Evaluation of the mechanical behavior of high-performance concrete (HPC) reinforced with 3D-Printed trusses

Chiadighikaobi, Paschal Chimeremeze; Hasanzadeh, Ali; Hematibahar, Mohammad; Kharun, Makhmud; Mousavi, Mir Sharif; Stashevskaya, Nadezhda A.; Adegoke, Muritala Adedapo

doi:10.1016/j.rineng.2024.102058

Results in Engineering

2024

DOI: 10.1016/j.rineng.2024.102058

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Evaluation of the mechanical behavior of high-performance concrete (HPC) reinforced with 3D-Printed trusses

Paschal Chimeremeze Chiadighikaobi,

Ali Hasanzadeh,

Mohammad Hematibahar

et al.

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Cited by 4 publications

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Combined mechanical couplers and special-length-priority algorithm for reducing rebar consumption and cutting waste of beam reinforcement

Widjaja,

Park,

Choi

et al. 2024

Results in Engineering

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Combined mechanical couplers and special-length-priority algorithm for reducing rebar consumption and cutting waste of beam reinforcement

Widjaja,

Park,

Choi

et al. 2024

Results in Engineering

View full text Add to dashboard Cite

Concurrent multiscale topology optimization for size-dependent structures incorporating multiple-phase materials

Zhao,

Gan

2024

Results in Engineering

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Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning

Hematibahar,

Kharun,

Beskopylny

et al. 2024

J. Compos. Sci.

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High-Performance Concrete (HPC) and Ultra-High-Performance Concrete (UHPC) have many applications in civil engineering industries. These two types of concrete have as many similarities as they have differences with each other, such as the mix design and additive powders like silica fume, metakaolin, and various fibers, however, the optimal percentages of the mixture design properties of each element of these concretes are completely different. This study investigated the differences and similarities between these two types of concrete to find better mechanical behavior through mixture design and parameters of each concrete. In addition, this paper studied the correlation matrix through the machine learning method to predict the mechanical properties and find the relationship between the concrete mix design elements and the mechanical properties. In this way, Linear, Ridge, Lasso, Random Forest, K-Nearest Neighbors (KNN), Decision tree, and Partial least squares (PLS) regressions have been chosen to find the best regression types. To find the accuracy, the coefficient of determination (R2), mean absolute error (MAE), and root-mean-square error (RMSE) were selected. Finally, PLS, Linear, and Lasso regressions had better results than other regressions, with R2 greater than 93%, 92%, and 92%, respectively. In general, the present study shows that HPC and UHPC have different mix designs and mechanical properties. In addition, PLS, Linear, and Lasso regressions are the best regressions for predicting mechanical properties.

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Evaluation of the mechanical behavior of high-performance concrete (HPC) reinforced with 3D-Printed trusses

Cited by 4 publications

References 32 publications

Combined mechanical couplers and special-length-priority algorithm for reducing rebar consumption and cutting waste of beam reinforcement

Combined mechanical couplers and special-length-priority algorithm for reducing rebar consumption and cutting waste of beam reinforcement

Concurrent multiscale topology optimization for size-dependent structures incorporating multiple-phase materials

Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning

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