Optimizing of predictive performance for construction projects utilizing support vector machine technique

Construction projects still face the old–new problem of delivering the projects within the predefined time and cost. This problem becomes more complicated with when addendums and variations are considered during the projects. This study aimed at developing an artificial intelligent model using suppo...

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Autores principales:	Firas Kh. Jaber, Faiq M. S. Al-Zwainy, Saba W. Hachem
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	cost index time index tunnels projects mean absolute percentage errors average accuracy Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/1dac06763cc848f4aebca1fb00fc77f8
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spelling	oai:doaj.org-article:1dac06763cc848f4aebca1fb00fc77f82021-11-04T15:51:56ZOptimizing of predictive performance for construction projects utilizing support vector machine technique2331-191610.1080/23311916.2019.1685860https://doaj.org/article/1dac06763cc848f4aebca1fb00fc77f82019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1685860https://doaj.org/toc/2331-1916Construction projects still face the old–new problem of delivering the projects within the predefined time and cost. This problem becomes more complicated with when addendums and variations are considered during the projects. This study aimed at developing an artificial intelligent model using support vector machine (SVM) technique to predict the time and cost indices of projects. Data from 21 tunnel projects implemented in Kurdistan, Iraq were collected and used in this study. The input data include five variables, namely contract value, contract duration, number of change orders, number of conflicts, and classification of company. WEKA––a set of software for machine learning and data mining––developed at the University of Waikato in New Zealand was used to build SVM model to predict the time and cost indices. The collected data were split by default into a training set of 65%, a testing set of 10% and a validation set of 25%. The results show that SVM model I successfully predicted the cost index not only for the trained data, but also for projects with input parameters out of the range of the training inputs. Mean Absolute Percentage Error (MAPE) and Average Accuracy (AA) for SVM prediction of cost index were found to be 13.9% and 86.1%, respectively. The SVM model II accurately predicted the time index with MAPE and AA of 3.4% and 96.6%, respectively.Firas Kh. JaberFaiq M. S. Al-ZwainySaba W. HachemTaylor & Francis Grouparticlecost indextime indextunnels projectsmean absolute percentage errorsaverage accuracyEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	cost index time index tunnels projects mean absolute percentage errors average accuracy Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	cost index time index tunnels projects mean absolute percentage errors average accuracy Engineering (General). Civil engineering (General) TA1-2040 Firas Kh. Jaber Faiq M. S. Al-Zwainy Saba W. Hachem Optimizing of predictive performance for construction projects utilizing support vector machine technique
description	Construction projects still face the old–new problem of delivering the projects within the predefined time and cost. This problem becomes more complicated with when addendums and variations are considered during the projects. This study aimed at developing an artificial intelligent model using support vector machine (SVM) technique to predict the time and cost indices of projects. Data from 21 tunnel projects implemented in Kurdistan, Iraq were collected and used in this study. The input data include five variables, namely contract value, contract duration, number of change orders, number of conflicts, and classification of company. WEKA––a set of software for machine learning and data mining––developed at the University of Waikato in New Zealand was used to build SVM model to predict the time and cost indices. The collected data were split by default into a training set of 65%, a testing set of 10% and a validation set of 25%. The results show that SVM model I successfully predicted the cost index not only for the trained data, but also for projects with input parameters out of the range of the training inputs. Mean Absolute Percentage Error (MAPE) and Average Accuracy (AA) for SVM prediction of cost index were found to be 13.9% and 86.1%, respectively. The SVM model II accurately predicted the time index with MAPE and AA of 3.4% and 96.6%, respectively.
format	article
author	Firas Kh. Jaber Faiq M. S. Al-Zwainy Saba W. Hachem
author_facet	Firas Kh. Jaber Faiq M. S. Al-Zwainy Saba W. Hachem
author_sort	Firas Kh. Jaber
title	Optimizing of predictive performance for construction projects utilizing support vector machine technique
title_short	Optimizing of predictive performance for construction projects utilizing support vector machine technique
title_full	Optimizing of predictive performance for construction projects utilizing support vector machine technique
title_fullStr	Optimizing of predictive performance for construction projects utilizing support vector machine technique
title_full_unstemmed	Optimizing of predictive performance for construction projects utilizing support vector machine technique
title_sort	optimizing of predictive performance for construction projects utilizing support vector machine technique
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/1dac06763cc848f4aebca1fb00fc77f8
work_keys_str_mv	AT firaskhjaber optimizingofpredictiveperformanceforconstructionprojectsutilizingsupportvectormachinetechnique AT faiqmsalzwainy optimizingofpredictiveperformanceforconstructionprojectsutilizingsupportvectormachinetechnique AT sabawhachem optimizingofpredictiveperformanceforconstructionprojectsutilizingsupportvectormachinetechnique
_version_	1718444699935571968

Optimizing of predictive performance for construction projects utilizing support vector machine technique

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