Simulating the behavior of trade crews in construction using agents and building information modeling

Simulation is particularly useful for testing different production control and information flow methods in construction, because field experiments suffer from difficulties with isolating cause and effect. Existing methods such as Discrete Event Simulation are limited in their ability to model the be...

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Published inAutomation in construction Vol. 74; pp. 12 - 27
Main Authors Ben-Alon, Lola, Sacks, Rafael
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.02.2017
Elsevier BV
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Online AccessGet full text
ISSN0926-5805
1872-7891
DOI10.1016/j.autcon.2016.11.002

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Abstract Simulation is particularly useful for testing different production control and information flow methods in construction, because field experiments suffer from difficulties with isolating cause and effect. Existing methods such as Discrete Event Simulation are limited in their ability to model the behavior of crews and of individuals who make decisions subject to their perceptions of uncertain conditions. Agent-Based Simulation may offer a better solution because agents can be applied with behavioral models. The aim of this work was to build an experimental tool capable of reflecting the emergent nature of production in construction. This required capturing trade crew behaviors through interviews and encapsulating the behavior in software agents. The system models trades' decision-making and situational awareness while using a Building Information Model to define the physical and the process environment for the simulation. The resulting simulation tool was validated by testing predictable scenarios, which resulted in similar patterns to those found in an actual construction site. It was then applied to explore the emergent outcomes of more complex scenarios. Emergent outcomes result from interaction of behavior, environment (context), and events. [Display omitted] •EPIC (Emergent Production in Construction) is an experimental agent-based simulation.•EPIC is uniquely capable of modeling the decisions and interactions of trade crews.•Existing agent-based simulations use simplistic geometry; EPIC uses BIM models.•EPIC was validated with seven predictable scenarios and tested on four new scenarios.•EPIC proved applicable for testing different modes of construction control policies.
AbstractList Simulation is particularly useful for testing different production control and information flow methods in construction, because field experiments suffer from difficulties with isolating cause and effect. Existing methods such as Discrete Event Simulation are limited in their ability to model the behavior of crews and of individuals who make decisions subject to their perceptions of uncertain conditions. Agent-Based Simulation may offer a better solution because agents can be applied with behavioral models. The aim of this work was to build an experimental tool capable of reflecting the emergent nature of production in construction. This required capturing trade crew behaviors through interviews and encapsulating the behavior in software agents. The system models trades' decision-making and situational awareness while using a Building Information Model to define the physical and the process environment for the simulation. The resulting simulation tool was validated by testing predictable scenarios, which resulted in similar patterns to those found in an actual construction site. It was then applied to explore the emergent outcomes of more complex scenarios. Emergent outcomes result from interaction of behavior, environment (context), and events. [Display omitted] •EPIC (Emergent Production in Construction) is an experimental agent-based simulation.•EPIC is uniquely capable of modeling the decisions and interactions of trade crews.•Existing agent-based simulations use simplistic geometry; EPIC uses BIM models.•EPIC was validated with seven predictable scenarios and tested on four new scenarios.•EPIC proved applicable for testing different modes of construction control policies.
Simulation is particularly useful for testing different production control and information flow methods in construction, because field experiments suffer from difficulties with isolating cause and effect. Existing methods such as Discrete Event Simulation are limited in their ability to model the behavior of crews and of individuals who make decisions subject to their perceptions of uncertain conditions. Agent-Based Simulation may offer a better solution because agents can be applied with behavioral models. The aim of this work was to build an experimental tool capable of reflecting the emergent nature of production in construction. This required capturing trade crew behaviors through interviews and encapsulating the behavior in software agents. The system models trades' decision-making and situational awareness while using a Building Information Model to define the physical and the process environment for the simulation. The resulting simulation tool was validated by testing predictable scenarios, which resulted in similar patterns to those found in an actual construction site. It was then applied to explore the emergent outcomes of more complex scenarios.
Author Ben-Alon, Lola
Sacks, Rafael
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Keywords Agent-Based Simulation
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Snippet Simulation is particularly useful for testing different production control and information flow methods in construction, because field experiments suffer from...
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SubjectTerms Agent-Based Simulation
Building information modeling
Building management systems
Computer simulation
Construction
Construction equipment
Construction methods
Construction sites
Control methods
Crews
Decision making
Discrete Event Simulation
Emergent construction
Encapsulation
Information flow
Production controls
Production methods
Simulation
Situational awareness
Software agents
Title Simulating the behavior of trade crews in construction using agents and building information modeling
URI https://dx.doi.org/10.1016/j.autcon.2016.11.002
https://www.proquest.com/docview/1919588406
Volume 74
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