Simulating multi-agent narratives for pre-occupancy evaluation of architectural designs

Abstract Simulating and evaluating the impact that a building design might produce on its prospective occupants is a key challenge in architectural design. Prior work demonstrated the capabilities of narrative-based modeling to coordinate the collaborative behavior of virtual occupants. In this work, we aim to demonstrate the scalability and applicability of narrative-based modeling to support the pre-occupancy evaluation of alternative design options in complex real-world hospital facilities. To do so, we developed a narrative-based pre-occupancy evaluation platform that extends pre-existing narrative-based capabilities with (a) a newly developed library space, actor, activities, and narrative entities that support the simulation of real-world human behavior patterns while accounting for the impact that a building design produces on how the patterns unfold, and (b) a newly integrated evaluation module able to generate and visualize numerical data-logs and spatiotemporal data-maps of key performance indicators in hospital settings. We applied the platform to conduct a comparative pre-occupancy evaluation of two different architectural designs for an outpatient ophthalmology clinic. Results demonstrate the scalability and applicability of narrative-based modeling to help design stakeholders visualize and analyze how design decisions may impact future building operations in outpatient clinics.

[1]  Anders Ekholm,et al.  Modelling of User Activities in Building Design , 2001, eCAADe proceedings.

[2]  Mehul Bhatt,et al.  Architecture, computing, and design assistance☆ , 2013 .

[3]  Tao Wang,et al.  An Energy-Aware, Agent-Based Maintenance-Scheduling Framework to Improve Occupant Satisfaction , 2015 .

[4]  Rafael Sacks,et al.  Simulating the behavior of trade crews in construction using agents and building information modeling , 2017 .

[5]  Sungwon Jung,et al.  Pre-Occupancy Evaluation based on user behavior prediction in 3D virtual simulation , 2017 .

[6]  Tae Wan Kim,et al.  Ontology for Representing Building Users’ Activities in Space-Use Analysis , 2014 .

[7]  Avishai Mandelbaum,et al.  Designing patient flow in emergency departments , 2012 .

[8]  Victor J. Blue,et al.  Cellular Automata Microsimulation of Bidirectional Pedestrian Flows , 1999 .

[9]  Aizhu Ren,et al.  GIS-based 3D evacuation simulation for indoor fire , 2012 .

[10]  Jinhui Wang,et al.  A Cellular Automata occupant evacuation model considering gathering behavior , 2015 .

[11]  W. Dunsmuir,et al.  Association of interruptions with an increased risk and severity of medication administration errors. , 2010, Archives of internal medicine.

[12]  Jeff Haberl,et al.  Developing a physical BIM library for building thermal energy simulation , 2015 .

[13]  L. F. Henderson,et al.  The Statistics of Crowd Fluids , 1971, Nature.

[14]  Geoffrey Qiping Shen,et al.  The User Pre-Occupancy Evaluation Method in designer–client communication in early design stage: A case study , 2013 .

[15]  Ralf Klein,et al.  An automated IFC-based workflow for building energy performance simulation with Modelica , 2018, Automation in Construction.

[16]  Seung Wan Hong,et al.  The Effects of Human Behavior Simulation on Architecture Major Students′ Fire Egress Planning , 2018 .

[17]  Qiping Shen,et al.  Building Information Modeling-based user activity simulation and evaluation method for improving designer–user communications , 2012 .

[18]  Matthew Hedrick,et al.  Developing an experienced-based design review application for healthcare facilities using a 3d game engine , 2011, J. Inf. Technol. Constr..

[19]  Tianzhen Hong,et al.  Occupant behavior modeling for building performance simulation: Current state and future challenges , 2015 .

[20]  Craig Zimring,et al.  Impact of Hospital Unit Design for Patient-Centered Care on Nurses’ Behavior , 2011 .

[21]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[23]  Stefania Bandini,et al.  SITUATED CELLULAR AGENTS APPROACH TO CROWD MODELING AND SIMULATION , 2007, Cybern. Syst..

[24]  Kincho H. Law,et al.  Modeling social behaviors in an evacuation simulator , 2014, Comput. Animat. Virtual Worlds.

[25]  Martin Fischer,et al.  Automated generation of user activity-space pairs in space-use analysis , 2014 .

[26]  Michael J. Ostwald,et al.  A computational model for accommodating spatial uncertainty: Predicting inhabitation patterns in open-planned spaces , 2014 .

[27]  C. Zimring,et al.  A Review of the Research Literature on Evidence-Based Healthcare Design , 2008, HERD.

[28]  T. Vicsek,et al.  Simulation of pedestrian crowds in normal and evacuation situations , 2002 .

[29]  Bauke de Vries,et al.  Simulation and Validation of Human Movement in Building Spaces , 2010 .

[30]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[31]  Gabriel Wurzer.  Schematic Systems — Constraining Functions through Processes (and Vice Versa) , 2010 .

[32]  Xiaoping Zheng,et al.  Modeling crowd evacuation of a building based on seven methodological approaches , 2009 .

[33]  Davide Schaumann,et al.  A dashboard model to support spatio-temporal analysis of simulated human behavior in future built environments , 2018 .

[34]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[35]  Laura Bellia,et al.  Evaluating performance of daylight-linked building controls during preliminary design , 2018, Automation in Construction.

[36]  Lamine Mahdjoubi,et al.  A dynamic approach for evacuees' distribution and optimal routing in hazardous environments , 2018, Automation in Construction.

[37]  R. Hughes.  The flow of human crowds , 2003 .

[38]  Robert G. Sargent,et al.  Verification and validation of simulation models , 2013, Proceedings of Winter Simulation Conference.

[39]  Peter E. D. Love,et al.  Virtual reality for the built environment: a critical review of recent advances , 2013, J. Inf. Technol. Constr..

[40]  Daniel Thalmann,et al.  Crowd Simulation , 2019, Encyclopedia of Computer Graphics and Games.

[41]  Ren-Jye Dzeng,et al.  An Activity‐Based Simulation Model for Assessing Function Space Assignment for Buildings: A Service Performance Perspective , 2015, Comput. Aided Civ. Infrastructure Eng..

[42]  Yang Ning,et al.  Application of computer simulation technology for structure analysis in disaster , 2004 .

[43]  Nicholas Watkins,et al.  Simulation and Mock-Up Research Methods to Enhance Design Decision Making , 2012, HERD.

[44]  Simon Breslav,et al.  Simulating use scenarios in hospitals using multi-agent narratives , 2017 .

[45]  Bryan Lawson,et al.  What designers know , 2018, The Design Student’s Journey.

[46]  Franklin Becker,et al.  The Ecology of the Patient Visit: Physical Attractiveness, Waiting Times, and Perceived Quality of Care , 2008, The Journal of ambulatory care management.

[47]  Christoph Hölscher,et al.  Map Use and Wayfinding Strategies in a Multi-building Ensemble , 2006, Spatial Cognition.

[48]  Kincho H. Law,et al.  A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations , 2007, AI & SOCIETY.

[49]  Alasdair Turner,et al.  Pre and Post Occupancy Evaluations in Workplace Environments , 2010 .

[50]  Carl P. L. Schultz,et al.  The shape of empty space: Human-centred cognitive foundations in computing for spatial design , 2012, 2012 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

[51]  H. Rittel,et al.  Dilemmas in a general theory of planning , 1973 .