Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils

By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP)...

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Veröffentlicht in:Engineering with computers Jg. 37; H. 1; S. 437 - 447
Hauptverfasser: Moayedi, Hossein, Abdullahi, Mu’azu Mohammed, Nguyen, Hoang, Rashid, Ahmad Safuan A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.01.2021
Springer Nature B.V
Schlagworte:
Accuracy
Bearing capacity
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Civil engineering
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Data mining
Evolutionary algorithms
Friction
Genetic algorithms
Heuristic methods
Machine learning
Math. Applications in Chemistry
Mathematical analysis
Mathematical and Computational Engineering
Modulus of elasticity
Multilayers
Neural networks
Optimization
Optimization techniques
Original Article
Poisson's ratio
R&D
Research & development
Research methodology
Soil bearing capacity
Soil layers
Soils
Systems Theory
Dragonfly algorithm
Bearing capacity analysis
Evolutionary science
ISSN:0177-0667, 1435-5663
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Zusammenfassung:By assist of novel evolutionary science, the classification accuracy of neural computing is improved in analyzing the bearing capacity of footings over two-layer foundation soils. To this end, Harris hawks optimization (HHO) and dragonfly algorithm (DA) are applied to a multi-layer perceptron (MLP) predictive tool for adjusting the connecting weights and biases in predicting the failure probability using seven settlement key factors, namely unit weight, friction angle, elastic modulus, dilation angle, Poisson’s ratio, applied stress, and setback distance. As the first result, incorporating both HHO and DA metaheuristic algorithms resulted in higher efficiency of the MLP. Moreover, referring to the calculated area under the receiving operating characteristic curve (AUC), as well as the calculated mean square error, the DA-MLP (AUC = 0.942 and MSE = 0.1171) outperforms the HHO-MLP (AUC = 0.915 and MSE = 0.1350) and typical MLP (AUC = 0.890 and MSE = 0.1416). Furthermore, the DA surpassed the HHO in terms of time-effectiveness.
Bibliographie:ObjectType-Article-1
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ObjectType-Feature-2
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-019-00834-w