Evaluation of Multi-agent Architecture for Structural Damage Detection and Location

In this paper the results of using a Multi-agent system (MAS) for Structural Health Monitoring (SHM) are detailed. A study between different MAS architectures reported in literature is presented in order to select and adapt the most adequate one for SHM tasks. Requirements are established according...

Full description

Autores:
Quintero-Parra, Andres F.
Camacho-Navarro, Jhonatan
Flórez, Marco
Vázquez-González, J.L.
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
spa
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/1745
Acceso en línea:
http://hdl.handle.net/20.500.12442/1745
Palabra clave:
Artificial Intelligence
Intelligent Agent
Multiagent Architecture
Structural Health Monitoring
Rights
License
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
id USIMONBOL2_599eb5bcf823a72cde6b09f6c0bd0477
oai_identifier_str oai:bonga.unisimon.edu.co:20.500.12442/1745
network_acronym_str USIMONBOL2
network_name_str Repositorio Digital USB
repository_id_str
dc.title.eng.fl_str_mv Evaluation of Multi-agent Architecture for Structural Damage Detection and Location
title Evaluation of Multi-agent Architecture for Structural Damage Detection and Location
spellingShingle Evaluation of Multi-agent Architecture for Structural Damage Detection and Location
Artificial Intelligence
Intelligent Agent
Multiagent Architecture
Structural Health Monitoring
title_short Evaluation of Multi-agent Architecture for Structural Damage Detection and Location
title_full Evaluation of Multi-agent Architecture for Structural Damage Detection and Location
title_fullStr Evaluation of Multi-agent Architecture for Structural Damage Detection and Location
title_full_unstemmed Evaluation of Multi-agent Architecture for Structural Damage Detection and Location
title_sort Evaluation of Multi-agent Architecture for Structural Damage Detection and Location
dc.creator.fl_str_mv Quintero-Parra, Andres F.
Camacho-Navarro, Jhonatan
Flórez, Marco
Vázquez-González, J.L.
dc.contributor.author.none.fl_str_mv Quintero-Parra, Andres F.
Camacho-Navarro, Jhonatan
Flórez, Marco
Vázquez-González, J.L.
dc.subject.eng.fl_str_mv Artificial Intelligence
Intelligent Agent
Multiagent Architecture
Structural Health Monitoring
topic Artificial Intelligence
Intelligent Agent
Multiagent Architecture
Structural Health Monitoring
description In this paper the results of using a Multi-agent system (MAS) for Structural Health Monitoring (SHM) are detailed. A study between different MAS architectures reported in literature is presented in order to select and adapt the most adequate one for SHM tasks. Requirements are established according to recent solutions, where main parameters are type and number of sensors and communication protocols, among others. MAS technique uses several intelligent agents, that are algorithms able to act in a reactive or active way. Their action depends on surrounding environment or collected data. These agents can work in a decentralized way, searching the fulfillment of an individual goal or they can work with another system to achieve a common goal. Decision is based on their internal state (beliefs, goals and commitments). MAS’ effectiveness depends on the interconnection between the agents. Type of agents is defined according to its communication method and protocol, common and individual goals, among others. Decentralization and versatility are two important characteristics of MAS technique useful to solve SHM problem. This is one of the main motivations to consider this technique to be a good approach for the studied problem. A benchmark numerical model, which consists of a metallic framework, was used to validate and demonstrate the feasibility of the selected architecture for SHM.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2018-03-01T22:42:15Z
