Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining
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VIEW THE REPLYPublication: Journal of Construction Engineering and Management
Volume 139, Issue 11
Abstract
In order to develop a realistic simulation model, it is critical to provide the model with factual input data based on the interactions and events that take place between real entities. However, the existing trend in simulation of construction fleet activities is based on estimating input parameters such as activity durations using expert judgments and assumptions. Not only may such estimations not be precise, but project dynamics can influence model parameters beyond expectation. Therefore, the simulation model may not be a proper and reliable representation of the real engineering system. In order to alleviate these issues and improve the current practice of construction simulation, a thorough approach is needed that enables the integration of field data into simulation modeling and systematic refinement of the resulting models. This paper describes the latest efforts by authors to design and test a novel methodology for multimodal-process data capturing, fusion, and mining that provides a solid basis for automated generation and refinement of simulation models that realistically represent construction fleet operations. Different modes of operational data are collected and fused to facilitate the discovery of operational knowledge required to create realistic simulation models. The developed algorithms are validated using laboratory scale experiments and analytical results are also provided. The main contribution of this research to the body of knowledge is that it lays the foundation to systematically investigate whether it is possible to robustly discover computer-interpretable knowledge patterns from heterogeneous field data in order to create or refine realistic simulation models from complex, unstructured, and evolving operations such as heavy construction and infrastructure projects.
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References
AbouRizk, S., and Shi, J. (1994). “Automated construction-simulation optimization.” J. Constr. Eng. Manage., 120(2), 374–385.
Akhavian, R., and Behzadan, A. H. (2011). “Dynamic data driven simulation of ongoing construction operations.” Proc., 3rd Int. and 9th Construction Specialty Conf., CSCE, Ottawa, ON, Canada.
Akhavian, R., and Behzadan, A. H. (2012). “An integrated data collection and analysis framework for remote monitoring and planning of construction operations.” Adv. Eng. Inf., 26(4), 749–761.
Akinci, B., Boukamp, F., Gordon, C., Huber, D., Lyons, C., and Park, K. (2006). “A formalism for utilization of sensor systems and integrated project models for active construction quality control.” Autom. Constr., 15(2), 124–138.
Ali, R., Ghani, U., and Saeed, A. (1998). “Data clustering and its applications.” 〈http://members.tripod.com/asim_saeed/paper.htm〉 (Jul. 23, 2013).
Andoh, A. R., Xing, S., and Hubo, C. (2012). “A framework of RFID and GPS for tracking construction site dynamics.” Proc., Construction Research Congress 2012, Construction Challenges in a Flat World, ASCE, Reston, VA, 818–827.
Banks, J. (1998). Handbook of simulation: Principles, methodology, advances, applications, and practice, Wiley, New York.
Behzadan, A. H., Aziz, Z., Anumba, C. J., and Kamat, V. R. (2008). “Ubiquitous location tracking for context-specific information delivery on construction sites.” Autom. Constr., 17(6), 737–748.
Berkhin, P. (2002). “A survey of clustering data mining techniques.”, Accrue Software, San Jose, CA, 1–56.
Bishop, C. M. (2006). Pattern recognition and machine learning, Springer, New York.
Brilakis, I., Park, M. W., and Jog, G. (2011). “Automated vision tracking of project related entities.” Adv. Eng. Inf., 25(4), 713–724.
Caldas, C. H., Grau, D. T., and Haas, C. T. (2006). “Using global positioning system to improve materials-locating processes on industrial projects.” J. Constr. Eng. Manage., 132(7), 741–749.
Chen, P., Buchheit, R. B., Garrett, J. H., Jr., and McNeil, S. (2005). “Web-vacuum: Web-based environment for automated assessment of civil infrastructure data.” J. Comput. Civ. Eng., 19(2), 137–147.
Chung, T. H., Mohamed, Y., and AbouRizk, S. M. (2006). “Bayesian updating application into simulation in the North Edmonton Sanitary Trunk tunnel project.” J. Constr. Eng. Manage., 132(8), 882–894.
Daneshgari, P., and Moore, H. (2009). “The secret to short-interval scheduling.”, Electrical Construction and Maintenance, Chicago, IL, 32–36.
Davis, W. J. (1998). “On-line simulation: Need and evolving research requirements.” Handbook of simulation, Wiley, New York, 465–516.
Duda, R. O., Hart, P. E., and Stork, D. G. (1973). Pattern classification and scene analysis, 2nd Ed., Wiley, New York.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1996). Advances in knowledge discovery and data mining, AAAI/MIT Press, Menlo Park, CA.
Ford, D. R., and Schroer, B. J. (1987). “An expert manufacturing simulation system.” Simulation, 48(5), 193–200.
Gong, J., and Caldas, C. H. (2010). “Computer vision-based video interpretation model for automated productivity analysis of construction operations.” J. Comput. Civ. Eng., 24(3), 252–263.
Grau, D. T., and Caldas, C. H. (2009). “Methodology for automating the identification and localization of construction components on industrial projects.” J. Comput. Civ. Eng., 23(1), 3–13.
