A framework for modelling masonry construction using hybrid simulation approaches
Author(s) -
Orsolya Bokor,
Laura Flórez,
Allan Osborne,
Barry Gledson
Publication year - 2018
Publication title -
creative construction conference 2018 - proceedings
Language(s) - English
Resource type - Conference proceedings
DOI - 10.3311/ccc2018-096
Subject(s) - process (computing) , productivity , computer science , fuzzy logic , discrete event simulation , masonry , resource (disambiguation) , risk analysis (engineering) , simulation modeling , industrial engineering , engineering , civil engineering , simulation , artificial intelligence , business , economics , computer network , macroeconomics , microeconomics , operating system
Labour is a crucial resource for construction projects. More risks are associated with this than with other resources, such as materials and equipment. Contractors need tools to make more precise estimations concerning labour productivity that will allow them to minimise these risks and manage labour resources in the most efficient way possible. To achieve this, use can be made of construction simulation techniques, however, depending on the complexity of the problem, applying a single simulation approach might not be enough to appropriately model construction. Hybrid simulation approaches seem to be suitable because they combine the advantages of their components to reflect the dynamic nature of construction processes better and consider the number of uncertainties. Hybrid approaches can combine traditional discrete-event simulation (DES), agent-based modelling (ABM) or system dynamics (SD) with each other or with, for example, fuzzy logic (FL) to better capture the factors influencing productivity. To address these issues, a framework for modelling a masonry construction process that uses hybrid simulation is presented. Because masonry works are one of the most labour-intensive construction processes, and skilled labour resources are scarce, the use of such a framework would help contractors to make more realistic schedules based on accurate labour productivity estimation; thus, enabling them to utilise their resources more efficiently.
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