dc.date.available.none.fl_str_mv 2018-03-01T22:42:15Z
dc.type.spa.fl_str_mv article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.identifier.issn.none.fl_str_mv 18612121
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12442/1745
identifier_str_mv 18612121
url http://hdl.handle.net/20.500.12442/1745
dc.language.iso.eng.fl_str_mv spa
language spa
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
rights_invalid_str_mv Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
dc.publisher.spa.fl_str_mv International Association of Online Engineering
dc.source.eng.fl_str_mv International Journal of Online Engineering
Vol. 13, No.06 (2017)
institution Universidad Simón Bolívar
dc.source.uri.none.fl_str_mv http://online-journals.org/index.php/i-joe/article/view/7184
bitstream.url.fl_str_mv https://bonga.unisimon.edu.co/bitstreams/abc87598-7c4e-4b55-88b4-c6f629c176a7/download
https://bonga.unisimon.edu.co/bitstreams/b20b919a-b022-49fc-9923-9801f4afa534/download
https://bonga.unisimon.edu.co/bitstreams/d5d4af27-7422-4562-97b7-2a5ceb2eedd6/download
https://bonga.unisimon.edu.co/bitstreams/d6464c9f-5ec5-42ac-b44c-0b0ad3e1ecc6/download
https://bonga.unisimon.edu.co/bitstreams/93a73d77-116e-44c1-997f-41c4911b43d9/download
https://bonga.unisimon.edu.co/bitstreams/2090acd4-8955-4cdd-823f-30f01f985c60/download
bitstream.checksum.fl_str_mv 6c2514cfd92537069c810c615ceaa732
8a4605be74aa9ea9d79846c1fba20a33
9e0e71342aa4e6bd697fc919191b105c
75f21f92184df8ee5def1c1a85f1fbf3
352e86be4b68425ac3be9351629f23fd
df2721ae20feb2806f97d09bb221b8a2
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Digital Universidad Simón Bolívar
repository.mail.fl_str_mv repositorio.digital@unisimon.edu.co
_version_ 1814076143852060672
spelling Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Quintero-Parra, Andres F.5ae86b85-d852-48dc-bd60-42cb5dcd7070-1Camacho-Navarro, Jhonatana986b522-f0f2-4409-a9ba-5f866650e84d-1Flórez, Marco338304ad-c361-45b2-a1b1-ba014563caae-1Vázquez-González, J.L.8649ff77-c209-4a66-8774-d48fb0e9435b-12018-03-01T22:42:15Z2018-03-01T22:42:15Z201718612121http://hdl.handle.net/20.500.12442/1745In this paper the results of using a Multi-agent system (MAS) for Structural Health Monitoring (SHM) are detailed. A study between different MAS architectures reported in literature is presented in order to select and adapt the most adequate one for SHM tasks. Requirements are established according to recent solutions, where main parameters are type and number of sensors and communication protocols, among others. MAS technique uses several intelligent agents, that are algorithms able to act in a reactive or active way. Their action depends on surrounding environment or collected data. These agents can work in a decentralized way, searching the fulfillment of an individual goal or they can work with another system to achieve a common goal. Decision is based on their internal state (beliefs, goals and commitments). MAS’ effectiveness depends on the interconnection between the agents. Type of agents is defined according to its communication method and protocol, common and individual goals, among others. Decentralization and versatility are two important characteristics of MAS technique useful to solve SHM problem. This is one of the main motivations to consider this technique to be a good approach for the studied problem. A benchmark numerical model, which consists of a metallic framework, was used to validate and demonstrate the feasibility of the selected architecture for SHM.spaInternational Association of Online EngineeringInternational Journal of Online EngineeringVol. 