Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model

Milad, Abdalrhman; Hussein, Sadaam Hadee; Khekan, Ahlam R.; Rashid, Mohammed; Al-Msari, Haitham; Tran, Tan Huy

doi:10.1007/s00366-021-01398-4

Cited by 41 publications

(15 citation statements)

References 62 publications

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“…The model was validated, and its applicability was verified using the available data from other locations [35]. In recent years, researchers have shown increasing interest in using artificial intelligence techniques, for example, machine learning (ML) and deep learning, to analyse and predict pavement temperature parameters [4,36]. In 2014, a study in Serbia used artificial neural networks to develop models for predicting the minimum and maximum pavement temperatures based on the pavement's surface temperature and depth [23].…”

Section: B Literature Reviewmentioning

confidence: 97%

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Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

Milad¹,

Adwan

Majeed³

et al. 2021

IEEE Access

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Section: B Literature Reviewmentioning

confidence: 97%

“…The training set is made up of about two-thirds of the original dataset. However, one-third of the dataset was not a part of each RF, and the bootstrap sample data were not plotted as a graph in the training progress [36]. This portion of the dataset is the untrained data.…”

Section: B Random Forestmentioning

confidence: 99%

Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

Milad¹,

Adwan

Majeed³

et al. 2021

IEEE Access

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“…Their research provided technical results for enhancing the ANN’s performance by applying the mentioned optimization techniques. Abdalrhman Milad et al [ 33 ] presented three methods of ensemble Machine Learning (ML) for FRP strain prediction, including the effective parameters such as strength properties, material geometry, FRP properties, strain properties, and confinement properties. These parameters provided five input combinations to predict the strain enhancement ratio of FRP composites.…”

Section: Introductionmentioning

confidence: 99%

Structural Performance of EB-FRP-Strengthened RC T-Beams Subjected to Combined Torsion and Shear Using ANN

et al. 2022

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This research study applied Artificial Neural Networks (ANNs) to predict and evaluate the structural responses of externally bonded FRP (EB-FRP)-strengthened RC T-beams under combined torsion and shear. Previous studies proved that, compared to reinforced concrete (RC) rectangular beams, RC T-beams performance in shear is significantly higher in structural analysis and design. The structural response of RC beams experiences a critical change while torsion moments are applied in load conditions. Fiber Reinforced Polymer (FRP) is used to retrofit the structural elements due to changing structural design codes and loadings, especially in earthquake-prone countries. We applied Finite Element Method (FEM) software, ABAQUS, to provide a precise numerical database of a set of experimentally tested FRP-retrofitted RC T-beams in previous research works. ANN predicted structural analysis results and Mean Square Error (MSE) and Multiple Determination Coefficients proved the accuracy of this study. The MSE values that were less than 0.0009 and values greater than 0.9960 showed that the ANN precisely fits the data. The consistency between analyzed experimental and numerical results demonstrated the accurate implication of ANN, MSE, and in predicting the structural responses of EB-FRP- strengthened RC T-beams.

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“…Recently, machine learning (ML) techniques have risen to prominence in the field of material science [15,16]. Large databases with various features are trained using ML algorithms to obtain the models with a strong generalization ability and high accuracy when assessing the material properties of composites.…”

Section: Introductionmentioning

confidence: 99%

Data-driven modeling for thermo-elastic properties of vacancy-defective graphene reinforced nanocomposites with its application to functionally graded beams

Zhao

Zhang

et al. 2022

Engineering with Computers

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The presence of unavoidable defects in the form of atom vacancies in graphene sheets considerably deteriorates the thermo-elastic properties of graphene-reinforced nanocomposites. Since none of the existing micromechanics models is capable of capturing the effect of vacancy defect, accurate prediction of the mechanical properties of these nanocomposites poses a great challenge. Based on molecular dynamics (MD) databases and genetic programming (GP) algorithm, this paper addresses this key issue by developing a data-driven modeling approach which is then used to modify the existing Halpin–Tsai model and rule of mixtures by taking vacancy defects into account. The data-driven micromechanics models can provide accurate and efficient predictions of thermo-elastic properties of defective graphene-reinforced Cu nanocomposites at various temperatures with high coefficients of determination (R2 > 0.9). Furthermore, these well-trained data-driven micromechanics models are employed in the thermal buckling, elastic buckling, free vibration, and static bending analyses of functionally graded defective graphene reinforced composite beams, followed by a detailed parametric study with a particular focus on the effects of defect percentage, content, and distribution pattern of graphene as well as temperature on the structural behaviors.

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Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model

Cited by 41 publications

References 62 publications

Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction

Structural Performance of EB-FRP-Strengthened RC T-Beams Subjected to Combined Torsion and Shear Using ANN

Data-driven modeling for thermo-elastic properties of vacancy-defective graphene reinforced nanocomposites with its application to functionally graded beams

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