Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement
Immunizing projects such as piled bridges entails considerations that ensure safety over the operation period. Pile Settlement (PS) which seems one of the most critical matters in constructional project failure, has attracted experts’ attention to be predicted before starting projects with piles. Th...
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Bilijipub publisher
2023-03-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_169078_a7de7ff3378c1c428202c7de432e1de4.pdf |
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| author | Ehsanolah Assareh Reza Poultangari |
| author_facet | Ehsanolah Assareh Reza Poultangari |
| author_sort | Ehsanolah Assareh |
| collection | DOAJ |
| description | Immunizing projects such as piled bridges entails considerations that ensure safety over the operation period. Pile Settlement (PS) which seems one of the most critical matters in constructional project failure, has attracted experts’ attention to be predicted before starting projects with piles. The variables to appraise the pile movement would help us determine the perspectives after and during loading. Theoretical ways to calculate the pile movement mathematically have been adopted to model the PS, mostly by using artificial intelligence (AI). This paper has aimed to estimate the pile settlement rates based on pile samples. For this reason, a new hybrid model containing a Radial Basis Function Neural Network (RBFNN) joining with Grey Wolf Optimization (GWO) and Ant Colony Optimization (ACO) were used in a framework. In fact, optimizers utilized for calculating the neuron number of hidden layer in RBFNN at optimal level. In Malaysia, the Kuala Lumpur transportation network was investigated to examine the pile movement based on ground conditions and properties through the developed hybrid RBF-GWO and RBF-ACO algorithm. Evaluating each framework’s performance was done via the indices. So, the RMSEs of RBF-GWO and RBF-ACO reached values 0.5176 and 0.6562, respectively, and the MAE showed the rates 0.2583 and 0.3386, respectively. The correlation R-value also showed the RBF-GWO suitable accuracy with 1.23 percent higher than another model. Therefore, results have implied the RBF-GWO desirable performance to estimate PS. |
| format | Article |
| id | doaj-art-9e2d5efe70c143058d32182df4b33b9b |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Bilijipub publisher |
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| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-9e2d5efe70c143058d32182df4b33b9b2025-02-12T08:47:02ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632023-03-0100201243710.22034/aeis.2023.375382.1061169078Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile SettlementEhsanolah Assareh0Reza Poultangari1School of Chemical Engineering, Yeungnam University, Gyeongsan, 38541, South KoreaDepartment of Mechanical Engineering, Islamic Azad University Dezful Branch, Dezful, Khuzestan, 6461646411, IranImmunizing projects such as piled bridges entails considerations that ensure safety over the operation period. Pile Settlement (PS) which seems one of the most critical matters in constructional project failure, has attracted experts’ attention to be predicted before starting projects with piles. The variables to appraise the pile movement would help us determine the perspectives after and during loading. Theoretical ways to calculate the pile movement mathematically have been adopted to model the PS, mostly by using artificial intelligence (AI). This paper has aimed to estimate the pile settlement rates based on pile samples. For this reason, a new hybrid model containing a Radial Basis Function Neural Network (RBFNN) joining with Grey Wolf Optimization (GWO) and Ant Colony Optimization (ACO) were used in a framework. In fact, optimizers utilized for calculating the neuron number of hidden layer in RBFNN at optimal level. In Malaysia, the Kuala Lumpur transportation network was investigated to examine the pile movement based on ground conditions and properties through the developed hybrid RBF-GWO and RBF-ACO algorithm. Evaluating each framework’s performance was done via the indices. So, the RMSEs of RBF-GWO and RBF-ACO reached values 0.5176 and 0.6562, respectively, and the MAE showed the rates 0.2583 and 0.3386, respectively. The correlation R-value also showed the RBF-GWO suitable accuracy with 1.23 percent higher than another model. Therefore, results have implied the RBF-GWO desirable performance to estimate PS.https://aeis.bilijipub.com/article_169078_a7de7ff3378c1c428202c7de432e1de4.pdfpile settlementradial basis functiongrey wolf optimizationant colony optimizationrmse |
| spellingShingle | Ehsanolah Assareh Reza Poultangari Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement Advances in Engineering and Intelligence Systems pile settlement radial basis function grey wolf optimization ant colony optimization rmse |
| title | Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement |
| title_full | Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement |
| title_fullStr | Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement |
| title_full_unstemmed | Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement |
| title_short | Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement |
| title_sort | investigating the two optimization algorithms gwo and aco coupling with radial basis neural network to estimate the pile settlement |
| topic | pile settlement radial basis function grey wolf optimization ant colony optimization rmse |
| url | https://aeis.bilijipub.com/article_169078_a7de7ff3378c1c428202c7de432e1de4.pdf |
| work_keys_str_mv | AT ehsanolahassareh investigatingthetwooptimizationalgorithmsgwoandacocouplingwithradialbasisneuralnetworktoestimatethepilesettlement AT rezapoultangari investigatingthetwooptimizationalgorithmsgwoandacocouplingwithradialbasisneuralnetworktoestimatethepilesettlement |