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

Han, Bing; Wu, Yanqi; Liu, Lulu

doi:10.1002/suco.202100732

Cited by 17 publications

(8 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the sample data and network structure having been determined, the determination of initial weights and thresholds is the most important factor affecting the model training and prediction results. The random forest algorithm, genetic algorithm, ant colony algorithm, and particle swarm optimization algorithm provide new insights for seeking the best range of weights and thresholds [37][38][39][40].…”

Section: Pso-ann Neural Networkmentioning

confidence: 99%

“…Firstly, random numbers were used to simulate the experimental data according to the characteristics of the data set in Section 2, and the data requirements are shown in Table 3. To study the variation in axial compression bearing capacity caused by the variation of a single variable, a normally distributed random sample with a coefficient of variation of 10% was generated for each parameter by referring to relevant studies by other scholars [40,51]. The distribution of each parameter sample with its corresponding axial compression bearing capacity variation is shown in Figure 12.…”

Section: Quantification Of the Influence Degree Of Design Parameters ...mentioning

confidence: 99%

See 1 more Smart Citation

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

Liu

Zhou

2022

Buildings

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Pso-ann Neural Networkmentioning

confidence: 99%

Section: Quantification Of the Influence Degree Of Design Parameters ...mentioning

confidence: 99%

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

Liu

Zhou

2022

Buildings

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, soft computing represented by machine learning (ML) has made remarkable achievements in the prediction of rubber-concrete material performance, e.g., artificial neural networks (ANN) [ 12 , 13 ], support vector machine (SVM) [ 14 , 15 ], back-propagation neural network (BPNN) [ 16 , 17 ], extreme learning machine (ELM) [ 18 , 19 ], multi-layer perceptron (MLP) [ 20 , 21 ] and trees-based models [ 22 , 23 ]. Among the ML models, the random forest (RF) model has an excellent resistance to overfitting and a fitting ability in solving prediction problems [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material

et al. 2023

View full text Add to dashboard Cite

The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot of earthquake resistance, especially in tunnel engineering. In this study, the pelican optimization algorithm (POA) is improved using the Latin hypercube sampling (LHS) method and the Chaotic mapping (CM) method to optimize the random forest (RF) model for predicting the aseismic performance of a novel aseismic rubber-concrete material. Seventy uniaxial compression tests and seventy impact tests were conducted to quantify this aseismic material performance, i.e., strength and energy absorption properties and four other artificial intelligence models were generated to compare the predictive performance with the proposed hybrid RF models. The performance evaluation results showed that the LHSPOA-RF model has the best prediction performance among all the models for predicting the strength and energy absorption property of this novel aseismic concrete material in both the training and testing phases (R2: 0.9800 and 0.9108, VAF: 98.0005% and 91.0880%, RMSE: 0.7057 and 1.9128, MAE: 0.4461 and 0.7364; R2: 0.9857 and 0.9065, VAF: 98.5909% and 91.3652%, RMSE: 0.5781 and 1.8814, MAE: 0.4233 and 0.9913). In addition, the sensitive analysis results indicated that the rubber and cement are the most important parameters for predicting the strength and energy absorption properties, respectively. Accordingly, the improved POA-RF model not only is proven as an effective method to predict the strength and energy absorption properties of aseismic materials, but also this hybrid model provides a new idea for assessing other aseismic performances in the field of tunnel engineering.

show abstract

“…[15][16][17] When working with concrete materials, the cost and availability of local materials are the problems that lie in choosing the right material and estimating the mechanical properties of concrete. 18,19 Over the last two decades, many researchers have extensively used and developed predicting approaches through artificial intelligence (AI) for various engineering problems. With the recent improvement of AI, there is a trend to operate machine learning (ML) methods to estimate the mechanical properties of concrete.…”

Section: Introductionmentioning

confidence: 99%

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

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

Cited by 17 publications

References 64 publications

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

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

Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material

Optimal boosting method of HPC concrete compressive and tensile strength prediction

Contact Info

Product

Resources

About