Distributed Simulation Platforms and Data Passing Tools for Natural Hazards Engineering: Reviews, Limitations, and Recommendations

Lichao Xu , Szu-Yun Lin , Andrew W. Hlynka , Hao Lu , Vineet R. Kamat , Carol C. Menassa , Sherif El-Tawil , Atul Prakash , Seymour M. J. Spence , Jason McCormick

International Journal of Disaster Risk Science ›› 2021, Vol. 12 ›› Issue (5) : 617 -634.

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International Journal of Disaster Risk Science ›› 2021, Vol. 12 ›› Issue (5) : 617 -634. DOI: 10.1007/s13753-021-00361-7
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Distributed Simulation Platforms and Data Passing Tools for Natural Hazards Engineering: Reviews, Limitations, and Recommendations

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Abstract

There has been a strong need for simulation environments that are capable of modeling deep interdependencies between complex systems encountered during natural hazards, such as the interactions and coupled effects between civil infrastructure systems response, human behavior, and social policies, for improved community resilience. Coupling such complex components with an integrated simulation requires continuous data exchange between different simulators simulating separate models during the entire simulation process. This can be implemented by means of distributed simulation platforms or data passing tools. In order to provide a systematic reference for simulation tool choice and facilitating the development of compatible distributed simulators for deep interdependent study in the context of natural hazards, this article focuses on generic tools suitable for integration of simulators from different fields but not the platforms that are mainly used in some specific fields. With this aim, the article provides a comprehensive review of the most commonly used generic distributed simulation platforms (Distributed Interactive Simulation (DIS), High Level Architecture (HLA), Test and Training Enabling Architecture (TENA), and Distributed Data Services (DDS)) and data passing tools (Robot Operation System (ROS) and Lightweight Communication and Marshalling (LCM)) and compares their advantages and disadvantages. Three specific limitations in existing platforms are identified from the perspective of natural hazard simulation. For mitigating the identified limitations, two platform design recommendations are provided, namely message exchange wrappers and hybrid communication, to help improve data passing capabilities in existing solutions and provide some guidance for the design of a new domain-specific distributed simulation framework.

Keywords

Civil infrastructure / Data passing tools / Distributed simulation platforms / Hybrid communication / Message exchange wrapper / Natural hazards

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Lichao Xu, Szu-Yun Lin, Andrew W. Hlynka, Hao Lu, Vineet R. Kamat, Carol C. Menassa, Sherif El-Tawil, Atul Prakash, Seymour M. J. Spence, Jason McCormick. Distributed Simulation Platforms and Data Passing Tools for Natural Hazards Engineering: Reviews, Limitations, and Recommendations. International Journal of Disaster Risk Science, 2021, 12(5): 617-634 DOI:10.1007/s13753-021-00361-7

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References

[1]

AIR Worldwide. 2020. AIR hurricane model for the United States. https://www.air-worldwide.com/siteassets/Publications/Brochures/documents/AIR-Hurricane-Model-for-the-United-States-Brochure. Accessed 20 Apr 2021.

[2]

ARA (Applied Research Associates). 2021. HurLoss hurricane catastrophe model. https://www.ara.com/hurloss/. Accessed 20 Apr 2021.

[3]

Arguello L, Miró J. Distributed interactive simulation for space projects. ESA Bulletin, 2000, 102: 125-130.

[4]

Azar, E., and C.C. Menassa. 2010. A conceptual framework to energy estimation in buildings using agent based modeling. In Proceedings of the Winter Simulation Conference 2019: Simulation for Risk Management, 8–11 December 2019, Baltimore, MD, USA.

[5]

Barrett, C., R. Beckman, K. Channakeshava, F. Huang, V.S.A. Kumar, A. Marathe, M.V. Marathe, and G. Pei. 2010. Cascading failures in multiple infrastructures: From transportation to communication network. Paper presented at the 2010 5th International Conference on Critical Infrastructure (CRIS), 20–22 September 2010, Beijing, China.

[6]

Behner, H., and B. Lofstrand. 2017. The new HLA certification process in NATO. Paper presented at the Symposium on M&S Technologies and Standards for Enabling Alliance Interoperability and Pervasive M&S Applications, 19–20 October 2017, Lisbon, Portugal.

[7]

Bunea, G., F. Leon, and G.M. Atanasiu. 2016. Postdisaster evacuation scenarios using multiagent system. Journal of Computing in Civil Engineering 30(6): Article 05016002.

[8]

Calytrix Technologies. 2018. Portico. http://porticoproject.org. Accessed 23 Dec 2018.

