability in the colloidal medium sets. These functional groups also contribute toward developing better bonding and hence load transfer efficiency between silicate hydrate phases, resulting in a 10-fold increase in electrical conductivity.

4.5 Microencapsulation and self-healing mechanism

The self-healing system was silica-based microcapsules that contained polymeric epoxy as a healing agent, which were embedded in the cement-matrix at 5% weight of cement. Healing was induced thermally by localized Joule heating through CNT networks, heating the temperature high enough to rupture the capsules. This process could be repeated for up to three successful healing cycles for cracks below or equal to 0.3 mm, achieving over 80% recovery capacity in mechanical strength before and after each cycle. After carrying out both cyclic loading and damage-inducing experiments, this repeatability was evident as all performance tests showed consistent results over repeated thermal activations without loss of healing efficiency.

4.6 Validating wireless detection claims

These integrated CNT-based antennas for reliable signal transmission of not more than 50 m, which can detect cracks as small as about 0.05 mm precisely, made up the wireless sensing system. All of these were experimentally verified using real-time resistance monitoring blended with a four-point bending setup. This proves that there is a signal-noise ratio maintained above 20 dB even within the wired walls of the laboratory, and what was found through the impedance variation tracking was microcracks detected under both dry and humid conditions, thus proving the durability of the sensing mechanism for everyday applications.

4.7 Integration of physics-informed neural networks for generalize modeling

Later iterations of the AI framework could be improved significantly by the integration of physics informed neural networks that entwine governing physical laws with the learning of a data-driven model. By accommodating constitutive relationships such as stress–strain behavioral patterns or damage propagation kinetics into the neural architecture, PINNs will refine the interpretability and generalization of the model, possibly even under not-seen-before loading or environmental conditions. With this approach, the highly adaptable deep learning method is made entirely robust and consistent with known mechanics.

4.8 Role of sensitivity analysis in model design

How much effect the major input parameters such as CNT content, water-to-cement ratio, and crack width had on compressive strength and electrical resistivity was analyzed with a global sensitivity analysis through Sobol indices in process. The analysis demonstrates that most of the variance in model output, which is sometimes the main target in optimization, is influenced by CNT concentration (35%) and crack width (28%). This analysis gives insight into parametric interdependencies and factors from which material optimization can happen for specific performance outcomes in predictive modeling sets.

4.9 Artificial Intelligence complementing model behavior analysis

It was through the use of explainable AI features such as Shapley Additive Explanations (SHAP) values that a modeling foundation was set for understanding critical aspects of variable importance and model behavior. For example, SHAP analysis concluded that an improving strength retention from water-to-cement ratios below 0.45 had an increasing nonlinear positive influence while crack width disproportionately acted negatively on electrical stability. These interpretability tools enhance the confidence of predictions while decision-making in real-time monitoring systems.

4.10 Challenges for scalability and environmental robustness

At the scale for mass deployment, CNT-infused smart cement would face issues with long-term dispersability of CNT, nonhomogeneity in comparable mixing of very large batches, and high sensitivity toward moisture ingress. The mitigation strategies include the use of polycarboxylate superplasticizers and controlled sonication during mixing to avoid agglomeration. Environmental factors, especially moisture and thermal cycle, may also affect CNT networks. Ongoing studies are working to address environmental robustness and long-term stability with proposed systems of hydrophobic coating or encapsulant layers.

4.11 Transparency of artificial intelligence model and diversity of data sets

The AI framework is built on a structured data set holding 1000 samples and highly variant with respect to mix design parameters, environmental stressors, and mechanical responses. These data sets were balanced along performance categories to avoid bias and improve generalizability. The core algorithms were: Random Forests for feature ranking, ANN for pattern recognition, and SVM for classification on constrained data sets and samples. Training took between 2 and 5 h on graphics processing units-enabled platforms. The models were subjected to stratified cross-validation and testing against unseen validation subsets.

4.12 Assessment of self-healing repeatability

Material proved effective in repeatability of healing performance within three successive damage-healing cycles, allowing cracks of width up to 0.3 mm. Healing efficiency remained above 80%, after each cycle no visible mechanical integrity loss or electrical conductivity degradation was noted. Beyond three cycles, microcapsule depletion and matrix fatigue started to lower effectiveness, indicating that while the system can support multiple repair events, it may require recharging or reinforcement for long-term heavy-damage or high-cycle structure use sets.

