The RISCONA system: constructability appraisal through the identification and assessment of technical project risks sources
Paper in proceedings, 2019

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.

nonnegative matrix factorization

support vector machines

holistic integration

project management

risk analysis

prototype software application


unsupervised and supervised machine learning


classification prediction


Dimosthenis Kifokeris

Aristotle University of Thessaloniki

Yiannis Xenidis

Aristotle University of Thessaloniki

IABSE Symposium Guimarães 2019 Report: Towards a Resilient Built Environment Risk and Asset Management

Vol. 114 1696-1703

Guimarães 2019 IABSE Conference
Guimarães, Portugal,

Areas of Advance

Information and Communication Technology


Building Futures (2010-2018)


Driving Forces

Sustainable development

Innovation and entrepreneurship

Subject Categories

Language Technology (Computational Linguistics)

Construction Management

Infrastructure Engineering

Other Civil Engineering

Building Technologies

Computer Science

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