Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

Rahman, Jesika; Ahmed, Khondaker Sakil; Khan, Nafiz Imtiaz; Islam, Kamrul; Mangalathu, Sujith

doi:10.1016/j.engstruct.2020.111743

Cited by 185 publications

(51 citation statements)

References 80 publications

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“…(2018), Huang and Burton (2019), Rahman et al (2021) and Zhang et al (2019). The RF model has been improved with a BAS algorithm to predict the CS of RAC that contains waste of rubbers by Sun et al (2019).…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Ahmed

Ali

2022

ACT

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Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Ahmed

Ali

2022

ACT

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“…The research shows that steel fiber can enhance the shear strength of members and the tensile strength of the concrete matrix [71][72][73]. Hence, V cf can be expressed by…”

Section: Ultimate Shear Capacity Of Sfrc-bcjsmentioning

confidence: 99%

Numerical Simulation on Seismic Behavior of Steel Fiber Reinforced Concrete Beam—Column Joints

Shi

Zhu

et al. 2021

Materials

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Steel fiber reinforced concrete (SFRC) is a novel material of concrete, which has a great potential to be used in practical engineering. Based on the finite element software Opensees, the main objective of this paper presented a numerical simulation method on investigating the seismic behavior of SFRC–beam-column joints (BCJs) through modifying the calculation method of joint shear and longitudinal reinforcement slip deformations. The feasibility and accuracy of the numerical modeling method were verified by comparing the computed results with experimental data in terms of the hysteresis curves, skeleton curves, feature points, energy dissipation, and stiffness degradation. And then, the influences of some key parameters on the seismic behavior of BCJs were investigated and discussed in detail. The parametric studies clearly illustrated that both adding the steel fiber and increasing the stirrup amount of joint core area could significantly improve the seismic behavior of BCJs. The axial compression ratio had limited influence on the seismic behavior of BCJs. Finally, based on the main factors (steel fiber volume ratio, stirrup amount, and axial compression ratio), a formula for predicting ultimate shear capacity is derived.

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“…Artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), gene expression programming (GEP), random forest (RF), and deep learning (DL) are widely used prediction techniques in case of mechanical properties of concrete [67]. The shear strength of steel fibers reinforced concrete beams was predicted with the help of eleven algorithms by Rahman, et al [68]. ANN with optimizer as multi-objective grey wolves (MOGW) was used by Behnood and Golafshani [69] for predicting the static properties of silica fume modified concrete.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms

et al. 2022

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Recently, the high demand for marble stones has progressed in the construction industry, ultimately resulting in waste marble production. Thus, environmental degradation is unavoidable because of waste generated from quarry drilling, cutting, and blasting methods. Marble waste is produced in an enormous amount in the form of odd blocks and unwanted rock fragments. Absence of a systematic way to dispose of these marble waste massive mounds results in environmental pollution and landfills. To reduce this risk, an effort has been made for the incorporation of waste marble powder into concrete for sustainable construction. Different proportions of marble powder are considered as a partial substitute in concrete. A total of 40 mixes are prepared. The effectiveness of marble in concrete is assessed by comparing the compressive strength with the plain mix. Supervised machine learning algorithms, bagging (Bg), random forest (RF), AdaBoost (AdB), and decision tree (DT) are used in this study to forecast the compressive strength of waste marble powder concrete. The models’ performance is evaluated using correlation coefficient (R2), root mean square error, and mean absolute error and mean square error. The achieved performance is then validated by using the k-fold cross-validation technique. The RF model, having an R2 value of 0.97, has more accurate prediction results than Bg, AdB, and DT models. The higher R2 values and lesser error (RMSE, MAE, and MSE) values are the indicators for better performance of RF model among all individual and ensemble models. The implementation of machine learning techniques for predicting the mechanical properties of concrete would be a practical addition to the civil engineering domain by saving effort, resources, and time.

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Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

Cited by 185 publications

References 80 publications

Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Numerical Simulation on Seismic Behavior of Steel Fiber Reinforced Concrete Beam—Column Joints

Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms

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