| 1. |
- Alyami, Mana, et al.
(författare)
-
Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
- 2024
-
Ingår i: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 20
-
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.
|
|
| 2. |
- Alyousef, Rayed, et al.
(författare)
-
Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches
- 2024
-
Ingår i: Case Studies in Construction Materials. - : Elsevier Ltd. - 2214-5095. ; 20
-
Tidskriftsartikel (refereegranskat)abstract
- Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the durability and strength of the concrete. Nevertheless, the complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints in comprehensively capturing these intricate compositions. Thus, posing challenges in delivering precise and dependable estimations. Therefore, the current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS), in conjunction with experimental investigation to study the effect of the integration of graphene nanoplatelets (GNPs) on the electrical resistivity (ER) and compressive strength (CS) of concrete containing GNPs. Concrete containing GNPs demonstrated an improved fractional change in resistivity (FCR) and strength. The experimental measures depict that strength enhancement was notable at GNP concentrations of 0.05% and 0.1%, showcasing increases of 13.23% and 16.58%, respectively. Simultaneously, the highest observed FCR change reached −12.19% and −13%, respectively. The prediction efficacy of the three models proved to be outstanding in forecasting the characteristics of concrete containing GNPs. For CS, the GEP, ANN, and ANFIS models demonstrated impressive correlation coefficient (R) values of 0.974, 0.963, and 0.954, respectively. For electrical resistivity, the GEP, ANN, and ANFIS models exhibited high R-values of 0.999, 0.995, and 0.987, respectively. The comparative analysis of the models revealed that the GEP model delivered precise predictions for both ER and CS. The mean absolute error (MAE) of the GEP-CS model demonstrated a 14.51% reduction compared to the ANN-CS model and a substantial 48.15% improvement over the ANFIS-CS model. Similarly, the ANN-CS model displayed an MAE that was 38.14% lower compared to the ANFIS-CS model. Moreover, the MAE of the GEP-ER model demonstrated a 56.80% reduction compared to the ANN-CS model and a substantial 82.47% improvement over the ANFIS-CS model. The Shapley Additive explanation (SHAP) analysis provided that curing age exhibited the highest SHAP score. Thus, indicating its predominant contribution to CS prediction. In predicting ER, the graphene content exhibited the highest SHAP score, signifying its predominant contribution to ER estimation. This study highlights ML's accuracy in predicting the properties of concrete with graphene nanoplatelets, offering a fast and cost-effective alternative to time-consuming experiments.
|
|
| 3. |
- Khan, Majid, et al.
(författare)
-
Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms
- 2024
-
Ingår i: Results in Engineering (RINENG). - : Elsevier B.V.. - 2590-1230. ; 21
-
Tidskriftsartikel (refereegranskat)abstract
- Integrating nanomaterials into concrete is a promising solution to improve concrete strength and durability. However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions to provide accurate and reliable estimations. This study focuses on developing robust prediction models for the compressive strength (CS) of graphene nanoparticle-reinforced cementitious composites (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), and AdaBoost regressor (AR), were employed to predict CS based on a comprehensive dataset of 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand content (SC), curing age (CA), and GrN thickness (GT), were considered. The models were trained with 70 % of the data, and the remaining 30 % of the data was used for testing the models. Statistical metrics such as mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R) were employed to assess the predictive accuracy of the models. The DT and AR models demonstrated exceptional accuracy, yielding high correlation coefficients of 0.983 and 0.979 for training, and 0.873 and 0.822 for testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted the influential role of curing age and GrN thickness (GT), positively impacting CS, while an increased water-to-cement ratio (w/c) negatively affected CS. This study showcases the efficacy of ML techniques in accurately predicting CS of graphene nanoparticle-modified concrete, offering a swift and cost-effective approach for assessing nanomaterial impact on concrete strength and reducing reliance on time-consuming and expensive experiments.
|
|
