Matches in SemOpenAlex for { <https://semopenalex.org/work/W4224041885> ?p ?o ?g. }
- W4224041885 endingPage "25" @default.
- W4224041885 startingPage "1" @default.
- W4224041885 abstract "Pile foundations are widely used for high-rise structures constructed in soft ground. The bearing capacity of pile is a crucial parameter required during the design and construction phase of pile foundation engineering projects. In practice, accurate predictions of pile bearing capacity are challenging due to a complex interplay of various geotechnical engineering factors including pile characteristics and ground conditions. This study proposes a data-driven model for coping with the problem of interest that hybridizes machine learning and metaheuristic approaches. Least squares support vector regression (LSSVR) is used for analyzing a dataset containing historical records of pile tests. Based on such datasets, LSSVR is capable of generalizing a multivariate function that estimates values of pile bearing capacity based on a set of variables describing pile characteristics and ground conditions. Moreover, opposition-based differential flower pollination (ODFP) metaheuristic is proposed to optimize the LSSVR learning process. Experimental results supported by the statistical test showed that the proposed ODFP-optimized LSSVR can achieve a good predictive performance in terms of root mean square error, mean absolute percentage error mean absolute error, and coefficient of determination. These results confirm that the ODFP-optimized LSSVR can be a potential alternative to assist civil engineers in the task of pile bearing capacity estimation." @default.
- W4224041885 created "2022-04-19" @default.
- W4224041885 creator A5028371557 @default.
- W4224041885 creator A5038919661 @default.
- W4224041885 creator A5085107699 @default.
- W4224041885 date "2022-04-14" @default.
- W4224041885 modified "2023-10-05" @default.
- W4224041885 title "Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR)" @default.
- W4224041885 cites W1047195804 @default.
- W4224041885 cites W1496317909 @default.
- W4224041885 cites W1500895378 @default.
- W4224041885 cites W1579480730 @default.
- W4224041885 cites W1595159159 @default.
- W4224041885 cites W1808644423 @default.
- W4224041885 cites W1968439835 @default.
- W4224041885 cites W1983170570 @default.
- W4224041885 cites W1989784053 @default.
- W4224041885 cites W1997024616 @default.
- W4224041885 cites W2031635009 @default.
- W4224041885 cites W2048193978 @default.
- W4224041885 cites W2073082177 @default.
- W4224041885 cites W2079803814 @default.
- W4224041885 cites W2083523694 @default.
- W4224041885 cites W2083711477 @default.
- W4224041885 cites W2086282769 @default.
- W4224041885 cites W2102771921 @default.
- W4224041885 cites W2123682012 @default.
- W4224041885 cites W2137610909 @default.
- W4224041885 cites W2139508021 @default.
- W4224041885 cites W2162745921 @default.
- W4224041885 cites W2241420790 @default.
- W4224041885 cites W2346511712 @default.
- W4224041885 cites W2484546859 @default.
- W4224041885 cites W2516567929 @default.
- W4224041885 cites W2532231543 @default.
- W4224041885 cites W2562603753 @default.
- W4224041885 cites W2563139571 @default.
- W4224041885 cites W2565116123 @default.
- W4224041885 cites W2593949166 @default.
- W4224041885 cites W2641311918 @default.
- W4224041885 cites W2767308334 @default.
- W4224041885 cites W2767972150 @default.
- W4224041885 cites W2793051107 @default.
- W4224041885 cites W2795850749 @default.
- W4224041885 cites W2802700742 @default.
- W4224041885 cites W2905210019 @default.
- W4224041885 cites W2907761876 @default.
- W4224041885 cites W2920455132 @default.
- W4224041885 cites W2920808372 @default.
- W4224041885 cites W2925322067 @default.
- W4224041885 cites W2941280710 @default.
- W4224041885 cites W2945198840 @default.
- W4224041885 cites W2948324982 @default.
- W4224041885 cites W2949597371 @default.
- W4224041885 cites W2969696253 @default.
- W4224041885 cites W2972872998 @default.
- W4224041885 cites W2976804821 @default.
- W4224041885 cites W2981874965 @default.
- W4224041885 cites W2990080715 @default.
- W4224041885 cites W2990990692 @default.
- W4224041885 cites W2999209402 @default.
- W4224041885 cites W3004837573 @default.
- W4224041885 cites W3008062478 @default.
- W4224041885 cites W3011594722 @default.
- W4224041885 cites W3013826304 @default.
- W4224041885 cites W3015773857 @default.
- W4224041885 cites W3025835050 @default.
- W4224041885 cites W3034253215 @default.
- W4224041885 cites W3034487379 @default.
- W4224041885 cites W3046407199 @default.
- W4224041885 cites W3082306038 @default.
- W4224041885 cites W3095874217 @default.
- W4224041885 cites W3100933494 @default.
- W4224041885 cites W3111140248 @default.
- W4224041885 cites W3112891538 @default.
- W4224041885 cites W3121986961 @default.
- W4224041885 cites W3132451580 @default.
- W4224041885 cites W3161859922 @default.
- W4224041885 cites W3174089940 @default.
- W4224041885 cites W3180846963 @default.
- W4224041885 cites W3192159963 @default.
- W4224041885 cites W3195978792 @default.
- W4224041885 cites W3208555273 @default.
- W4224041885 cites W4212956447 @default.
- W4224041885 cites W4214491624 @default.
- W4224041885 cites W4245350108 @default.
- W4224041885 cites W4254212488 @default.
- W4224041885 cites W4292199333 @default.
- W4224041885 cites W4300851297 @default.
- W4224041885 cites W647985083 @default.
- W4224041885 doi "https://doi.org/10.1155/2022/7183700" @default.
- W4224041885 hasPublicationYear "2022" @default.
- W4224041885 type Work @default.
- W4224041885 citedByCount "4" @default.
- W4224041885 countsByYear W42240418852022 @default.
- W4224041885 countsByYear W42240418852023 @default.
- W4224041885 crossrefType "journal-article" @default.
- W4224041885 hasAuthorship W4224041885A5028371557 @default.