[9]

Chen S-C, Chen M, Zhao N, Hamid S, Chatterjee K, Armella M. Florida public hurricane loss model: Research in multi-disciplinary system integration assisting government policy making. Government Information Quarterly, 2009, 26(2): 285-294

[10]

Cozby, R. 1998. Foundation initiative 2010. Army Test and Evaluation Command, Aberdeen Proving Ground, MD, USA.

[11]

D.S. Committee. 2012. IEEE standard for distributed interactive simulation – Application protocols. IEEE Standard 1278.12012. Piscataway, NJ: IEEE Standards Association.

[12]

Dahmann JS, Kuhl F, Weatherly R. Standards for simulation: As simple as possible but not simpler the high level architecture for simulation. Simulation, 1998, 71(6): 378-387

[13]

Dai K, Zhao W, Zhang H, Shi G, Zhang Y. TENA based implementation technology for virtual test. Journal of System Simulation, 2011

[14]

DIS Steering Committee. 1998. IEEE standard for distributed interactive simulation – Application protocols. IEEE Standard 1278.1a1998. Piscataway, NJ: IEEE Standards Association.

[15]

Dong, S., and V.R. Kamat. 2010. Robust mobile computing framework for visualization of simulated processes in augmented reality. In Proceedings of the winter simulation conference, 5–8 Decemebr 2010, Baltimore, MD, USA.

[16]

Eusgeld, I., and C. Nan. 2009. Creating a simulation environment for critical infrastructure interdependencies study. Paper presented at the IEEE international conference on industrial engineering and engineering management (IEEM), 8–11 December 2009, Hong Kong, China.

[17]

Eusgeld I, Nan C, Dietz S. “System-of-systems” approach for interdependent critical infrastructures. Reliability Engineering & System Safety, 2011, 96(6): 679-686

[18]

Fiedrich, F. 2006. An HLA-based multiagent system for optimized resource allocation after strong earthquakes. In Proceedings of the 38th conference on winter simulation, December 2006, Monterey, CA, USA.

[19]

Ginting, B.M., and R.-P. Mundani. 2019. Parallel flood simulations for wet-dry problems using dynamic load balancing concept. Journal of Computing in Civil Engineering 33(3): Article 04019013.

[20]

Grogan PT, De Weck OL. Infrastructure system simulation interoperability using the high-level architecture. IEEE Systems Journal, 2015, 12(1): 103-114

[21]

HLA Working Group. 2000. IEEE standard for modeling and simulation (M&S) high level architecture (HLA) – Framework and rules. IEEE Standard 15162000. Piscataway, NJ: IEEE Standards Association.

[22]

HLA Working Group. 2010. IEEE standard for modeling and simulation (M&S) high level architecture (HLA) – Framework and rules. IEEE Standard 15162010 (Revision of IEEE Standard 15162000). Piscataway, NJ: IEEE Standards Association.

[23]

Hollenbach, J.W. 2009. Inconsistency, neglect, and confusion: A historical review of DoD distributed simulation architecture policies. In Proceedings of the spring simulation interoperability workshop, 23–27 March 2009, San Diego, CA, USA.

[24]

Hori M. Introduction to computational earthquake engineering, 2011, Hackensack, NJ: World Scientific

[25]

Hori M, Ichimura T. Current state of integrated earthquake simulation for earthquake hazard and disaster. Journal of Seismology, 2008, 12(2): 307-321

[26]

Huang, A.S., E. Olson, and D.C. Moore. 2010. LCM: Lightweight communications and marshalling. Paper presented at the IEEE/RSJ international conference on intelligent robots and systems (IROS), 18–22 October 2010 Taipei.

[27]

Hwang, S., R. Starbuck, S. Lee, M. Choi, S. Lee, and M. Park. 2016. High level architecture (HLA) compliant distributed simulation platform for disaster preparedness and response in facility management. In Proceedings of the winter simulation conference, 11–14 December 2016, Washington, DC, USA.

[28]

IoTivity. 2018. IoTivity. https://www.iotivity.org. Accessed 18 Dec 2018.

[29]

Jain, S., and C. McLean. 2003. Simulation for emergency response: A framework for modeling and simulation for emergency response. In Proceedings of the 35th conference on winter simulation: Driving innovation, December 2003, New Orleans, LA, USA.

[30]

Kamat, V.R., and J.C. Martinez. 2002. Comparison of simulation-driven construction operations visualization and 4D CAD. In Proceedings of the Winter Simulation Conference, 8–11 December 2002, San Diego, CA, USA.

[31]

Karen Clark & Company. 2020. RiskInsight® open loss modeling platform. https://www.karenclarkandco.com/riskinsight/. Accessed 21 Apr 2021.

