Citation: Chen, H.; Sun, Z.; Zhong, Z.;
Huang, Y. Fatigue Factor Assessment
and Life Prediction of Concrete Based
on Bayesian Regularized BP Neural
Network. Materials 2022, 15, 4491.
https://doi.org/10.3390/ma15134491
Academic Editors: Alberto
Campagnolo and Dario
De Domenico
Received: 16 May 2022
Accepted: 22 June 2022
Published: 25 June 2022
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Article
Fatigue Factor Assessment and Life Prediction of Concrete
Based on Bayesian Regularized BP Neural Network
Huating Chen
1,
* , Zhenyu Sun
1
, Zefeng Zhong
2
and Yan Huang
1,
*
1
Faculty of Urban Construction, Beijing University of Technology, Beijing 100124, China; 15725701865@163.com
2
Baobida IOT Technology (Suzhou) Co., Ltd., Suzhou 200041, China; zhongzefengwy@163.com
*
Correspondence: chenhuating@bjut.edu.cn (H.C.); hybridge@bjut.edu.cn (Y.H.); Tel.: +86-13301137705 (H.C.)
Abstract:
Concrete tensile properties usually govern the fatigue cracking of structural components
such as bridge decks under repetitive loading. A fatigue life reliability analysis of commonly used
ordinary cement concrete is desirable. As fatigue is affected by many interlinked factors whose
effect is nonlinear, a unanimous consensus on the quantitative measurement of these factors has
not yet been achieved. Benefiting from its unique self-learning ability and strong generalization
capability, the Bayesian regularized backpropagation neural network (BR-BPNN) was proposed to
predict concrete behavior in tensile fatigue. A total of 432 effective data points were collected from the
literature, and an optimal model was determined with various combinations of network parameters.
The average relative impact value (ARIV) was constructed to evaluate the correlation between fatigue
life and its influencing parameters (maximum stress level Smax, stress ratio R, static strength f,
failure probability P). ARIV results were compared with other factor assessment methods (weight
equation and multiple linear regression analyses). Using BR-BPNN, S-N curves were obtained for
the combinations of R = 0.1, 0.2, 0.5; f = 5, 6, 7 MPa; P = 5%, 50%, 95%. The tensile fatigue results
under different testing conditions were finally compared for compatibility. It was concluded that
Smax had the most significant negative effect on fatigue life; and the degree of influence of R, P,
and f, which positively correlated with fatigue life, decreased successively. ARIV was confirmed
as a feasible way to analyze the importance of parameters and could be recommended for future
applications. It was found that the predicted logarithmic fatigue life agreed well with the test results
and conventional data fitting curves, indicating the reliability of the BR-BPNN model in predicting
concrete tensile fatigue behavior. These probabilistic fatigue curves could provide insights into
fatigue test program design and fatigue evaluation. Since the overall correlation coefficient between
the prediction and experimental results reached 0.99, the experimental results of plain concrete under
flexural tension, axial tension, and splitting tension could be combined in future analyses. Besides
utilizing the valuable fatigue test data available in the literature, this work provided evidence of
the successful application of BR-BPNN on concrete fatigue prediction. Although a more accurate
and comprehensive method was derived in the current study, caution should still be exercised when
utilizing this method.
Keywords:
concrete tensile fatigue; backpropagation neural networks; Bayesian regularization;
quantitative assessment; influencing factors; fatigue life prediction; average relative impact value
1. Introduction
Bridge decks, highway pavements, and railway sleepers are structural components
subjected to numerous repetitions of bending load cycles during their entire service life.
Fatigue failure may occur even when the loads on the structures are lower than their
strength under static loading. Since concrete is primarily utilized with its compressive
strength, much attention has been devoted to compressive fatigue performance. With tensile
strength significantly lower than compressive strength, concrete’s tensile behavior controls
the fatigue cracking of concrete structures and plays a vital role in concrete durability.
Materials 2022, 15 , 4491. https://doi.org/10.3390/ma15134491 https://www.mdpi.com/journal/materials