4.13 Scalability and three-dimensional printing tasks

The fact that dispersion could not be maintained during the extrusion of the mixture was caused mainly by extrusion creating shear-induced re-agglomeration of CNT. This was solved through dispersants and real-time rheological monitoring during the printing process. Sensor integration, especially on CNT-based antenna structures, would require the sensors to be aligned within the print path, which was optimized using a synchronized multi-head deposition system. Printability index remained in the range, 1.2–1.5, thus ensuring consistent layer deposition and structural integrity sets.

4.14 Accelerated durability testing

Accelerated aging tests were carried out to simulate environmental exposure including 300 freeze–thaw cycles, raised temperature level (50 °C), and high humidity (95% relative humidity conditions. After this exposure, the material retained 85% of its initial compressive strength and 92% of its electrical conductivity, exhibiting an excellent resistance to environ degradations. These results indicate the effectiveness of using such composites for long-term application in infrastructures subjected to varying climates and mechanical trees.

4.15 Environmental stability of wireless signals

The CNT-based wireless sensing system distinguished itself by high robustness against environmental interference sets. Less than 5% in signal attenuation was recorded under 95% relative humidity and in proximity to metallic structures due to the nature of the antenna structures embedded in non-metallic and their different impedance matchings. Shielding layers and signal filtering algorithms were also used for the reduction of electromagnetic noise. Overall, the system gave consistent performance in both laboratory-controlled and semi-field test environments, thereby proving suitability for real-world applications.

5 Conclusions and future scope

5.1 Conclusions

The results of this work show that the proposed multi-functional CNT Infused smart cement composite has an immense transformative potential in elevating the mechanical performance of the composite, self-healing capacities, real-time structural monitoring, and predictive maintenance accuracy. The integration of chemically f-CNT into the cement matrix attained a 30% rise in compressive strength and reached its peak value under a water-to-cement ratio of 0.4 at 65 MPa. This is a very significant improvement over the current methods like plasma-modified CNT (58 MPa) and CNT-superplasticizer dispersion (55 MPa), pointing out the superior interfacial bonding and dispersion that can be achieved with the proposed functionalization approach. The electrical resistivity was reduced to 100 Ω·m, crack detection sensitivity 0.05 mm, and signal variation 95%, allowing for very precise and sensitive structural monitoring. These properties exceeded comparative methods’ abilities, which demonstrated higher resistivity (150–250 Ω·m) and lower crack sensitivity (0.07–0.12 mm) than the established levels. A self-healing system developed for this composite revealed 90% efficiency of healing for cracks with the width equal to 0.3 mm within 36 h and greatly surpassed existing techniques. Besides, a material under freeze−thaw cycles up to 300 showed retention of 85% strength and electrical stability at a level of 92%. The material was capable of 95% crack detection accuracy within a range of 50 m, thus showing a lot of applicability to the large-scale implementation. AI-driven predictive maintenance model based on real-time and historical data had 96% predictability, and hence 30% reduction in cost for maintenance. In this regard, it established the composite as cost-effective and reliable for the sustenance of infrastructure. The results of this study underpin the effectiveness of the proposed smart cement composite in addressing some of the critical challenges facing modern infrastructure, such as durability, sustainability, and real-time monitoring. f-CNT, self-healing agents, and advanced sensing capabilities are integrated into the material to provide a holistic solution for next-generation construction. These results provide a good foundation in developing smart infrastructure systems with low material consumption, efficiency of operations, and prolonging the structural lifespans.

5.2 Future scope

While this work has proven the superior performance of the proposed CNT Infused cement composite, several aspects are left open for future investigation to bring it fully into practice. In the first place, large-scale feasibility of the functionalization and dispersion processes should be ascertained with a focus on cost optimization and reproducibility for industrial applications. Further, although the material demonstrated an excellent self-healing performance, future work can be extended by using alternative encapsulated healing agents so that the crack sizes and the environmental conditions under which these cracks can be healed can be extended. For example, healing agents that can heal cracks that are larger than 0.5 mm or heal under lower temperatures can widen the applicability levels of the composite. Future research may be focused on improving the AI models to accommodate multi-modal sensor data such as vibrational and strain measurements toward predictive maintenance. Predictive degradation can also be further developed with the capability of simulating long-term degradation under various real-world conditions such as dynamic loads and chemical attacks. Investigating the recyclability and end-of-life properties of CNT-enhanced composites will contribute to practices in circular economy, therefore ensuring that the material is fit for the principles of a green construction. Addressing all these areas will further optimize this proposed smart cement composite to deliver on the demands of sophisticated and sustainable infrastructure projects which the process demands.

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

RIGHTS & PERMISSIONS

Higher Education Press