[32]

Kerber S, Milke JA. Using FDS to simulate smoke layer interface height in a simple atrium. Fire Technology, 2007, 43(1): 45-75

[33]

Koliou M, van de Lindt JW, McAllister TP, Ellingwood BR, Dillard M, Cutler H. State of the research in community resilience: Progress and challenges. Sustainable and Resilient Infrastructure, 2017

[34]

Lazarov, B., G. Kirov, P. Zlateva, and D. Velev. 2015. Network-centric operations for crisis management due to natural disasters. International Journal of Innovation, Management and Technology 6(4): Article 252.

[35]

Lin, S.-Y., and S. El-Tawil. 2020. Time-dependent resilience assessment of seismic damage and restoration of interdependent lifeline systems. Journal of Infrastructure Systems 26(1): Article 04019040.

[36]

Lin, S.-Y., W.-C. Chuang, L. Xu, S. El-Tawil, S.M.J. Spence, V.R. Kamat, C.C. Menassa, and J. McCormick. 2019. Framework for modeling interdependent effects in natural disasters: Application to wind engineering. Journal of Structural Engineering 145(5): Article 04019025.

[37]

Lin, S.-Y., L. Xu, W.-C. Chuang, S. El-Tawil, S.M.J. Spence, V.R. Kamat, C.C. Menassa, and J. McCormick. 2018. Modeling interactions in community resilience. Paper presented at the Structures Conference, 19–21 April 2018, Fort Worth, TX, USA.

[38]

Liu, K., X. Shen, N.D. Georganas, A. El Saddik, and A. Boukerche. 2007. SimSITE: The HLA/RTI based emergency preparedness and response training simulation. In Proceedings of the international symposium on distributed simulation and real-time applications, 22–24 October 2007, Chania, Greece.

[39]

MÄK Technologies. 2018. MÄK high performance RTI. http://www.mak.com/products/link/mak-rti. Accessed 23 Dec 2018.

[40]

Mandiak, M., P. Shah, Y. Kim, and T. Kesavadas. 2005. Development of an integrated GUI framework for post-disaster data fusion visualization. Paper presented at the international conference on information fusion, 25–28 July 2005, Philadelphia, PA, USA.

[41]

McGregor, D., D. Brutzman, and J. Grant. 2008. Open-DIS: An open source implementation of the DIS protocol for C++ and Java. Paper presented at the simulation interoperability workshop (SIW) of simulation interoperability standards organization (SISO), 15–19 September 2008, Orlando, FL, USA, Paper No. 08F-SIW-051.

[42]

Miles SB, Chang SE. Modeling community recovery from earthquakes. Earthquake Spectra, 2006, 22(2): 439-458

[43]

Miller DC, Thorpe JA. SIMNET: The advent of simulator networking. Proceedings of the IEEE, 1995, 83(8): 1114-1123

[44]

Mitsova D. Integrative interdisciplinary approaches to critical infrastructure interdependency analysis. Risk Analysis, 2018

[45]

Nan C, Eusgeld I. Adopting HLA standard for interdependency study. Reliability Engineering & System Safety, 2011, 96(1): 149-159

[46]

NIST (National Institute of Standards and Technology). 2016a. Community resilience planning guide for buildings and infrastructure systems, Vol. I. National Institute of Standards and Technology, Gaithersburg, MD, USA.

[47]

NIST (National Institute of Standards and Technology). 2016b. Community resilience planning guide for buildings and infrastructure systems, Vol. II. National Institute of Standards and Technology, Gaithersburg, MD, USA.

[48]

NRC (National Research Council) Grand challenges in earthquake engineering research: A community workshop report, 2011, Washington, DC: National Academies Press

[49]

NRC (National Research Council) National earthquake resilience: Research, implementation, and outreach, 2011, Washington, DC: National Academies Press

[50]

OMG (Object Management Group). 2004. Data Distribution Service (DDS), Version 1.0. https://www.omg.org/spec/DDS/1.0/About-DDS/. Accessed 19 Dec 2018.

[51]

OMG (Object Management Group). 2015. Data Distribution Service (DDS), Version 1.4. https://www.omg.org/spec/DDS/1.4. Accessed 19 Dec 2018.

[52]

OMG (Object Management Group). 2018. Open DDS. http://opendds.org/. Accessed 19 Dec 2018.

[53]

ONERA. 2018. CERTI. http://savannah.nongnu.org/projects/certi/. Accessed 23 Dec 2018.

