Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm
In order to improve the prediction accuracy of foundation pit deformation, an improved optimization algorithm of supply and demand-exponential power product foundation pit deformation prediction model (ISDO-EPP model) is proposed. Through six standard test functions and three application examples, t...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/7055693 |
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| _version_ | 1832556253698588672 |
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| author | Chuankui Jing Hao Wang Hongsong Li |
| author_facet | Chuankui Jing Hao Wang Hongsong Li |
| author_sort | Chuankui Jing |
| collection | DOAJ |
| description | In order to improve the prediction accuracy of foundation pit deformation, an improved optimization algorithm of supply and demand-exponential power product foundation pit deformation prediction model (ISDO-EPP model) is proposed. Through six standard test functions and three application examples, the optimization ability of the ISDO algorithm is verified, and the optimization results are compared with those of basic supply demand optimization algorithm (SDO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), moth swarm algorithm (MSA), and particle swarm optimization algorithm (PSO). Taking the settlement prediction of three foundation pits as an example, the delay time and embedding dimension of each case are determined by autocorrelation function method and false nearest neighbor method, and input and output vectors are constructed to train and predict each model. The results show that the search ability of the ISDO algorithm is better than that of SDO and other five algorithms, and the ISDO algorithm has better search accuracy, global search ability, and robustness. The absolute values of average relative errors of the ISDO-EPP model for three cases are 0.73%, 3.36%, and 1.33%, respectively, which are better than ISDO-SVM and ISDO-BP models. It shows that the ISDO algorithm can effectively optimize the parameters of the EPP model, and the ISDO-EPP model is feasible and effective for deformation prediction. |
| format | Article |
| id | doaj-art-ad031a283f35494d8b888ffa39287312 |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-ad031a283f35494d8b888ffa392873122025-02-03T05:45:57ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/70556937055693Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved AlgorithmChuankui Jing0Hao Wang1Hongsong Li2CCTEB Infrastructure Construction Investment Co., Ltd., Wuhan 430000, ChinaCCTEB Infrastructure Construction Investment Co., Ltd., Wuhan 430000, ChinaCCTEB Infrastructure Construction Investment Co., Ltd., Wuhan 430000, ChinaIn order to improve the prediction accuracy of foundation pit deformation, an improved optimization algorithm of supply and demand-exponential power product foundation pit deformation prediction model (ISDO-EPP model) is proposed. Through six standard test functions and three application examples, the optimization ability of the ISDO algorithm is verified, and the optimization results are compared with those of basic supply demand optimization algorithm (SDO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), moth swarm algorithm (MSA), and particle swarm optimization algorithm (PSO). Taking the settlement prediction of three foundation pits as an example, the delay time and embedding dimension of each case are determined by autocorrelation function method and false nearest neighbor method, and input and output vectors are constructed to train and predict each model. The results show that the search ability of the ISDO algorithm is better than that of SDO and other five algorithms, and the ISDO algorithm has better search accuracy, global search ability, and robustness. The absolute values of average relative errors of the ISDO-EPP model for three cases are 0.73%, 3.36%, and 1.33%, respectively, which are better than ISDO-SVM and ISDO-BP models. It shows that the ISDO algorithm can effectively optimize the parameters of the EPP model, and the ISDO-EPP model is feasible and effective for deformation prediction.http://dx.doi.org/10.1155/2021/7055693 |
| spellingShingle | Chuankui Jing Hao Wang Hongsong Li Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm Geofluids |
| title | Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm |
| title_full | Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm |
| title_fullStr | Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm |
| title_full_unstemmed | Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm |
| title_short | Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm |
| title_sort | deformation prediction of foundation pit based on exponential power product model of improved algorithm |
| url | http://dx.doi.org/10.1155/2021/7055693 |
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