Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete

Liang, Minxian; Chang, Ze; Zhi, Wei; Gan, Yidong; Schlangen, Erik; Šavija, Branko

doi:10.1016/j.cemconcomp.2021.104295

Cited by 164 publications

(57 citation statements)

References 77 publications

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“…First presented by Lloyd Shapley, it uses Shapley values to interpret the model's output 16 , 23 , 40 , 41 . The Shapley value of a feature is equal to the difference between the average prediction value of samples with and without this feature 42 . It measures the feature's importance in the model 43 , 44 .…”

Section: Methodsmentioning

confidence: 99%

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

Fatahi

Nasiri

Dadfar

et al. 2022

Sci Rep

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Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.

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Section: Methodsmentioning

confidence: 99%

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

Fatahi

Nasiri

Dadfar

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Therefore, CART will search through all possible values of all variables in the dataset for the best partition pair that minimizes the cost function of Eq. ( 4) [23]. The steps of BaggedCART can be summarized as follows:…”

Section: Bagged Cartmentioning

confidence: 99%

Ensemble Tree Machine Learning Models for Improvement of Eurocode 2 Creep Model Prediction

Daou

Raphaël

2022

Civil and Environmental Engineering

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Ensemble tree machine learning models have proved useful for solving poorly understood and complex problems. This paper aims to calibrate the Eurocode 2 creep model by inserting a correction coefficient to the model. The correction coefficient is calculated using ensemble tree (bagging and boosting) models. The results showed that the insertion of the correction coefficient obtained by both the bagging and boosting models into the Eurocode 2 model led to significantly higher prediction accuracy. These approaches may lead to a better performance prediction, thereby reducing the effect of the time-dependent deformation on concrete structures.

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“…Machine learning predictions would help design such complex materials. Indeed, artificial intelligence techniques have been successfully applied to several Civil Engineering problems such as concrete strength prediction [40,41], creep prediction [42][43][44], crack assessment in structures [45] or durability and microstructural properties such as surface chloride concentration [46] and and mechanical properties of stabilized soil [47,48]. Among the various techniques developed, ensemble machine learning algorithms applied to datasets with hundreds of data points have proved a good accuracy and robustness against overfitting risk, often associated to conventional techniques and neural networks.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning techniques for predicting autogenous shrinkage of concrete incorporating superabsorbent polymers and supplementary cementitious materials

Hilloulin

Tran

2022

Journal of Building Engineering

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Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete

Cited by 164 publications

References 77 publications

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

Ensemble Tree Machine Learning Models for Improvement of Eurocode 2 Creep Model Prediction

Using machine learning techniques for predicting autogenous shrinkage of concrete incorporating superabsorbent polymers and supplementary cementitious materials

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