Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete (UHPC)

Mahjoubi, Soroush; Meng, Weina; Bao, Yi

doi:10.1016/j.asoc.2021.108182

Cited by 48 publications

(15 citation statements)

References 56 publications

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“…Mahjoubi, et al [ 46 ] constructed an auto-tune learning framework for ultra-high-performance concrete flowability, mechanical characteristics, and porosity prediction (UHPC). Other models were also considered by Mahjoubi et al [ 47 , 48 ] in previous studies for multiple functions, and can be applied to similar types of studies in the future.…”

Section: Resultsmentioning

confidence: 99%

“…Other models were also considered by Mahjoubi et al [ 47 , 48 ] in previous studies for multiple functions, and can be applied to similar types of studies in the future. This study evaluated compressive strength in the range of 100–160 MPa, considering 372 mix proportions with 10 input parameters selected from the database of Mahjoubi et al [ 39 , 46 ]. A much more relevant model could be obtained by increasing the number of datasheets and by importing a significantly higher number of mixtures, as well as by considering higher input parameters.…”

Section: Resultsmentioning

confidence: 99%

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Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

et al. 2022

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In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The model’s performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study’s findings.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

et al. 2022

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“…The multicollinearity occurs because of the strong correlation between input parameters which may lead to the misinterpretation of the effect of input variables. It was reported that multicollinearity occurs if the absolute value of the correlation coefficient is higher than 0.70 [39]. The relative distribution of some input and output parameters is shown in Figure 7.…”

Section: Experimental Data Setmentioning

confidence: 95%

Comparative study of advanced computational techniques for estimating the compressive strength of UHPC

Khan¹,

Lao²,

Dai³

2022

JACF

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The effect of raw materials on the compressive strength of concrete is a complex process, especially in the case of ultra-high-performance concrete (UHPC), where a higher number of inter-dependent parameters are involved in the strength development. In this era of digitalization, advanced machine learning methods are used to predict the material's mechanical characteristics because of their superior performance compared to conventional and nonlinear statistical regression models. Thus, the goal of the current study is to estimate the compressive strength of UHPC from the designed raw materials using advanced machine learning techniques. The compressive strength of UHPC is predicted from the 14 input parameters, i.e., cement, fly ash, slag, silica fume, nano-silica, limestone powder, sand, coarse aggregate, quartz powder, water, superplasticizer, PE fiber, steel fiber, and curing time. A total of eight machine learning models were compared that include multi-layer perceptron neural network (MLPNN), MLPNN Bootstrap aggregating (MLPNN-BA), MLPNN adaptive boosting (MLPNN-AB), Gradient boosting (GB), Decision tree (DT), DT Bootstrap aggregating (DT-BA), DT adaptive boosting (DT-AB) and Random Forest (RNF). The validation and performance evaluation of the above models were checked by using K-fold cross-validation, mean absolute error (MAE), root mean square error (RSME), coefficient of determination (R2), relative root mean square error (RRMSE), performance index (PI), and Nash Sutcliffe efficiency (NSE). The optimal model was selected based on the results of all statistical checks. It was found the ensembled machine learning models especially decision tree-based models outperform the neural network-based models with higher accuracy and low error. Thus, the recommended machine learning model is random forest having superior prediction capacity followed by DT Bootstrap aggregating and DT adaptive boosting.

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“…The database was taken from the literature [44,45] and includes 255 mix designs having 15 input factors with compressive strength of 60-120 MPa. Table 1 exhibits the statistical summary of the database utilized to predict SFRHSC compressive strength.…”

Section: Datasetmentioning

confidence: 99%

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete

Li¹,

Xu²,

Ahmad

et al. 2022

Materials

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The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers’ addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered parameters were cement content, fly ash content, slag content, silica fume content, nano-silica content, limestone powder content, sand content, coarse aggregate content, maximum aggregate size, water content, super-plasticizer content, steel fiber content, steel fiber diameter, steel fiber length, and curing time. The application of statistical checks, i.e., root mean square error (RMSE), determination coefficient (R2), and mean absolute error (MAE), was also performed for the assessment of algorithms’ performance. The study demonstrated the suitability of the Bagging technique in the prediction of SFRHSC compressive strength. Compared to other models, the Bagging approach was more accurate as it produced higher, i.e., 0.94, R2, and lower error values. It was revealed from the SHAP analysis that curing time and super-plasticizer content have the most significant influence on the compressive strength of SFRHSC. The outcomes of this study will be beneficial for researchers in civil engineering for the timely and effective evaluation of SFRHSC compressive strength.

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Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete (UHPC)

Cited by 48 publications

References 56 publications

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning

Comparative study of advanced computational techniques for estimating the compressive strength of UHPC

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete

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