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Accuracy Evaluation of Machine Learning Model for Concrete Aging Prediction due to Thermal Effect and Carbonation

콘크리트 탄산화 및 열효과에 의한 경년열화 예측을 위한 기계학습 모델의 정확성 검토

  • Kim, Hyun-Su (Division of Architecture, Sunmoon University)
  • Received : 2023.11.13
  • Accepted : 2023.11.22
  • Published : 2023.12.15

Abstract

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

Keywords

Acknowledgement

본 논문은 2022년도 가동원전 안전성향상 핵심기술개발사업의 지원으로 수행되고 있는 과제(과제번호: 20224B10200080) 내용의 일부입니다. 산업통상자원부와 한국에너지기술평가원의 연구비 지원에 깊은 감사를 드립니다.

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