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

Jaber, Firas; Al-Zwainy, Faiq M. S.; Hachem, Saba W.

doi:10.1080/23311916.2019.1685860

Cited by 7 publications

(2 citation statements)

References 9 publications

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“… Publications examining project performance and success estimation (10%) are generally targeted toward project management, the majority focusing on decision support for the project manager, or the discipline and process of project management itself (Hajdasz, 2015;Gudauskas et al, 2015;Hanna et al, 2018;Mahfouz et al, 2018;Vickranth et al, 2019). Other studies focus on predicting and optimizing project performance, time, and cost (Mirahadi and Zayed, 2016;Jaber et al, 2019) or project evaluation (Erzaij et al, 2020).…”

Section: Conceptual 40%mentioning

confidence: 99%

Artificial Intelligence in Construction Projects: A Systematic Scoping Review

2022

EPPM-Journal

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The use of artificial intelligence (AI) in construction projects has surged in recent years and is believed to represent a significant potential for increasing productivity and efficiency in the industry. The purpose of this paper is to present a state-of-the-art view of the field by conducting a review of publications concerning the topic of AI in construction and comparing the findings to previously conducted reviews. This paper provides an overview of the recent and current uses of AI in construction projects, through a descriptive analysis of the characteristics and contents of 86 peer-reviewed articles from 2015 to 2020. Although the application of AI in the industry is not entirely new, construction appears to currently be behind other industries in terms of adopting and adapting to AI. The results show that a wide range of research is conducted on AI in construction projects. A limited number of publication channels and authors stand behind a significant part of the reviewed publications. Most studies are conceptual or use a mixed-methods research design. The research addresses several areas of application, but there is a predominance of quantitatively based subfields of construction, such as estimation and cost control, logistics, planning, and scheduling. Future research should focus on developing holistic and process-oriented frameworks for projects to move from ambition to practice. Findings can inform the future development and implementation of AI in the construction industry context. For researchers, this study identifies areas in need of further attention and examines possibilities for future exploration of multidisciplinary approaches that combine construction engineering, project management, AI, and social science. For practitioners, the study highlights current trends and work within the field, providing an overview of the potential for pilot studies, tests, and innovations.

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Section: Conceptual 40%mentioning

confidence: 99%

Artificial Intelligence in Construction Projects: A Systematic Scoping Review

2022

EPPM-Journal

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“…MAPE and AA for SVM cost index prediction were 13.9% and 86.1%, respectively. MAPE and AA for SVM model II were 3.4% and 96.6%, respectively [8].…”

Section: Literature Reviewmentioning

confidence: 95%

Archives of Civil Engineering

Hasan,

Hammody,

Albayati

2022

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The cost overrun in road construction projects in Iraq is one of the major problems that face the construction of new roads. To enable the concerned government agencies to predict the final cost of roads, the objective this paper suggested is to develop an early cost estimating model for road projects using a support vector machine based on (43) sets of bills of quantity collected in Baghdad city in Iraq. As cost estimates are required at the early stages of a project, consideration was given to the fact that the input data for the support vector machine model could be easily extracted from sketches or the project's scope definition. The data were collected from contracts awarded by the Mayoralty of Baghdad for completed projects between 2010-2013. Mathematical equations were constructed using the Support Vector Machine Algorithm (SMO) technique. An average of accuracy (AA) (99.65%) and coefficient of determination (R 2 ) (97.63%) for the model was achieved by the created prediction equations.

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