TY - JOUR
T1 - Machine learning models to predict sewer concrete strength exposed to sulfide environments
T2 - unveiling the superiority of Bayesian-optimized prediction models
AU - Siddiq, Bilal
AU - Javed, Muhammad Faisal
AU - Khan, Majid
AU - Aladbuljabbar, Hisham
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
PY - 2024/11
Y1 - 2024/11
N2 - The climate catastrophe is the result of unrelenting developments and overuse of concrete resources. One development is the reinforced concrete sewerage network, which needs constant maintenance due to its strength deteriorates over time. Therefore, this study approaches the development of optimized, unoptimized, and standalone models such as extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) to predict the concrete compressive strength exposed to aggressive conditions. Therefore, this study and concrete materials must use ensembles and non-linear models. Accordingly, 138 specimens with five significant inputs were gathered for training (80%) and testing (20%) assessment. K-fold cross-validation and fitness functions (R2, RMSE, MAE, PI, VAF, LMI, and a-20 index) were used to evaluate the models' dependability. Among all techniques, the ensemble optimized method (BO-XGboost) provided a remarkable correlation (R2 = 0.99) in comparison to the rival methods such as BO-Ad boost (R2 = 0.94), BO-RF (R2 = 0.93), XGBoost (R2 = 0.93), Adboost (R2 = 0.91), RF (R2 = 0.90), ANN (R2 = 0.82), SVR (R2 = 0.74). Similarly, the BO-XGBoost exhibited the slightest error of RMSE = 1.87 MPa among all techniques. Furthermore, the k-fold cross-validation confirmed the model's (BO-XGB) authenticity and performance. In addition to that, the XGBoost Shapley analysis showed that w/c is the dominant component in the concrete strength prediction. This study's findings verified that w/c has a greater influence on concrete compressive strength compared to other elements. In terms of innovation, the application's graphical user interface is established to make the model simpler for inputs and necessary results and open up a useful and practical engineering route. Graphical abstract: (Figure presented.).
AB - The climate catastrophe is the result of unrelenting developments and overuse of concrete resources. One development is the reinforced concrete sewerage network, which needs constant maintenance due to its strength deteriorates over time. Therefore, this study approaches the development of optimized, unoptimized, and standalone models such as extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) to predict the concrete compressive strength exposed to aggressive conditions. Therefore, this study and concrete materials must use ensembles and non-linear models. Accordingly, 138 specimens with five significant inputs were gathered for training (80%) and testing (20%) assessment. K-fold cross-validation and fitness functions (R2, RMSE, MAE, PI, VAF, LMI, and a-20 index) were used to evaluate the models' dependability. Among all techniques, the ensemble optimized method (BO-XGboost) provided a remarkable correlation (R2 = 0.99) in comparison to the rival methods such as BO-Ad boost (R2 = 0.94), BO-RF (R2 = 0.93), XGBoost (R2 = 0.93), Adboost (R2 = 0.91), RF (R2 = 0.90), ANN (R2 = 0.82), SVR (R2 = 0.74). Similarly, the BO-XGBoost exhibited the slightest error of RMSE = 1.87 MPa among all techniques. Furthermore, the k-fold cross-validation confirmed the model's (BO-XGB) authenticity and performance. In addition to that, the XGBoost Shapley analysis showed that w/c is the dominant component in the concrete strength prediction. This study's findings verified that w/c has a greater influence on concrete compressive strength compared to other elements. In terms of innovation, the application's graphical user interface is established to make the model simpler for inputs and necessary results and open up a useful and practical engineering route. Graphical abstract: (Figure presented.).
KW - Bayesian optimization
KW - Concrete compressive strength
KW - Extreme gradient boosting
KW - Machine learning
KW - Sulfuric gas
UR - https://www.scopus.com/pages/publications/85201584616
U2 - 10.1007/s41939-024-00561-w
DO - 10.1007/s41939-024-00561-w
M3 - Article
AN - SCOPUS:85201584616
SN - 2520-8160
VL - 7
SP - 6045
EP - 6071
JO - Multiscale and Multidisciplinary Modeling, Experiments and Design
JF - Multiscale and Multidisciplinary Modeling, Experiments and Design
IS - 6
ER -