Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches
Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron arti...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kardani, Navid [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Optimised machine learning algorithms |
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| Anmerkung: |
© Springer Nature Switzerland AG 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Geotechnical and geological engineering - Springer International Publishing, 1991, 38(2019), 2 vom: 29. Nov., Seite 2271-2291 |
|---|---|
| Übergeordnetes Werk: |
volume:38 ; year:2019 ; number:2 ; day:29 ; month:11 ; pages:2271-2291 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10706-019-01085-8 |
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| Katalog-ID: |
OLC2025981139 |
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| 520 | |a Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches. | ||
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| 650 | 4 | |a Bearing capacity of piles | |
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| 700 | 1 | |a Nazem, Majidreza |4 aut | |
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10.1007/s10706-019-01085-8 doi (DE-627)OLC2025981139 (DE-He213)s10706-019-01085-8-p DE-627 ger DE-627 rakwb eng 620 660 550 VZ 19,1 ssgn Kardani, Navid verfasserin aut Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature Switzerland AG 2019 Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches. Optimised machine learning algorithms Particle swarm optimisation algorithm Bearing capacity of piles Relative variable importance Zhou, Annan (orcid)0000-0001-5209-5169 aut Nazem, Majidreza aut Shen, Shui-Long aut Enthalten in Geotechnical and geological engineering Springer International Publishing, 1991 38(2019), 2 vom: 29. Nov., Seite 2271-2291 (DE-627)130993441 (DE-600)1081719-0 (DE-576)032852495 0960-3182 nnns volume:38 year:2019 number:2 day:29 month:11 pages:2271-2291 https://doi.org/10.1007/s10706-019-01085-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 38 2019 2 29 11 2271-2291 |
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10.1007/s10706-019-01085-8 doi (DE-627)OLC2025981139 (DE-He213)s10706-019-01085-8-p DE-627 ger DE-627 rakwb eng 620 660 550 VZ 19,1 ssgn Kardani, Navid verfasserin aut Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature Switzerland AG 2019 Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches. Optimised machine learning algorithms Particle swarm optimisation algorithm Bearing capacity of piles Relative variable importance Zhou, Annan (orcid)0000-0001-5209-5169 aut Nazem, Majidreza aut Shen, Shui-Long aut Enthalten in Geotechnical and geological engineering Springer International Publishing, 1991 38(2019), 2 vom: 29. Nov., Seite 2271-2291 (DE-627)130993441 (DE-600)1081719-0 (DE-576)032852495 0960-3182 nnns volume:38 year:2019 number:2 day:29 month:11 pages:2271-2291 https://doi.org/10.1007/s10706-019-01085-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 38 2019 2 29 11 2271-2291 |
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10.1007/s10706-019-01085-8 doi (DE-627)OLC2025981139 (DE-He213)s10706-019-01085-8-p DE-627 ger DE-627 rakwb eng 620 660 550 VZ 19,1 ssgn Kardani, Navid verfasserin aut Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature Switzerland AG 2019 Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches. Optimised machine learning algorithms Particle swarm optimisation algorithm Bearing capacity of piles Relative variable importance Zhou, Annan (orcid)0000-0001-5209-5169 aut Nazem, Majidreza aut Shen, Shui-Long aut Enthalten in Geotechnical and geological engineering Springer International Publishing, 1991 38(2019), 2 vom: 29. Nov., Seite 2271-2291 (DE-627)130993441 (DE-600)1081719-0 (DE-576)032852495 0960-3182 nnns volume:38 year:2019 number:2 day:29 month:11 pages:2271-2291 https://doi.org/10.1007/s10706-019-01085-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 38 2019 2 29 11 2271-2291 |
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10.1007/s10706-019-01085-8 doi (DE-627)OLC2025981139 (DE-He213)s10706-019-01085-8-p DE-627 ger DE-627 rakwb eng 620 660 550 VZ 19,1 ssgn Kardani, Navid verfasserin aut Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature Switzerland AG 2019 Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches. Optimised machine learning algorithms Particle swarm optimisation algorithm Bearing capacity of piles Relative variable importance Zhou, Annan (orcid)0000-0001-5209-5169 aut Nazem, Majidreza aut Shen, Shui-Long aut Enthalten in Geotechnical and geological engineering Springer International Publishing, 1991 38(2019), 2 vom: 29. Nov., Seite 2271-2291 (DE-627)130993441 (DE-600)1081719-0 (DE-576)032852495 0960-3182 nnns volume:38 year:2019 number:2 day:29 month:11 pages:2271-2291 https://doi.org/10.1007/s10706-019-01085-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 AR 38 2019 2 29 11 2271-2291 |
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620 660 550 VZ 19,1 ssgn Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches Optimised machine learning algorithms Particle swarm optimisation algorithm Bearing capacity of piles Relative variable importance |
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Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches |
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Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches |
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Kardani, Navid |
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Kardani, Navid Zhou, Annan Nazem, Majidreza Shen, Shui-Long |
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estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches |
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Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches |
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Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches. © Springer Nature Switzerland AG 2019 |
| abstractGer |
Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches. © Springer Nature Switzerland AG 2019 |
| abstract_unstemmed |
Abstract Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches. © Springer Nature Switzerland AG 2019 |
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