The RISCONA system: constructability appraisal through the identification and assessment of technical project risks sources
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Bibliographic Details
| Author(s): |
Dimosthenis Kifokeris
(Aristotle University of Thessaloniki, Thessaloniki, Greece)
Yiannis Xenidis (Aristotle University of Thessaloniki, Thessaloniki, Greece) |
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Symposium: Towards a Resilient Built Environment Risk and Asset Management, Guimarães, Portugal, 27-29 March 2019 | ||||
| Published in: | IABSE Symposium Guimarães 2019 | ||||
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| Page(s): | 1696-1703 | ||||
| Total no. of pages: | 8 | ||||
| DOI: | 10.2749/guimaraes.2019.1696 | ||||
| Abstract: |
In construction management, constructability and risk analysis have never been methodologically and computationally integrated, leading to non-optimal construction knowledge implementation, stakeholders’ cooperation, choice of construction method, and risk-driven perception of key managerial concepts. In this paper, a methodology unifying constructability and risk analysis is delineated, where: (1) risk sources are derived with unsupervised machine learning, (2) actual projects’ data are collected and suitably correlated with the derived risk sources, and (3) the appraisal of constructability through the data-correlated risk sources is modelled with supervised machine learning. As the culmination of this modelling, the prototype software application RISCONA (RIsk Source-based CONstructability Appraisal) is presented, as a tool that can help construction managers in their decision-making regarding constructability and risk analysis. |
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| Keywords: |
project management risk analysis constructability holistic integration unsupervised and supervised machine learning k-means++ nonnegative matrix factorization support vector machines classification prediction prototype software application
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