Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization

Yang, Dawei; Xu, Ping; Zaman, Athar; Alomayri, Thamer; Houda, Moustafa; Al-Askar, Abdulaziz A.; Javed, Muhammad Faisal

doi:10.1016/j.jmrt.2023.04.250

Cited by 16 publications

(2 citation statements)

References 91 publications

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“…To predict product defects using data collected from the factory that had undergone preprocessing, a total of five machine learning-based classification models were utilized: K-Nearest Neighbors Classifier, Decision Trees Classifier, Random Forest Classifier, Extra Trees Classifier, and Gradient Boosting Classifier. In employing each model, parameters such as the number of neighbors (n_neighbors), the number of trees (n_estimators), the maximum depth of the trees (max_depth), and the learning rate (learning_rate) were meticulously controlled to ensure a fair comparison of accuracy across models [32][33][34][35][36]. Furthermore, the Grid Search method was employed to identify the most optimal combination of parameters for each model [37].…”

Section: Anomaly Detection Using Machine Learning Modelsmentioning

confidence: 99%

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Kim,

Seo,

Lee

2024

Electronics

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In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios.

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Section: Anomaly Detection Using Machine Learning Modelsmentioning

confidence: 99%

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Kim,

Seo,

Lee

2024

Electronics

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“…Due to the advancement of AI, various soft-computing approaches have been utilized to forecast the characteristics of various types of concrete. For instance, ML methods have been used for predicting properties of recycled aggregate concrete 31 , 32 , fiber-reinforced concrete 33 , carbon fiber-reinforced concrete 34 , 35 , geopolymer concrete 36 , 37 , and concrete containing SCMs such as slag, fly ash, and silica fume 38 – 40 , as shown in Fig. 1 .…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand

Javed,

Khan,

Fawad

et al. 2024

Sci Rep

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The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.

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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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Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization

Cited by 16 publications

References 91 publications

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand

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

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