<i>Retracted:</i> Prediction of the elastic modulus of recycled aggregate concrete applying hybrid artificial intelligence and machine learning algorithms

Zhou, Qian; Afzal, Mansour

doi:10.1002/suco.202100250

Cited by 27 publications

(8 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of artificial intelligence in the field of civil engineering 33–44 today more than ever is being considered 45 . The application of artificial intelligence considerably reduces the cost and time of experimental studies, provided that there is sufficient laboratory data in this area 46 .…”

Section: Introductionmentioning

confidence: 99%

“…Most studies showed that in addition to the concentration of chloride ions, increasing the temperature also significantly reduces the chloride ions penetration resistance. [30][31][32] The use of artificial intelligence in the field of civil engineering [33][34][35][36][37][38][39][40][41][42][43][44] today more than ever is being considered. 45 The application of artificial intelligence considerably reduces the cost and time of experimental studies, provided that there is sufficient laboratory data in this area.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Retracted: A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Jia

Zhao

Esmaeili‐Falak

2022

Structural Concrete

View full text Add to dashboard Cite

Obtaining a trustworthy approach to forecast the chloride penetration into self-compacting concrete via rapid test may lead to frugality in cost, time, and energy to provide a durable mix design. Different single and hybrid regression methods are developed to predict the results of rapid chloride penetration tests in the present study. Cement content, fly ash, and silica fume replacement percent with cement, temperature and fine and coarse aggregates are considered as input variables. All predicted values using expanded models have a good agreement with experimentally measured results. Evaluating the accuracy and precision of single and hybrid optimized models by five statistical performance criteria (R 2 , root mean square error, mean absolute error, mean absolute percentage error, and performance index) illustrates that the hybrid support vector regression with optimization algorithm is a high-accurate promising model for predicting the results of a rapid chloride penetration test.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retracted: A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Jia

Zhao

Esmaeili‐Falak

2022

Structural Concrete

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the models, five evaluation criteria are introduced, namely, the correlation coefficient ( R ), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and relative root mean square percentage error (RRMSE). These statistical parameters are defined as follows 49–53

R goodbreak= \frac{n \sum ((), y_{true} y_{predict}) - \sum y_{true} \sum y_{predict}}{\sqrt{[n (\sum y_{true}^{2}) - {((), \sum y_{true})}^{2}] [n (\sum y_{predict}^{2}) - {((), \sum y_{predict})}^{2}]}}

RMSE goodbreak= \sqrt{\frac{1}{n} \sum_{i = 1}^{n} {(y_{true} - y_{predict})}^{2}}

MAE goodbreak= \frac{1}{n} \sum_{i = 1}^{n} (||, y_{true} goodbreak- y_{predict})

MAPE goodbreak= \frac{1}{n} \sum_{i = 1}^{n} (||, \frac{y_{true} - y_{predict}}{y_{true}})

RRMSE goodbreak= \frac{\sqrt{\frac{1}{n} {false\sum}_{i = 1}^{n} {((), y_{true} goodbreak- y_{predict})}^{2}}}{\frac{1}{n} \sum_{i = 1}^{n} y_{true}}

...…”

Section: Dataset Descriptionmentioning

confidence: 99%

Prediction and uncertainty quantification of compressive strength of high‐strength concrete using optimized machine learning algorithms

Han

Liu

2022

Structural Concrete

View full text Add to dashboard Cite

Compressive strength is considered to be one of the most important mechanical properties of high-strength concrete (HSC). In this study, three machine learning models, ELM, PSO-ANN, and GS-SVR were employed to predict the compressive strength of HSC using 681 data records. The five ingredients and the compressive strength of HSC were regarded as input variable features and output target, respectively. Results indicated that the GS-SVR model showed the best performance in forecasting with the R of 0.992, MAPE of 0.016, RMSE of 1.241, MAE of 0.842, and RRMSE of 0.024, which could be an alternative candidate for strength prediction. Additionally, a sensitivity analysis was performed within the GS-SVR model to achieve an in-depth examination of the effect of each single input variable on the compressive strength. The sensitivity of input variables demonstrated that the water is the most sensitive parameter to the compressive strength of HSC, while the superplasticizer is less sensitive.

show abstract

“…ML algorithms enable the analysis of multiple variables and the processing of large amounts of data [ 19 ]. ML techniques have proved to be successful in predicting the mechanical properties of concrete, e.g., compressive strength [ 20 , 21 ], tensile strength [ 22 , 23 ], and elastic modulus [ 24 ] Furthermore, ML is efficiently applied in analyzing durability and deterioration processes, e.g., sulfate attack [ 25 ], chloride diffusion [ 26 ], and alkali–silica reaction [ 27 ]. Recently, applications in more sophisticated areas were proposed, e.g., predicting hydration kinetics [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete

et al. 2023

View full text Add to dashboard Cite

Developing accurate and interpretable models to forecast concrete’s self-healing behavior is of interest to material engineers, scientists, and civil engineering contractors. Machine learning (ML) and artificial intelligence are powerful tools that allow constructing high-precision predictions, yet often considered “black box” methods due to their complexity. Those approaches are commonly used for the modeling of mechanical properties of concrete with exceptional accuracy; however, there are few studies dealing with the application of ML for the self-healing of cementitious materials. This paper proposes a pioneering study on the utilization of ML for predicting post-fire self-healing of concrete. A large database is constructed based on the literature studies. Twelve input variables are analyzed: w/c, age of concrete, amount of cement, fine aggregate, coarse aggregate, peak loading temperature, duration of peak loading temperature, cooling regime, duration of cooling, curing regime, duration of curing, and specimen volume. The output of the model is the compressive strength recovery, being one of the self-healing efficiency indicators. Four ML methods are optimized and compared based on their performance error: Support Vector Machines (SVM), Regression Trees (RT), Artificial Neural Networks (ANN), and Ensemble of Regression Trees (ET). Monte Carlo analysis is conducted to verify the stability of the selected model. All ML approaches demonstrate satisfying precision, twice as good as linear regression. The ET model is found to be the most optimal with the highest prediction accuracy and sufficient robustness. Model interpretation is performed using Partial Dependence Plots and Individual Conditional Expectation Plots. Temperature, curing regime, and amounts of aggregates are identified as the most significant predictors.

show abstract

Retracted: Prediction of the elastic modulus of recycled aggregate concrete applying hybrid artificial intelligence and machine learning algorithms

Cited by 27 publications

References 53 publications

Retracted: A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Retracted: A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Prediction and uncertainty quantification of compressive strength of high‐strength concrete using optimized machine learning algorithms

Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete

Contact Info

Product

Resources

About