Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters

Estimation of the seismic retrofit cost (SRC) is a complicated task in construction projects. In this study, the performance of four machine learning algorithms (MLAs), including Random Forest (RF), Extreme Learning Machine (ELM), Classification and Regression Tree (CART), and Multivariate Adaptive...

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Autores principales:	Naser Safaeian Hamzehkolaei, Meysam Alizamir
Formato:	article
Lenguaje:	EN
Publicado:	Pouyan Press 2021
Materias:	seismic retrofit cost random forest (rf) extreme learning machine (elm) classification and regression tree (cart) multivariate adaptive regression spline (mars) Technology T
Acceso en línea:	https://doaj.org/article/d75ec4ff23314d288c9340f949c3940d
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spelling	oai:doaj.org-article:d75ec4ff23314d288c9340f949c3940d2021-11-11T11:41:52ZPerformance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters2588-287210.22115/scce.2021.284630.1312https://doaj.org/article/d75ec4ff23314d288c9340f949c3940d2021-07-01T00:00:00Zhttp://www.jsoftcivil.com/article_134929_006f74c73227399a0d757559c91f64c9.pdfhttps://doaj.org/toc/2588-2872Estimation of the seismic retrofit cost (SRC) is a complicated task in construction projects. In this study, the performance of four machine learning algorithms (MLAs), including Random Forest (RF), Extreme Learning Machine (ELM), Classification and Regression Tree (CART), and Multivariate Adaptive Regression Spline (MARS), was examined in estimating SRC values. The total floor area (TFA), number of stories (NS), seismic weight (SW), seismicity (S), soil type (ST), plan configuration (PC), and structural type (STT) were considered as structural input variables. To achieve the best performance of applied MLAs, twenty-two scenarios based on different combinations of input variables were considered. The correlation coefficient (r), Root Mean Squared Error (RMSE), Adjusted R-squared, and Nash-Sutcliffe efficiency (NSE) metrics together with the Taylor diagram were used to compare the accuracy of applied models. A sensitivity analysis using the RReliefF algorithm showed that TFA, SW, and PC are the most influential parameters, whereas the ST and STT have negative influences on SRC values. Comparison analysis results indicated that the ELM model with r of 0.896, RMSE of 0.081, and NSE of 0.758 had the best performance among other employed MLAs. Also, the RF regression achieved the second rank. In conclusion, the ELM model with single-layer feedforward neural network was superior to other data-driven models; therefore, it can be applied as an efficient tool for estimating SRC values using structural input parameters.Naser Safaeian HamzehkolaeiMeysam AlizamirPouyan Pressarticleseismic retrofit costrandom forest (rf)extreme learning machine (elm)classification and regression tree (cart)multivariate adaptive regression spline (mars)TechnologyTENJournal of Soft Computing in Civil Engineering, Vol 5, Iss 3, Pp 32-57 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	seismic retrofit cost random forest (rf) extreme learning machine (elm) classification and regression tree (cart) multivariate adaptive regression spline (mars) Technology T
spellingShingle	seismic retrofit cost random forest (rf) extreme learning machine (elm) classification and regression tree (cart) multivariate adaptive regression spline (mars) Technology T Naser Safaeian Hamzehkolaei Meysam Alizamir Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters
description	Estimation of the seismic retrofit cost (SRC) is a complicated task in construction projects. In this study, the performance of four machine learning algorithms (MLAs), including Random Forest (RF), Extreme Learning Machine (ELM), Classification and Regression Tree (CART), and Multivariate Adaptive Regression Spline (MARS), was examined in estimating SRC values. The total floor area (TFA), number of stories (NS), seismic weight (SW), seismicity (S), soil type (ST), plan configuration (PC), and structural type (STT) were considered as structural input variables. To achieve the best performance of applied MLAs, twenty-two scenarios based on different combinations of input variables were considered. The correlation coefficient (r), Root Mean Squared Error (RMSE), Adjusted R-squared, and Nash-Sutcliffe efficiency (NSE) metrics together with the Taylor diagram were used to compare the accuracy of applied models. A sensitivity analysis using the RReliefF algorithm showed that TFA, SW, and PC are the most influential parameters, whereas the ST and STT have negative influences on SRC values. Comparison analysis results indicated that the ELM model with r of 0.896, RMSE of 0.081, and NSE of 0.758 had the best performance among other employed MLAs. Also, the RF regression achieved the second rank. In conclusion, the ELM model with single-layer feedforward neural network was superior to other data-driven models; therefore, it can be applied as an efficient tool for estimating SRC values using structural input parameters.
format	article
author	Naser Safaeian Hamzehkolaei Meysam Alizamir
author_facet	Naser Safaeian Hamzehkolaei Meysam Alizamir
author_sort	Naser Safaeian Hamzehkolaei
title	Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters
title_short	Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters
title_full	Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters
title_fullStr	Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters
title_full_unstemmed	Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters
title_sort	performance evaluation of machine learning algorithms for seismic retrofit cost estimation using structural parameters
publisher	Pouyan Press
publishDate	2021
url	https://doaj.org/article/d75ec4ff23314d288c9340f949c3940d
work_keys_str_mv	AT nasersafaeianhamzehkolaei performanceevaluationofmachinelearningalgorithmsforseismicretrofitcostestimationusingstructuralparameters AT meysamalizamir performanceevaluationofmachinelearningalgorithmsforseismicretrofitcostestimationusingstructuralparameters
_version_	1718439172129161216

Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters

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