Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models
The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In rece...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/5/1242 |
| _version_ | 1797600754177933312 |
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| author | Jiajun Ren Xianbin Sun |
| author_facet | Jiajun Ren Xianbin Sun |
| author_sort | Jiajun Ren |
| collection | DOAJ |
| description | The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In recent years, artificial neural networks (ANN) have been successfully applied to various types of complex issues in geotechnical engineering, among which the back-propagation (BP) method is a relatively mature and widely used algorithm. However, it has inevitable shortcomings, resulting in large prediction errors and other issues. Based on this situation, this study was designed to accomplish two tasks: firstly, using the genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the BP network. On this basis, the two optimization algorithms were improved to enhance the performance of the two optimization algorithms. Then, an adaptive genetic algorithm (AGA) and adaptive particle swarm optimization (APSO) were used to optimize a BP neural network to predict the ultimate bearing capacity of the pile foundation. Secondly, to test the performance of the two optimization models, the predicted results were compared and analyzed in relation to the traditional BP model and other network models of the same type in the literature based on the three most common statistical indicators. The models were evaluated using three common evaluation metrics, namely the coefficient of determination (R<sup>2</sup>), value account for (VAF), and the root mean square error (RMSE), and the evaluation metrics for the test set were obtained as AGA-BP (0.9772, 97.8348, 0.0436) and APSO-BP (0.9854, 98.4732, 0.0332). The results show that compared with the predicted results of the BP model and other models, the test set of the AGA-BP model and APSO-BP model achieved higher accuracy, and the APSO-BP model achieved higher accuracy and reliability, which provides a new method for the prediction of the ultimate bearing capacity of pile foundations. |
| first_indexed | 2024-03-11T03:52:22Z |
| format | Article |
| id | doaj.art-13e92bba45e14dd9bc550d4db3e56d6f |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T03:52:22Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-13e92bba45e14dd9bc550d4db3e56d6f2023-11-18T00:45:32ZengMDPI AGBuildings2075-53092023-05-01135124210.3390/buildings13051242Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm ModelsJiajun Ren0Xianbin Sun1School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, ChinaSchool of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, ChinaThe determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In recent years, artificial neural networks (ANN) have been successfully applied to various types of complex issues in geotechnical engineering, among which the back-propagation (BP) method is a relatively mature and widely used algorithm. However, it has inevitable shortcomings, resulting in large prediction errors and other issues. Based on this situation, this study was designed to accomplish two tasks: firstly, using the genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the BP network. On this basis, the two optimization algorithms were improved to enhance the performance of the two optimization algorithms. Then, an adaptive genetic algorithm (AGA) and adaptive particle swarm optimization (APSO) were used to optimize a BP neural network to predict the ultimate bearing capacity of the pile foundation. Secondly, to test the performance of the two optimization models, the predicted results were compared and analyzed in relation to the traditional BP model and other network models of the same type in the literature based on the three most common statistical indicators. The models were evaluated using three common evaluation metrics, namely the coefficient of determination (R<sup>2</sup>), value account for (VAF), and the root mean square error (RMSE), and the evaluation metrics for the test set were obtained as AGA-BP (0.9772, 97.8348, 0.0436) and APSO-BP (0.9854, 98.4732, 0.0332). The results show that compared with the predicted results of the BP model and other models, the test set of the AGA-BP model and APSO-BP model achieved higher accuracy, and the APSO-BP model achieved higher accuracy and reliability, which provides a new method for the prediction of the ultimate bearing capacity of pile foundations.https://www.mdpi.com/2075-5309/13/5/1242pile foundationultimate bearing capacityAGA-BPAPSO-BPoptimization algorithm |
| spellingShingle | Jiajun Ren Xianbin Sun Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models Buildings pile foundation ultimate bearing capacity AGA-BP APSO-BP optimization algorithm |
| title | Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models |
| title_full | Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models |
| title_fullStr | Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models |
| title_full_unstemmed | Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models |
| title_short | Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models |
| title_sort | prediction of ultimate bearing capacity of pile foundation based on two optimization algorithm models |
| topic | pile foundation ultimate bearing capacity AGA-BP APSO-BP optimization algorithm |
| url | https://www.mdpi.com/2075-5309/13/5/1242 |
| work_keys_str_mv | AT jiajunren predictionofultimatebearingcapacityofpilefoundationbasedontwooptimizationalgorithmmodels AT xianbinsun predictionofultimatebearingcapacityofpilefoundationbasedontwooptimizationalgorithmmodels |