Hajjar, D., and AbouRizk, S. (1999). “Simphony: An environment for building special purpose construction simulation tools.” Proc, of the 31st Conf. on Winter Simulation: Simulation—A bridge to the future, Vol. 2, Association for Computing Machinery (ACM), New York, 998–1006.
Han, S., Lee, S., and Peña-Mora, F. (2011). “Application of dimension reduction techniques for motion recognition: Construction worker behavior monitoring.” Proc., 2011 ASCE Int. Workshop on Computing in Civil Engineering, ASCE, Reston, VA, 19–22.
Heidorn, G. E. (1974). “English as a very high level language for simulation programming.” Proc., ACM SIGPLAN Notices, Association for Computing Machinery (ACM), New York, 91–100.
Jang, W. S., and Skibniewski, M. J. (2007). “Wireless sensor technologies for automated tracking and monitoring of construction materials utilizing Zigbee networks.” ASCE Construction Research Congress, ASCE, Reston, VA.
Kannan, G., and Vorster, M. (2000). “Development of an experience database for truck loading operations.” J. Constr. Eng. Manage., 126(3), 201–209.
Lee, W. J., Song, J. H., Kwon, S. W., Chin, S., Choi, C., and Kim, Y. S. (2008). “A gate sensor for construction logistics.” Proc., 25th Int. Symp. on Automation and Robotics in Construction, Institute of Internet and Intelligent Technologies, Vilnius, Lithunia, 100–105.
Ling, Q. (2011). “How many low-precision sensors are enough for reliable detection?” IEEE Trans. Aerosp. Electron. Syst., 47(4), 3001–3006.
Lu, M. (2003). “Simplified discrete-event simulation approach for construction simulation.” J. Constr. Eng. Manage., 129(5), 537–546.
MacKay, D. J. C. (1992). “Information-based objective functions for active data selection.” Neural Comput., 4(4), 590–604.
Martinez, J., and Ioannou, P. (1999). “General-purpose systems for effective construction simulation.” J. Constr. Eng. Manage., 125(4), 265–276.
Martinez, J., and Ioannou, P. G. (1994). “General purpose simulation with stroboscope.” Proc., 1994 Winter Simulation Conf. (WSC), Association for Computing Machinery (ACM), New York, 1159–1166.
Martinez, J. C. (1996). “Stroboscope: State and resource based simulation of construction processes.” Ph.D. dissertation, Univ. of Michigan, Ann Arbour, MI.
Mathewson, S. C. (1984). “The application of program generator software and its extensions to discrete event simulation modeling.” IIE Trans., 16(1), 3–18.
Mirkin, B. (2005). Clustering for data mining: A data recovery approach, Chapman and Hall/CRC, Boca Raton, FL.
Navon, R. (2005). “Automated project performance control of construction projects.” Autom. Constr., 14(4), 467–476.
Park, M. W., Palinginis, E., and Brilakis, I. (2012). “Detection of construction workers in video frames for automatic initialization of vision trackers.” Proc., Construction Research Congress 2012, Construction Challenges in a Flat World, ASCE, Reston, VA, 940–949.
Pradhan, A., and Akinci, B. (2012). “A taxonomy of reasoning mechanisms and data synchronization framework for road excavation productivity monitoring.” Adv. Eng. Inf., 26(3), 563–573.
Razavi, S. N., and Haas, C. T. (2012). “Reliability-based hybrid data fusion method for adaptive location estimation in construction.” J. Comput. Civ. Eng., 26(1), 1–10.
Rezazadeh Azar, E., and McCabe, B. (2012). “Vision-based recognition of dirt loading cycles in construction sites.” Construction Research Congress, ASCE, Reston, VA, 1042–1051.
Sall, J., Lehman, A., Stephens, M., and Creighton, L. (2012). JMP start statistics: A guide to statistics and data analysis using JMP, SAS Institute, Cary, NC.
Soibelman, L., and Kim, H. (2002). “Data preparation process for construction knowledge generation through knowledge discovery in databases.” J. Comput. Civ. Eng., 16(1), 39–48.
Son, Y. J., and Wysk, R. A. (2001). “Automatic simulation model generation for simulation-based, real-time shop floor control.” Comput. Ind., 45(3), 291–308.
Song, L., Cooper, C., and Lee, S. (2009). “Real-time simulation for look-ahead scheduling of heavy construction projects.” Proc., Construction Research Congress, ASCE, Reston, VA, 1318–1327.
Véjar, A., and Charpentier, P. (2012). “Generation of an adaptive simulation driven by product trajectories.” J. Intell. Manu., 23(6), 2667–2679.
Yang, J., Arif, O., Vela, P., Teizer, J., and Shi, Z. (2010). “Tracking multiple workers on construction sites using video cameras.” Adv. Eng. Inf., 24(4), 428–434.
Yuan, Y., Dogan, C. A., and Viegelahn, G. L. (1993). “A flexible simulation model generator.” Comput. Ind. Eng., 24(2), 165–175.
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Published In
Journal of Construction Engineering and Management
Volume 139 • Issue 11 • November 2013
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jan 31, 2013
Accepted: Jul 2, 2013
Published online: Aug 5, 2013
Published in print: Nov 1, 2013
Discussion open until: Jan 5, 2014
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