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Afrah Abdulelah Hamzah Alwanas

Bio: Afrah Abdulelah Hamzah Alwanas is an academic researcher from University of Baghdad. The author has contributed to research in topics: Shear strength & Extreme learning machine. The author has an hindex of 2, co-authored 2 publications receiving 98 citations.

Papers
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Journal ArticleDOI
TL;DR: New hybrid artificial intelligence model called integrative support vector regression with firefly optimization algorithm (SVR-FFA) for shear strength prediction of steel fiber-reinforced concrete beam (SFRCB) is attempted.
Abstract: Recent developments on shear strength (Vf) of steel fiber-reinforced concrete beam (SFRCB) simulation have been shifted to the implementation of the computer aid advancements. The current study is attempted to explore new hybrid artificial intelligence (AI) model called integrative support vector regression with firefly optimization algorithm (SVR-FFA) for shear strength prediction of SFRCB. The developed hybrid predictive model is constructed using laboratory experimental data set gathered from the literature and belongs to the shear failure capacity. The related beam dimensional and concrete properties are utilized as input attributes to predict Vf. The proposed SVR-FFA model is validated against classical SVR model and eight empirical formulations obtained from published researches. The attained results of the proposed hybrid AI model exhibited a reliable resultant performance in terms of prediction accuracy. Based on the examined root-mean-square error (RMSE) and the correlation coefficient (R2) over the testing phase, SVR-FFA achieved (RMSE ≈ 0.25 MPa) and (R2 ≈ 0.96).

80 citations

Journal ArticleDOI
TL;DR: ELM model designated as a robust intelligence model can be developed for structural predesigned process and an alternative for empirical codes for structural engineering aspects.

51 citations


Cited by
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Journal ArticleDOI
TL;DR: Examination of several Machine Learning models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms are examined.

241 citations

Journal ArticleDOI
TL;DR: The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model, and it is deducted that the ELm-GWO model is capable of reaching superior performance indices in comparison with those of the other models.
Abstract: Compressive strength of concrete is one of the most determinant parameters in the design of engineering structures. This parameter is generally determined by conducting several tests at different ages of concrete in spite of the fact that such tests are not only costly but also time-consuming. As an alternative to these tests, machine learning (ML) techniques can be used to estimate experimental results. However, the dependence of compressive strength on different parameters in the fabrication of concrete makes the prediction problem challenging, especially in the case of concrete with partial replacements for cement. In this investigation, an extreme learning machine (ELM) is combined with a metaheuristic algorithm known as grey wolf optimizer (GWO) and a novel hybrid ELM-GWO model is proposed to predict the compressive strength of concrete with partial replacements for cement. To evaluate the performance of the ELM-GWO model, five of the most well-known ML models including an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), an extreme learning machine, a support vector regression with radial basis function (RBF) kernel (SVR-RBF), and another SVR with a polynomial function (Poly) kernel (SVR-Poly) are developed. Finally, the performance of the models is compared with each other. The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model. Also, it is deducted that the ELM-GWO model is capable of reaching superior performance indices in comparison with those of the other models.

185 citations

Journal ArticleDOI
TL;DR: In this review, each element of the predictive models and their corresponding treatment processes, including its pros and cons, are discussed thoroughly and several research directions, which could bridge the gap in the same domain are proposed and recommended on the basis of the identified research limitations.

111 citations

01 Jan 2001
TL;DR: In this paper, the authors present analytical models for the analysis of reinforced concrete joints strengthened with composite materials, which can provide equations for stresses and strains at various stages of the response until the ultimate capacity is reached, defined by concrete crushing of FRP (fiber reinforced polymer) failure due to fracture or debonding.
Abstract: The study presents analytical models for the analysis of RC (reinforced concrete) joints strengthened with composite materials. These models can provide equations for stresses and strains at various stages of the response until the ultimate capacity is reached, defined by concrete crushing of FRP (fiber reinforced polymer) failure due to fracture or debonding; solutions to the equations are obtained numerically. Useful information is provided by the models on the shear capacity of FRP-strengthened joints in terms of the quantity and configuration of the externally bonded reinforcement and may be used to design FRP jackets for poorly detailed beam-column joints. The paper illustrates the analytical procedure through a case study, and compares the analytical model with a series of test results and the agreement between theory and experiments is found satisfactory.

106 citations

Journal ArticleDOI
TL;DR: In this paper, machine learning is used for the first time to predict the compressive strength of PCM-integrated cementitious composites, and a dataset of 154 cement-based mixtures incorporating PCM microcapsules was assembled.

93 citations