Intelligent methodology for project conceptual cost prediction. Issue 5 (May 2019)

• Record Type: Journal Article
• Authors: Elmousalami, Haytham H.
• Is Part Of: Heliyon. Volume 5:Issue 5(2019)
• Publication Date: 2019-05
• Subjects: Computer science; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2019.e01625
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision makers. Several methodologies exist to develop a conceptual cost model. However, many gaps exist in the current methodologies such as depending only on experts 'opinions and questionnaire survey to identify the project features, key cost drivers and developing deterministic predictive models without taking uncertainty nature into consideration. The main contribution of this study is developing an intelligent methodology for predicting the project cost at the conceptual stage. The proposed methodology can automatically identify key cost drivers and maintain uncertainty to predicted cost. Field canals improvement projects (FCIPs) are used as a case study to validate the proposed methodology. The selected methodology has applied quantitative approaches to identify the key cost drivers. In addition, the methodology has applied a genetic fuzzy model that automatically generates fuzzy rules to automatically predict the conceptual cost. Moreover, the results show a superior performance of the genetic fuzzy model than the traditional fuzzy model. In addition, this study presents a publicly open dataset for FCIPs to be used for future models validation and analysis. Highlights : A new methodology is presented to develop parametric cost estimation to automatically identify the key cost drivers and provide uncertainty to prediction values. The proposed methodology of presents and compares different quantitative methods to automatically identify key cost drivers The proposed methodology has conducted a genetic fuzzy model as a hybrid model that can automatically develop the fuzzy rules with superior performance than a traditional fuzzy model. This study presents the publicly open dataset FCIPs to be used for validation and analysis of the future cost prediction algorithms. *Dataset available at https://github.com/HaythamElmousalami/Field-canals-improvement-projects-FCIPs- .