[54]

Pinelli J-P, Esteva M, Rathje EM, Roueche D, Brandenberg SJ, Mosqueda G, Padgett J, Haan F. Disaster risk management through the DesignSafe cyberinfrastructure. International Journal of Disaster Risk Science, 2020, 11(6): 719-734

[55]

Pitch Technologies. 2018. Pitch pRTI. http://pitchtechnologies.com/products/prti/. Accessed 23 Dec 2018.

[56]

Powell ET, Noseworthy JR. Tolk A. The test and training enabling architecture (TENA). Engineering principles of combat modeling and distributed simulation, 2012, Hoboken, NJ: John Wiley & Sons 449-479

[57]

Rathje, E.M., C. Dawson, J.E. Padgett, J.-P. Pinelli, D. Stanzione, A. Adair, P. Arduino, S.J. Brandenberg, et al. 2017. DesignSafe: New cyberinfrastructure for natural hazards engineering. Natural Hazards Review 18(3): Article 06017001.

[58]

Rathje, E.M., C. Dawson, J.E. Padgett, J.-P. Pinelli, D. Stanzione, P. Arduino, S.J. Brandenberg, T. Cockerill, et al. 2020. Enhancing research in natural hazards engineering through the DesignSafe cyberinfrastructure. Frontiers in Built Environment 6: Article 213.

[59]

Ray PP, Mukherjee M, Shu L. Internet of things for disaster management: State-of-the-art and prospects. IEEE Access, 2017, 5: 18818-18835

[60]

Real-time Innovations. 2018. RTI connext DDS professional 2014. https://www.rti.com/products/connext-dds-professional. Accessed 19 Dec 2018.

[61]

Reinhorn AM, Manolis GD, Wen CY. Active control of inelastic structures. Journal of Engineering Mechanics, 1987, 113(3): 315-333

[62]

Ren, A., C. Chen, J. Shi, and L. Zou. 2007. Application of virtual reality technology to evacuation simulation in fire disaster. In Proceedings of the International conference on computer graphics & virtual reality (CGVR), 25–28 June 2007, Las Vegas, NV, USA.

[63]

Risk Management Solutions. 2007. RMS™ U.S. hurricane model. https://www.sbafla.com/method/Portals/Methodology/ModelSubmissions/2006/2006_RMS06Standards_06_20_07.pdf. Accessed 20 Apr 2021.

[64]

ROS.org. 2018. ROS-introduction. http://wiki.ros.org/ROS/Introduction. Accessed 9 Dec 2018.

[65]

Sahin, A., R. Sisman, A. Askan, and M. Hori. 2016. Development of integrated earthquake simulation system for Istanbul. Earth, Planets and Space 68(1): Article 115.

[66]

Team Oasis. 2021. Oasis loss modelling framework. https://oasislmf.org/. Accessed 21 Apr 2021.

[67]

Thomas, A., C.C. Menassa, and V.R. Kamat. 2017. Lightweight and adaptive building simulation (LABS) framework for integrated building energy and thermal comfort analysis. Building Simulation – An International Journal 10: 1023–1044.

[68]

VT MAK. 2018. VR-Link. https://www.mak.com/products/link/vr-link#vr-link-supports-dis. Accessed 18 Dec 2018

[69]

Wang Y, Chen C, Wang J, Baldick R. Research on resilience of power systems under natural disasters – A review. IEEE Transactions on Power Systems, 2015, 31(2): 1604-1613

[70]

Xie, H., N.N. Weerasekara, and R.R.A. Issa. 2016. Improved system for modeling and simulating stadium evacuation plans. Journal of Computing in Civil Engineering 31(3): Article 04016065.

[71]

Xiong C, Lu X, Guan H, Xu Z. A nonlinear computational model for regional seismic simulation of tall buildings. Bulletin of Earthquake Engineering, 2016, 14(4): 1047-1069

[72]

Xu L, Feng C, Kamat VR, Menassa CC. An occupancy grid mapping enhanced visual SLAM for real-time locating applications in indoor GPS-denied environments. Automation in Construction, 2019, 104: 230-245

[73]

Xu L, Kamat VR, Menassa CC. Automatic extraction of 1D barcodes from video scans for drone-assisted inventory management in warehousing applications. International Journal of Logistics Research and Applications, 2018, 21(3): 243-258

[74]

Yong Y, Jin Y. Marine simulator and distributed interactive simulation technology. Computer Simulation, 2000, 17(6): 66-68.

[75]

Zobel RN, Tandayya P, Duerrast H. Modelling and simulation of the impact of tsunami waves at beaches and coastlines for disaster reduction in Thailand. International Journal of Simulation, 2006, 7(4–5): 40-50.

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