13, No.06 (2017)http://online-journals.org/index.php/i-joe/article/view/7184Artificial IntelligenceIntelligent AgentMultiagent ArchitectureStructural Health MonitoringEvaluation of Multi-agent Architecture for Structural Damage Detection and Locationarticlehttp://purl.org/coar/resource_type/c_6501C.R. Farrar and S. Doebling, “A Review of Structural Health Monitoring Literature : 1996-2001,” Structural Health Monitoring, 2003, pp. 1996-2001.N.R. Jennings and W. College, “Applications of Intelligent Agents,” Intelligent agents, 1996.W. Jacak, K. Pro, and S. Dreiseitl, “Conflict Management in an Intelligent Multiagent Robotic System,” IEEE International Conference on Systems, Man, and Cybernetics, Nashville: 2000, pp. 1793-1798.H. Zhu, “A role-based architecture for intelligent agent systems,” Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications, 2006.S.Z.H. Zaidi, S.S.R. Abidi, M. S., and Y.-N. Cheah, “ADMI: A Multi-Agent Architecture to Autonomously Generate Data Mining Services,” International conference on intelligent systems, IEEE, 2004, pp. 273-279.P. Avitabile, “Experimental Modal Analysis ( A Simple Non-Mathematical Presentation ),” Modal Analysis, 2004, pp. 1-15.H. Voos, M.A. Lopez-Carmona, M.-M. Iván, and J.R. Velasco, Multiagent Systems, In-Teh, 2009.M. Scheutz and V. Andronache, The APOC Framework for the Comparison of Agent Architectures, Notre Dame: 2004.L. Kong and L. Xiao, “A Multi-Layered Control Architecture of Intelligent Agent,” IEEE International Conference on Control and Automation, Guangzhou: 2007, pp. 1454-1458.B. Horling, R. Mailler, and V. Lesser, “A Case Study of Organizational Effects in a Distributed Sensor Network,” IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2004, pp. 51-57.W.R. Otte, J.S. Kinnebrew, D.C. Schmidt, and G. Biswas, “A flexible infrastructure for distributed deployment in adaptive sensor webs,” 2009 IEEE Aerospace conference, Mar. 2009, pp. 1-12.A. Kazandzhiev, I. Momtchev, L. Popova, and D. Shikalanov, “Distributed multi-agent based approaches,” International Conference on Integration of Knowledge Intensive Multi-Agent Systems, Waltham: Ieee, 2005, pp. 3-8.S. Ibarra, C. Quintero, J. De La Rosa, and J. R. Castan, “An Approach based on New Coordination Mechanisms to Improve the Teamwork of Cooperative Intelligent Agents,” 2006 Seventh Mexican International Conference on Computer Science, Sep. 2006, pp. 164-172.T. Fukuda, I. Takagawa, and Y. Hasegawa, “From intelligent robot to multi-agent robotic system self-organizing robotic systems,” International Conference on Integration of Knowledge Intensive Multi-Agent Systems, Boston: 2003, pp. 413-417.X. Zhao, S. Yuan, Z. Yu, W. Ye, and J. Cao, “Designing strategy for multi-agent system based large structural health monitoring,” Expert Systems with Applications, vol. 34, 2008, pp. 1154-1168.S.E. Lander, “Issues in multiagent design systems,” IEEE Expert, vol. 12, Mar. 1997, pp. 18-26.M. Wooldridge, N.R. Jennings, and D. Kinny, “The Gaia methodology for agent-oriented analysis and design,” Proceedings of the third annual conference on Autonomous Agents, 1999, pp. 69-76.H.S. Nwana, D.T. Ndumu, L.C. Lee, B.T. Laboratories, and M. Heath, ZEUS: An Advanced Tool-Kit for Engineering Distributed Multi-Agent Systems, Ipswich, Suffolk: 2006.X. Zhao, S. Yuan, Z. Yu, W. Ye, and J. Cao, “Designing strategy for multi-agent system based large structural health monitoring,” Expert Systems with Applications, vol. 34, 2008, pp. 1154-1168.S. Yuan, X. Lai, X. Zhao, X. Xu, and L. Zhang, “Distributed structural