Title: Artificial Intelligence Approaches to Dynamic Project Success Assessment Taxonomic
Abstract:Artificial Intelligence (AI) approaches are widely applied to various civil engineering problems. This paper focuses on an approach to assessing project success using AI approaches including K-means C...Artificial Intelligence (AI) approaches are widely applied to various civil engineering problems. This paper focuses on an approach to assessing project success using AI approaches including K-means Clustering, Genetic Algorithm (GA), Fuzzy Logic (FL), and Neural Network (NN). As various factors at different construction stages affect project performance, project success criteria change dynamically and are hard to estimate accurately through reliance on experience alone. Information that is uncertain, vague, and incomplete is an inherent feature of this problem. CAPP (Continuous Assessment of Project Performance) software was used to study in a dynamic manner the significant factors that influence upon project performance. K-means clustering was employed to conduct an unsupervised clustering to extract similar cases for comparison. FL for was used to examine uncertainties, NN was employed for data mining, and GA was used for optimization. A developed Evolutionary Fuzzy Neural Inference Model (EFNIM) was used to achieve optimal mapping of input factors and project success output. Results show that EFNIM is able to estimate the degree of project success well and case clustering can greatly enhance project success assessments. (Min-Yuan Cheng, Li-Chuan Lien, Hsing-Chih Tsai, Pi-Hung Chen. Artificial Intelligence Approaches to Dynamic Project Success Assessment Taxonomic. Life Sci J. 2012.9(4). 5156-5163) (ISSN: 1097-8135). http://www.lifesciencesite.com. 768Read More
Publication Year: 2012
Publication Date: 2012-01-01
Language: en
Type: article
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Cited By Count: 8
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