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Zainab Hasan Ali

Bio: Zainab Hasan Ali is an academic researcher from University of Diyala. The author has contributed to research in topics: Computer science & Mean squared error. The author has an hindex of 4, co-authored 8 publications receiving 127 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: The developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error, and indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.
Abstract: Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have ...

63 citations

Journal ArticleDOI
TL;DR: The proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.
Abstract: The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R2) during the testing phase (R2 = 0.95), whereas the other comparable models generated relatively lower values of R2 ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

62 citations

Journal ArticleDOI
TL;DR: The hydrological process has a dynamic nature characterised by randomness and complex phenomena, and the application of machine learning (ML) models in forecasting river flow has grown rapidly.
Abstract: The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is ...

25 citations

Journal ArticleDOI
TL;DR: In this article, the capacity of deep learning neural network (DLNN) for shear strength prediction of Perfobond Rib Shear Strength Connector (PRSC) is investigated, and the proposed DLNN model is validated against support vector regression (SVR), ANN, and M5 tree model.
Abstract: Accurate and reliable prediction of Perfobond Rib Shear Strength Connector (PRSC) is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network (DLNN) for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression (SVR), artificial neural network (ANN), and M5 tree model. In the second scenario, a comparable AI model hybridized with genetic algorithm (GA) as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model.

12 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

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

Journal ArticleDOI
TL;DR: The proposed self-adaptive MARS-WCA model demonstrated a robust and significant data-intelligence mode for FCLC compressive strength prediction compared with the benchmark models and experimental formulations.

87 citations