Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms

Chen, Hong-Gen; Li, Xin; Wu, Yanqi; Zuo, Le; Lu, Mengjie; Zhou, Yisong

doi:10.3390/buildings12030302

Cited by 49 publications

(12 citation statements)

References 34 publications

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“…Another significant property of HPC is its compressive strength (CS), which indicates its ability to withstand stress before the collapse. This further emphasizes the importance of CS properties 12–14 . HPC density directly affects CS through component performance characteristics.…”

Section: Introductionmentioning

confidence: 86%

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Optimal boosting method of HPC concrete compressive and tensile strength prediction

Chao

2023

Structural Concrete

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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.

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Section: Introductionmentioning

confidence: 86%

“…This further emphasizes the importance of CS properties. [12][13][14] HPC density directly affects CS through component performance characteristics. There is a direct linear relationship between the CS and the density of HPC.…”

Section: Introductionmentioning

confidence: 99%

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Chao

2023

Structural Concrete

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“…For this purpose, the Shapley additive explanation (SHAP) method is introduced in this section to analyze the importance and contribution of each variable to the output results. As a game theory-based approach, the output model is constructed as a linear addition of the input variables in SHAP, which identifies whether the input variables contribute positively or negatively to each prediction [48,49]. The explanatory model g(x ) of the original model f (x) can be expressed as follows [50].…”

Section: Shap-based Importance Factor Identificationmentioning

confidence: 99%

Axial Compression Prediction and GUI Design for CCFST Column Using Machine Learning and Shapley Additive Explanation

Liu

Zhou

2022

Buildings

Self Cite

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Axial bearing capacity is the key index of circular concrete-filled steel tubes (CCFST). A hybrid PSO-ANN model consisting of an artificial neural network (ANN) optimized with particle swarm algorithm (PSO) was proposed to reliably and accurately predict the axial bearing capacity in this paper. The predictive performance of the model was evaluated and compared with the EC4 code and original ANN based on a dataset of 227 experiments, and a graphical user interface (GUI) was developed to achieve the automatic output of the results. The influence of each design parameter on the bearing capacity was analyzed and quantified using the Shapley additive explanation (SHAP) method and sensitivity analysis. The results show that the prediction performance of the PSO-ANN model is superior, and can be recommended as a candidate for the prediction of axial compression bearing capacity of the CCFST column in terms of performance indices. Shapley additive explanation-based parameter analysis indicated that the diameter and thickness of the steel tube are the most two important parameters to the bearing capacity; in particular, the fluctuation of the diameter under the stochastic environment leads to the variation of the axial compression bearing capacity beyond the diameter itself.

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“…For instance, in [10] backpropagation (BP) neural network (NN) is applied to the concrete compressive strength dataset to automate concrete strength analysis. Chen et al [11] employed a bagging classifier and developed an automated concrete compressive strength prediction. The evaluation of the predictive accuracy of the developed model reveals that the artificial neural network outperforms compared decision tree and bagging classifier for concrete strength prediction.…”

Section: Related Workmentioning

confidence: 99%

Estimation of concrete compression using regression models

Assegie¹,

Salau

Badrudeen

2022

Bulletin EEI

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The objective of this study is to evaluate the effectiveness of different regression models in concrete compressive strength estimation. A concrete compressive strength dataset is employed for the estimation of the regressor models. Regression models such as linear regressor, ridge regressor, k-neighbors regressor, decision tree regressor, random forest regressor, gradient boosting regressor, AdaBoost regressor, and support vector regressor are used for developing the model that predicts the concrete strength. Cross-validation techniques and grid search are used to tune the parameters for better model performance. Python 3.8 programming language is used to conduct the experiment. The Performance evaluation result reveals that the gradient boosting regressor has better performance as compared to other models using root mean square error (RMSE).

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Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms

Cited by 49 publications

References 34 publications

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Axial Compression Prediction and GUI Design for CCFST Column Using Machine Learning and Shapley Additive Explanation

Estimation of concrete compression using regression models

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