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.

k-means++

holistic integration

Construction management

prototype software application

classification prediction

unsupervised and supervised machine learning

support vector machines

nonnegative matrix factorization

Arkitektur och samhällsbyggnadsteknik

project management

constructability

risk analysis

Architecture and Civil Engineering

Författare

Dimosthenis Kifokeris

Chalmers, Arkitektur och samhällsbyggnadsteknik, Construction Management

Yiannis Xenidis

Aristotelio Panepistimio Thessalonikis

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

Vol. 114 1696-1703

Guimarães 2019 IABSE Conference
Guimarães, Portugal,

Ämneskategorier

Byggproduktion

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2020-06-07