health monitoring system based on smart wireless sensor and multi-agent technology,” Smart Materials and Structures, vol. 15, 2006, pp. 1-8.A.F. Quintero-Parra and R. Villamizar-Mejía, “Estado del arte en monitorización de salud estructural: un enfoque basado en agentes inteligentes,” Ciencia e Ingeniería neogranadina, vol. 20, 2010, pp. 1-14.F. Klügl, “A validation methodology for agent-based simulations,” Proceedings of the 2008 ACM symposium on Applied computing, New York, New York, USA: ACM Press, 2008, p. 39.R. Fuentes, J.J. Gómez-sanz, and J. Pavón, “Verification and Validation Techniques for Multi- Agent Systems,” Upgrade, vol. 5, 2004, pp. 15-19.D.G. Fabio Bellifemine, Giovanni Caire, Developing Multi Agent Systems with JADE, John Wiley & Sons, 2007.J. Thangarajah and M. Winikoff, “Tool Support for Agent Development using the Prometheus Methodology,” Fifth International Conference on Quality Software, Ieee, 2005, pp. 383-388.Juan Martin Caicedo, “Structural Health Monitoring of Flexible Civil Structures,” Washington University, 2003.J. Wu, S. Yuan, S. Ji, G. Zhou, Y. Wang, and Z. Wang, “Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring,” Expert Systems with Applications, vol. 37, Mar. 2010, pp. 2028-2036Hoschke, N., Lewis, C. J., Price, D. C., Scott, D. A., Edwards, G. C., Batten, A., “A Self-Organizing Sensing System For Structural Health Management” Knowledge-Based Intelligent Information And Engineering Systems, Proceedings, Lecture Notes In Artificial Intelligence, 2006, Páginas: 349-357Taylor, Stuart G., Farinholt, Kevin M., Flynn, Eric B., Figueiredo, Eloi, Mascarenas, David L., Moro, Erik A., Park, Gyuhae, Todd, Michael D., Farrar, Charles R., “A Mobile-Agent-Based Wireless Sensing Network For Structural Monitoring Applications” Measurement Science & Technology, Abril 2009.Zhou, Hengbao, Sun, Hongbing, Qiu, Lei, “An Evaluation on the Multi-Agent System Based Structural Health Monitoring For Large Scale Structures”, Expert Systems with Applications, 2008, Páginas: 4900-4914ORIGINALPDF.pdfPDF.pdfFormato Pdf texto completoapplication/pdf360645https://bonga.unisimon.edu.co/bitstreams/abc87598-7c4e-4b55-88b4-c6f629c176a7/download6c2514cfd92537069c810c615ceaa732MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://bonga.unisimon.edu.co/bitstreams/b20b919a-b022-49fc-9923-9801f4afa534/download8a4605be74aa9ea9d79846c1fba20a33MD52TEXTEvaEvaluation of Multi-agent Architecture for Structural Damage Detection and Location_Post-Print.pdf.txtEvaEvaluation of Multi-agent Architecture for Structural Damage Detection and Location_Post-Print.pdf.txtExtracted texttext/plain23303https://bonga.unisimon.edu.co/bitstreams/d5d4af27-7422-4562-97b7-2a5ceb2eedd6/download9e0e71342aa4e6bd697fc919191b105cMD53PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain23786https://bonga.unisimon.edu.co/bitstreams/d6464c9f-5ec5-42ac-b44c-0b0ad3e1ecc6/download75f21f92184df8ee5def1c1a85f1fbf3MD55THUMBNAILEvaEvaluation of Multi-agent Architecture for Structural Damage Detection and Location_Post-Print.pdf.jpgEvaEvaluation of Multi-agent Architecture for Structural Damage Detection and Location_Post-Print.pdf.jpgGenerated Thumbnailimage/jpeg1364https://bonga.unisimon.edu.co/bitstreams/93a73d77-116e-44c1-997f-41c4911b43d9/download352e86be4b68425ac3be9351629f23fdMD54PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg3538https://bonga.unisimon.edu.co/bitstreams/2090acd4-8955-4cdd-823f-30f01f985c60/downloaddf2721ae20feb2806f97d09bb221b8a2MD5620.500.12442/1745oai:bonga.unisimon.edu.co:20.500.12442/17452024-07-26 03:09:35.11open.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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