| 1. |
- Alyami, Mana, et al.
(författare)
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Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete
- 2024
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Ingår i: Developments in the Built Environment. - 2666-1659. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the construction industry has been striving to make production faster and handle more complex architectural designs. Waste reduction, geometric freedom, lower construction costs, and speedy construction make the 3D-printed fiber-reinforced concrete (3DPFRC) alternative for future construction. However, achieving the optimum mixture composition for 3DPFRC remains a daunting task, entailing the consideration of multiple variables and necessitating an extensive trial-and-error experimental process. Therefore, this study investigated the application of different metaheuristic optimization algorithms to predict the compressive strength (CS) of 3DPFRC. A database of 299 data samples with 16 different input features was compiled from the experimental studies in the literature. Six metaheuristic algorithms, such as human felicity algorithm (HFA), differential evolution algorithm (DEA), nuclear reaction optimization (NRO), Harris hawks optimization (HHO), lightning search algorithm (LSA), and tunicate swarm algorithm (TSA) were applied to identify the optimal hyperparameter combination for the random forest (RF) model in predicting the CS of 3DPFRC. Different statistical metrics and 10-fold cross-validation were used to evaluate the accuracy of the models. The TSA-RF model exhibited superior performance compared to other models, achieving correlation (R), mean absolute error (MAE), and root mean square error (RMSE) values of 0.99, 2.10 MPa, and 3.59 MPa, respectively. The LSA-RF model also performed well, with R, MAE, and RMSE values of 0.99, 2.93 MPa, and 6.23 MPa, respectively. SHapley Additive exPlanation (SHAP) interpretability elucidates the intricate relationships between features and their effects on the CS, thereby offering invaluable insights for the performance-based mix proportion design of 3DPFRC.
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| 2. |
- Alyami, Mana, et al.
(författare)
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Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
- 2024
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Ingår i: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- The construction sector is a major contributor to global greenhouse gas emissions. Using recycled and waste materials in concrete is a practical solution to address environmental challenges. Currently, agricultural waste is widely used as a substitute for cement in the production of eco-friendly concrete. However, traditional methods for assessing the strength of such materials are both expensive and time-consuming. Therefore, this study uses machine learning techniques to develop prediction models for the compressive strength (CS) of rice husk ash (RHA) concrete. The ML techniques used in the present study include random forest (RF), light gradient boosting machine (LightGBM), ridge regression, and extreme gradient boosting (XGBoost). A total of 348 values of CS were collected from the experimental studies, and five characteristics of RHA concrete were taken as input variables. For the performance assessment of the models, multiple statistical metrics were used. During the training phase, the correlation coefficients (R) obtained for ridge regression, RF, XGBoost, and LightGBM were 0.943, 0.981, 0.985, and 0.996, respectively. In the testing set, the developed models demonstrated even higher performance, with correlation coefficients of 0.971, 0.993, 0.992, and 0.998 for ridge regression, RF, XGBoost, and LightGBM, respectively. The statistical analysis revealed that the LightGBM model outperformed other models, whereas the ridge regression model exhibited comparatively lower accuracy. SHapley Additive exPlanation (SHAP) method was employed for the interpretability of the developed model. The SHAP analysis revealed that water-to-cement is a controlling parameter in estimating the CS of RHA concrete. In conclusion, this study provides valuable guidance for builders and researchers to estimate the CS of RHA concrete. However, it is suggested that more input variables be incorporated and hybrid models utilized to further enhance the reliability and precision of the models.
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| 3. |
- Fawad, Muhammad, et al.
(författare)
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Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.
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| 4. |
- Javed, Muhammad Faisal, et al.
(författare)
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Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 5. |
- Khan, Majid, et al.
(författare)
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Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms
- 2023
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Ingår i: Heliyon. - : Cell Press. - 2405-8440. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.
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