Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete

Wu, Yanqi; Zhou, Yisong

doi:10.1016/j.conbuildmat.2022.127298

Cited by 119 publications

(34 citation statements)

References 46 publications

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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

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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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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

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“…Hybrid learning combines several methods, including classroom, computer-based, and online learning (Bai, 2022;Behzad et al, 2022;Simpson & Goodyear, 2022). Hybrid can also describe a learning method (Abebe et al, 2022;Wu & Zhou, 2022). With this in mind, one of the most significant consequences of the Covid -19 case in Indonesia is the transformation of online learning into hybrid learning.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Learning on Pencak Silat Sport in Higher Education: Students’ Perception and Issues

2023

JHETP

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This study aimed to evaluate the implementation of hybrid learning in sports education, especially in Pencak Silat sport at Mpu Kuturan State College Singaraja. The data was collected using a questionnaire, interview, and observation. The questionnaire results were analyzed quantitatively to determine students' perceptions of implementing hybrid learning. While the interview and observation results were analyzed qualitatively to identify the challenges during the hybrid learning implementation. This study found that students' perceptions were very positive and challenges in this hybrid learning were balancing between offline and online activities, participating in face-to-face learning activities, getting reliable internet connection and technological devices.

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“…Features are vital in terms of their relevance and contribution in a data-driven modeling framework as they critically affect the prediction of the target variables . Shapley Additive exPlanation (SHAP) is a framework that helps interpret an ML model effectively.…”

Section: Introductionmentioning

confidence: 99%

“…25 Features are vital in terms of their relevance and contribution in a data-driven modeling framework as they critically affect the prediction of the target variables. 37 Shapley Additive exPlanation (SHAP) is a framework that helps interpret an ML model effectively. It provides precise information on the relevance of each feature instance and the order of importance of features for target variable prediction.…”

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confidence: 99%

Multiobjective Bayesian Optimization Framework for the Synthesis of Methanol from Syngas Using Interpretable Gaussian Process Models

et al. 2022

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Methanol production has gained considerable interest on the laboratory and industrial scale as it is a renewable fuel and an excellent hydrogen energy storehouse. The formation of synthesis gas (CO/H 2 ) and the conversion of synthesis gas to methanol are the two basic catalytic processes used in methanol production. Machine learning (ML) approaches have recently emerged as powerful tools in reaction informatics. Inspired by these, we employ Gaussian process regression (GPR) to the model conversion of carbon monoxide (CO) and selectivity of the methanol product using data sets obtained from experimental investigations to capture uncertainty in prediction values. The results indicate that the proposed GPR model can accurately predict CO conversion and methanol selectivity as compared to other ML models. Further, the factors that influence the predictions are identified from the best GPR model employing "Shapley Additive exPlanations" (SHAP). After interpretation, the essential input features are found to be the inlet mole fraction of CO (Y(CO, in)) and the net inlet flow rate (Fin(nL/min)) for our best prediction GPR models, irrespective of our data sets. These interpretable models are employed for Bayesian optimization in a weighted multiobjective framework to obtain the optimal operating points, namely, maximization of both selectivity and conversion.

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Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete

Cited by 119 publications

References 46 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

Hybrid Learning on Pencak Silat Sport in Higher Education: Students’ Perception and Issues

Multiobjective Bayesian Optimization Framework for the Synthesis of Methanol from Syngas Using Interpretable Gaussian Process Models

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