Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques

Wu, Yanqi; Zhou, Yisong

doi:10.1007/s11356-022-22048-2

Cited by 39 publications

(15 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to that proposed by ACI, many investigators have proposed tensile strength formulas for HPC or highstrength concrete, most of which involve a square root function. 8,9 De Larrard and Malier 10 have studied the calculating TS achieved from the French regulations agrees well with the observed dataset. Kim et al 11 found that the ACI model overestimated the TS for concrete with compressive strength <20 and underestimated >30 MPa.…”

Section: Introductionsupporting

confidence: 73%

“…Moreover, pavement slabs and airport runways are structured according to their tensile strength. Similar to that proposed by ACI, many investigators have proposed tensile strength formulas for HPC or high‐strength concrete, most of which involve a square root function 8,9 . De Larrard and Malier 10 have studied the calculating TS achieved from the French regulations agrees well with the observed dataset.…”

Section: Introductionmentioning

confidence: 63%

See 1 more Smart Citation

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Chao

2023

Structural Concrete

View full text Add to dashboard Cite

The evaluation of fly ash (FA) and micro‐silica (MS) effects on the mechanical properties of concrete at various ages prompts the search for efficient factors in forecasting the compressive strength (CS) and tensile strength (TS), which are extendable for future investigation as well as practical use. The main aim of this article is to overcome the complexity caused by nonlinearity, which is rooted in the relationship between input variables and outputs. Also, the nonlinearity is getting worse through the existence of admixtures. The Adaptive Boosting (ADA) approach was utilized in the related study as a hybrid model to provide an optimal relation between the ingredients and the mechanical strengths for evaluating the parameters most useful in predicting the CS and TS of high‐performance concrete. Furthermore, model boosting is applied using the starling murmuration optimization (SMO), termite queen algorithm (TQO), and gannet optimization algorithm (GOA) algorithms to reach the highest convergence. As a result of assessing the related models, the ADA‐SMO obtained a coefficient correlation of 0.9981 and 0.9825 for the predictions of CS and TS, respectively. Considering the adaptive framework provided to have the best compatibility with the complex physics of the problem, the ADA‐SMO earned the highest level of accuracy in estimating CS and TS among other hybrid models and was introduced as a trustable model in the prediction of strength properties.

show abstract

Section: Introductionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 63%

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Chao

2023

Structural Concrete

View full text Add to dashboard Cite

show abstract

“…The study of the importance and degree of influence of design parameters on the bearing capacity is an important guide for the design of CFST. For this reason, the Shapley additive explanation (SHAP) method is introduced in this section to analyze the influence of design parameters on the output 44 , 45 . As shown in Fig.…”

Section: Feature Importance Analysismentioning

confidence: 99%

Application of ensemble model in capacity prediction of the CCFST columns under axial and eccentric loading

Wang

Lü²,

Cheng³

2023

Sci Rep

View full text Add to dashboard Cite

Understanding the load-carrying capacity of circular concrete-filled steel tube (CCFST) columns is crucial for designing CCFST structures. However, traditional empirical formulas often yield inconsistent results for the same scenario, causing confusion for decision makers. Additionally, simple regression analysis is unable to accurately predict the complex mapping relationship between input and output variables. To address these limitations, this paper proposes an ensemble model that incorporates multiple input features, such as component geometry and material properties, to predict CCFST load capacity. The model is trained and tested on two datasets comprising 1305 tests on CCFST columns under concentric loading and 499 tests under eccentric loading. The results demonstrate that the proposed ensemble model outperforms conventional support vector regression and random forest models in terms of the determination coefficient (R2) and error metrics (MAE, RMSE, and MAPE). Moreover, a feature analysis based on the Shapley additive interpretation (SHAP) technique indicates that column diameter is the most critical factor affecting compressive strength. Other important factors include tube thickness, yield strength of steel tube, and concrete compressive strength, all of which have a positive effect on load capacity. Conversely, an increase in column length or eccentricity leads to a decrease in load capacity. These findings can provide useful insights and guidance for the design of CCFST columns.

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

Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques

Cited by 39 publications

References 55 publications

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Application of ensemble model in capacity prediction of the CCFST columns under axial and eccentric loading

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

Contact Info

Product

Resources

About