Machine learning models for cracking torque and pre‑cracking stiffness of RC beams

• Autorzy: Shenggang Chen , Quanquan Guo , Yingying Zhang , Hexiang Hu , Bei Shen

Identyfikatory

DOI

10.1007/s43452-022-00541-2

• Języki publikacji: EN

Abstrakty

EN

The torsional behavior of RC beams is a complex work involving interactions of different design parameters and mechanisms. Considering the limitations and lower accuracy of traditional calculation theories, two machine learning models, including artificial neural network (ANN) model and random forest (RF) model, were applied for the frst time to predict the cracking torque and initial or pre-cracking torsional stiffness of RC beams. A comprehensive database consisting 159 experimental results of RC beams with solid or hollow sections was compiled, with input variables including dimension parameters of cross-section, compressive stress of concrete, elastic modulus and strength ratio of reinforcements. The performance of the models was appraised by various statistical estimators and safety ratio, and compared with different theories for cracking torque and initial stiffness. Among all the calculation models, RF model achieved the best overall prediction performance with the highest coeffcient of determination (R²=0.985 for cracking torque and R²=0.978 for initial stifness) and lowest root-mean-square error (RMSE=5.867 for cracking torque and RMSE=3.994 for initial stiffness). However, theories for cracking torque, i.e., plastic theory, Bredt thin-tube theory and skew-bending theory, gave huge underestimation, whereas greatly exaggerated initial stiffness was obtained by elastic theory and simplified soften membrane model for torsion theory. Besides, input variable importance analysis was conducted, revealing that dimension parameters of cross-section were the most critical features to decide prediction performance for pre-cracking torsional performance of RC beams. The achievements of this paper may provide references to the establishment of new predicting model for pre-cracking torsional response of RC beams.

Słowa kluczowe

EN

cracking torque; pre-cracking torsional stifness; RC beam; machine learning model

PL

moment obrotowy; belka żelbetowa; model uczenia maszynowego

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2023
• Tom: Vol. 23, no. 1
• Strony: art. no. e6, 2023
• Opis fizyczny: Bibliogr. 58 poz., rys., tab., wykr.

Twórcy

autor

Shenggang Chen

• Jiangsu Key Laboratory Environmental Impact and Structural Safety in Engineering, China University of Mining and Technology, Xuzhou 211116, Jiangsu Province, China
• School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 211116, Jiangsu Province, China

• https://orcid.org/0000-0002-6205-6267

autor

Quanquan Guo

• School of Transportation and Science Engineering, Beihang University, Beijing 100191, China

autor

Yingying Zhang

• Jiangsu Key Laboratory Environmental Impact and Structural Safety in Engineering, China University of Mining and Technology, Xuzhou 211116, Jiangsu Province, China
• School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 211116, Jiangsu Province, China

autor

Hexiang Hu

• China Construction First Group Corporation Limited, Beijing 100161, China

autor

Bei Shen

• China Academy of Building Research, Beijing City 100029, China

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)