Application of linguistic clustering to define sources of risk in technical projects
Journal article, 2018

Risk identification is adversely affected by the still existing definitional and applicational discrepancy regarding risks and other
related notions, such as hazards and impacts. A paradigm shift is beginning to be in effect, proposing the preliminary identification of risk
sources to ameliorate the aforementioned adversities. However, apart from identifying risk sources from the outset, the bulk of the already
conducted project risk-related research, from which risk sources could be derived, is still not free of discrepancies and is falling short of use.
In this paper, a new linguistic clustering algorithm, using the k-means++ procedure in addition to the semantics tools of stop world removal
and word stemming is developed and codified. Then, the algorithm is applied on a vast risk notions set, emanated from an exhaustive review
of the relative literature. The clustered and semantically processed results of the application are then used for the deduction of risk sources.
Thus, this paper provides a compact, general, and encompassing master set of risk sources, discretized among distinct overhead categories.

Project risk management

k-means++ algorithm

Technical projects

Cluster analysis

Risk identification

Author

Dimosthenis Kifokeris

Aristotle University of Thessaloniki

Yiannis Xenidis

Aristotle University of Thessaloniki

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

2376-7642 (eISSN)

Vol. 4 1 04017031-1-04017031-13 04017031

Areas of Advance

Information and Communication Technology

Transport

Building Futures (2010-2018)

Production

Energy

Driving Forces

Sustainable development

Innovation and entrepreneurship

Subject Categories

Construction Management

Other Engineering and Technologies not elsewhere specified

Infrastructure Engineering

Other Civil Engineering

DOI

10.1061/AJRUA6.0000938

More information

Latest update

3/5/2019 7