Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization Algorithms
The Pile settlement (PS) is one of the most essential issues in designing piles and its foundation type applied in real state. Over the variants in designing the pile penetrated in rock, the vertical settlement is of paramount importance to know. However, rigorous theoretical descriptions for soil-p...
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Bilijipub publisher
2022-10-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_158270_3781b465302699953e36f47eef3f2ed2.pdf |
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| author | Augustinus Sieck Graciela Daniels |
| author_facet | Augustinus Sieck Graciela Daniels |
| author_sort | Augustinus Sieck |
| collection | DOAJ |
| description | The Pile settlement (PS) is one of the most essential issues in designing piles and its foundation type applied in real state. Over the variants in designing the pile penetrated in rock, the vertical settlement is of paramount importance to know. However, rigorous theoretical descriptions for soil-pile interactions are still ambiguous. In this regard, most research has tried to figure out the subsidence rate in piles after loading overtime via artificial intelligence methods. The Artificial Neural Network, as a widespread method, has absorbed attention to draw the actual picture of pile movement vertically during the loading period. This research aims to develop the Multilayer Perceptron coupled with the Novel Arithmetic Optimization Algorithm and Biogeography-Based Optimization) to find out the optimal number of hidden layers of neurons within MLP. The Klang Valley Mass Rapid Transit network built in Kuala Lumpur, Malaysia, was chosen to test the piles' settlement and earth properties algorithms. In the prediction process, the R2 value of MLP-AOA and MLP-BBO were obtained at 0.93 and 0.94, respectively. The measured range of piles movement was from 4.5 to 20 centimeters, which predicted settlements showed us an average one percent change compared to measured magnitudes. |
| format | Article |
| id | doaj-art-6aaddd30a4644cd9bbf22e833ce09aa8 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-6aaddd30a4644cd9bbf22e833ce09aa82025-02-12T08:46:30ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632022-10-0100103274110.22034/aeis.2022.353518.1033158270Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization AlgorithmsAugustinus Sieck0Graciela Daniels1The King's School, Bujumbura, BurundiCentral Arizona College, Coolidge, Arizona, 85128, United StatesThe Pile settlement (PS) is one of the most essential issues in designing piles and its foundation type applied in real state. Over the variants in designing the pile penetrated in rock, the vertical settlement is of paramount importance to know. However, rigorous theoretical descriptions for soil-pile interactions are still ambiguous. In this regard, most research has tried to figure out the subsidence rate in piles after loading overtime via artificial intelligence methods. The Artificial Neural Network, as a widespread method, has absorbed attention to draw the actual picture of pile movement vertically during the loading period. This research aims to develop the Multilayer Perceptron coupled with the Novel Arithmetic Optimization Algorithm and Biogeography-Based Optimization) to find out the optimal number of hidden layers of neurons within MLP. The Klang Valley Mass Rapid Transit network built in Kuala Lumpur, Malaysia, was chosen to test the piles' settlement and earth properties algorithms. In the prediction process, the R2 value of MLP-AOA and MLP-BBO were obtained at 0.93 and 0.94, respectively. The measured range of piles movement was from 4.5 to 20 centimeters, which predicted settlements showed us an average one percent change compared to measured magnitudes.https://aeis.bilijipub.com/article_158270_3781b465302699953e36f47eef3f2ed2.pdfarithmetic optimization algorithmpile settlement estimationbiogeography-based optimizationmultilayer perceptron, artificial neural networkmalaysia |
| spellingShingle | Augustinus Sieck Graciela Daniels Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization Algorithms Advances in Engineering and Intelligence Systems arithmetic optimization algorithm pile settlement estimation biogeography-based optimization multilayer perceptron, artificial neural network malaysia |
| title | Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization Algorithms |
| title_full | Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization Algorithms |
| title_fullStr | Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization Algorithms |
| title_full_unstemmed | Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization Algorithms |
| title_short | Utilizing the Novel Developed MLP Techniques to Survey Pile Subsidence via Optimization Algorithms |
| title_sort | utilizing the novel developed mlp techniques to survey pile subsidence via optimization algorithms |
| topic | arithmetic optimization algorithm pile settlement estimation biogeography-based optimization multilayer perceptron, artificial neural network malaysia |
| url | https://aeis.bilijipub.com/article_158270_3781b465302699953e36f47eef3f2ed2.pdf |
| work_keys_str_mv | AT augustinussieck utilizingthenoveldevelopedmlptechniquestosurveypilesubsidenceviaoptimizationalgorithms AT gracieladaniels utilizingthenoveldevelopedmlptechniquestosurveypilesubsidenceviaoptimizationalgorithms |