The RISCONA system: constructability appraisal through the identification and assessment of technical project risks sources
Paper i proceeding, 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

constructability

unsupervised and supervised machine learning

k-means++

classification prediction

Författare

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,

Styrkeområden

Informations- och kommunikationsteknik

Transport

Building Futures (2010-2018)

Produktion

Drivkrafter

Hållbar utveckling

Innovation och entreprenörskap

Ämneskategorier

Språkteknologi (språkvetenskaplig databehandling)

Byggproduktion

Infrastrukturteknik

Annan samhällsbyggnadsteknik

Husbyggnad

Datavetenskap (datalogi)

Mer information

Senast uppdaterat

2019-03-27