{"title":"Develop an artificial neural network (ANN) model to predict construction projects performance in Syria","authors":"Rana Maya, Bassam Hassan, Ammar Hassan","doi":"10.1016/j.jksues.2021.05.002","DOIUrl":null,"url":null,"abstract":"<div><p>The purpose of this paper is to enable members of the construction project team to understand the factors which they must closely monitor to complete the project with the required performance. Therefore, the research aimed to develop an artificial neural network (ANN) model to predict construction project performance based on the above factors.</p><p>A group of (34) factors that affect the performance of the project has been identified based on practitioners' opinions. ANN was designed to predict the project performance model using seven inputs that represent six factors that were prioritized as the most influencing factors. The model showed the factors that affect project performance as follows: Coordination and commitment of project parties (30.9%), Schedule estimate (25.4%), Project team experience and availability (24.5 %), and Support from senior management (14.3%).</p><p>We concluded to design a model that predicts project performance based on previous influencing factors, as this model has a prediction accuracy of 96.1 % and an error of 3.9 %.</p></div>","PeriodicalId":35558,"journal":{"name":"Journal of King Saud University, Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jksues.2021.05.002","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of King Saud University, Engineering Sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1018363921000738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Chemical Engineering","Score":null,"Total":0}
引用次数: 11
Abstract
The purpose of this paper is to enable members of the construction project team to understand the factors which they must closely monitor to complete the project with the required performance. Therefore, the research aimed to develop an artificial neural network (ANN) model to predict construction project performance based on the above factors.
A group of (34) factors that affect the performance of the project has been identified based on practitioners' opinions. ANN was designed to predict the project performance model using seven inputs that represent six factors that were prioritized as the most influencing factors. The model showed the factors that affect project performance as follows: Coordination and commitment of project parties (30.9%), Schedule estimate (25.4%), Project team experience and availability (24.5 %), and Support from senior management (14.3%).
We concluded to design a model that predicts project performance based on previous influencing factors, as this model has a prediction accuracy of 96.1 % and an error of 3.9 %.
期刊介绍:
Journal of King Saud University - Engineering Sciences (JKSUES) is a peer-reviewed journal published quarterly. It is hosted and published by Elsevier B.V. on behalf of King Saud University. JKSUES is devoted to a wide range of sub-fields in the Engineering Sciences and JKSUES welcome articles of interdisciplinary nature.