A novel approach in forecasting compressive strength of concrete with carbon nanotubes as nanomaterials

Jiao, Hongbo; Wang, Yonggang; Li, Lielie; Arif, Kiran; Farooq, Furqan; Al-Askar, Abdulaziz A.

doi:10.1016/j.mtcomm.2023.106335

Cited by 20 publications

(3 citation statements)

References 64 publications

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“…In order for ML algorithms to create the predicted output variable, they require a number of different input variables. Data were taken from previously published works and included in this review for the purpose of predicting the CS of concrete containing CNTs [15]. In order to accurately forecast the compressive strength of concrete, six characteristics were used as inputs.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Modern construction trends, such as the building of modern bridges, high-rise buildings, and huge water accumulation systems, are also driving the rising demand for concrete [13,14]. Nonetheless, the appearance of nanoscale voids and cracks is a major disadvantage that lowers concrete's performance and reduces its lifespan [15]. Consequently, incorporating nanoparticles into a cementitious matrix increases mechanical strength and makes the material very resistant to cracking [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Zhu,

Wu,

et al. 2024

Buildings

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The standard approach for testing ordinary concrete compressive strength (CS) is to cast samples and test them after different curing times. However, testing adds cost and time to projects, and, therefore, construction sites experience delays. Because carbon nanotubes (CNTs) vary in length, composition, diameter, and dispersion, experiment and formula fitting alone cannot reliably predict the strength of CNTs-based composites. For empirical equations or traditional statistical approaches to properly forecast complex materials’ mechanical characteristics, various significant parameters, databases, and nonlinear relationships between variables must be considered. Machine learning (ML) tools are the most advanced for accurate predictions of material behaviour. This study employed gradient boosting, light gradient boosting machine, and extreme gradient boosting techniques to forecast the CS of CNTs-modified concrete. Also, in order to explore the influence and interaction of various features, an interaction analysis was conducted. In terms of R2, gradient boosting, light gradient boosting machine, and extreme gradient boosting models proved their accuracy. Extreme gradient boosting had the highest R2 of 0.97, followed by light gradient boosting machine and gradient boosting with scores of 0.94 and 0.93, respectively. This type of research may help both academics and industry forecast material properties and influential elements, thereby reducing lab test requirements.

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Section: Data Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Zhu,

Wu,

et al. 2024

Buildings

View full text Add to dashboard Cite

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“…Hosseinzadeh et al (2023) 15 used ML techniques (both regression and classification algorithms), to forecast the mechanical properties of fly ash-based recycled aggregate concrete (FARAC). Jiao et al (2023) 16 employed ML to predict the compressive strength of concrete with carbon nanotubes as nanomaterials. In this context, Zheng et al (2023) 17 developed an ML model to predict the compressive strength of silica fume concrete.…”

Section: Introductionmentioning

confidence: 99%

An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

Hosseinzadeh,

Mousavi,

Hosseinzadeh

et al. 2023

Sci Rep

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By conducting an analysis of chloride migration in concrete, it is possible to enhance the durability of concrete structures and mitigate the risk of corrosion. In addition, the utilization of machine learning techniques that can effectively forecast the chloride migration coefficient of concrete shows potential as a financially viable and less complex substitute for labour-intensive experimental evaluations. The existing models for predicting chloride resistance encounter two primary challenges: the constraints imposed by a limited dataset and the absence of certain input variables. These factors collectively contribute to a decrease in the overall effectiveness of these models. Therefore, this study aims to propose an advanced approach for dataset cleaning, utilizing a comprehensive experimental dataset comprising 1073 pre-existing experimental outcomes. The proposed model for predicting the chloride diffusion coefficient incorporates various input variables, such as water content, cement content, slag content, fly ash content, silica fume content, fine aggregate content, coarse aggregate content, superplasticizer content, fresh density, compressive strength, age of compressive strength test, and age of migration test. The utilization of the artificial neural network (ANN) technique is also employed for the processing of missing data. The current supervised learning incorporates both regression and classification tasks. The efficacy of the proposed models for accurately predicting the chloride diffusion coefficient has been effectively validated. The findings indicate that the XGBoost and SVM algorithms exhibit superior performance compared to other regression prediction algorithms, as evidenced by their high R2 scores of 0.94 and 0.91, respectively. In relation to classification algorithms, the findings demonstrate that the Random Forest, LightGBM, and XGBoost models exhibit the highest levels of accuracy, specifically 0.93, 0.96, and 0.97, respectively. Furthermore, a website has been developed that is capable of predicting the chloride migration coefficient and chloride penetration resistance of concrete.

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Machine learning assisted prediction of the mechanical properties of carbon nanotube‐incorporated concrete

Imran,

Amjad,

Khan

et al. 2024

Structural Concrete

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The incorporation of carbon nanotubes (CNTs) in concrete can improve the physical, mechanical, and durability properties. However, the interaction of CNTs with concrete and their effect on the mechanical properties remains a challenging issue. Also, the determination of mechanical properties through experimental testing is time‐consuming, laborious, and uneconomical. This study focuses on the development of machine learning (ML) models for the prediction of the mechanical properties of concrete. A comprehensive data set of 758 CNT‐modified concrete specimens was established for the compressive strength (CS), split tensile strength (STS), flexural strength (FS), and modulus of elasticity (MOE) values from the experimental studies in the literature. Afterward, the predictive models were developed using multilinear regression (MLR), support vector machine (SVM), ensemble methods (EN), regression tree (RT), and Gaussian process regression (GPR). It was found that among ML models, the GPR model predicted the CS, STS, and FS at the highest efficiency with the coefficient of determination (R2) of 0.83, 0.78, and 0.93, respectively while the performance of the SVM model was superior for predicting MOE with an R2 value of 0.91. The mean absolute error (MAE) of the GPR model for CS, STS, FS, and MOE were 2.92, 0.26, 0.35, and 1.31, respectively which were also lesser than other models. The training time of different models demonstrated that the GPR model has also a lower training time (~3 s) as compared to other models which indicates it has a high accuracy‐to‐time cost ratio. Further, the most influential parameters on CS were age, cement, water–cement ratio, and carbon nanotubes. The one‐way partial dependence analysis showed a direct correlation for age and cement but an inverse correlation for the water–cement ratio and fine aggregate. The graphical user interface provides the implication of the developed models for practical applications.

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A novel approach in forecasting compressive strength of concrete with carbon nanotubes as nanomaterials

Cited by 20 publications

References 64 publications

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

Machine learning assisted prediction of the mechanical properties of carbon nanotube‐incorporated concrete

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