Bond performance of SFRC considering random distributions of aggregates and steel fibers

Zhang, Wei; Lee, Deuck Hang; Lee, Changjun; Zhang, Xuhui; Ikechukwu, Ogwu

doi:10.1016/j.conbuildmat.2021.123304

Cited by 12 publications

(2 citation statements)

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“…4 The internal resistance mechanism in an RC member basically originates from two components: compressive resistance provided by concrete and its counterpart provided by reinforcements in tension. [5][6][7][8][9][10][11][12] For this reason, previous fatigue research focused on characterizing the strength degradation of concrete and steel materials under fatigue depending on various stress level and loading frequency. [13][14][15][16][17][18] These existing studies reveal that there are inter-related mechanisms among key variables including the compressive strength, aspect ratio of test specimen, maximum stress level, minimum stress to maximum stress ratio, and fatigue loading frequency.…”

Section: Introductionmentioning

confidence: 99%

“…The internal resistance mechanism in an RC member basically originates from two components: compressive resistance provided by concrete and its counterpart provided by reinforcements in tension 5–12 . For this reason, previous fatigue research focused on characterizing the strength degradation of concrete and steel materials under fatigue depending on various stress level and loading frequency 13–18 .…”

Section: Introductionmentioning

confidence: 99%

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Residual strength of concrete subjected to fatigue based on machine learning technique

Zhang

Lee

et al. 2021

Structural Concrete

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This study utilizes the machine learning technique to solve the complex fatigue problem of concrete materials. To this end, several learning algorithms were addressed including the random forest (RF), support vector machine (SVM), and artificial neural networks (ANNs) models. Extensive experimental data were collected from literature to train the machine learning models for estimating the maximum number of cycles at failure (i.e., the so-called fatigue life). A machine learning model providing the best correlation was chosen through verifications. On this basis, a strength degradation model of concrete under fatigue loading was finally proposed to evaluate the residual strength of concrete after fatigue damage, which is a key factor in determining the remaining service life of concrete structures.

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