Load-Capacity Rating of Bridge Populations through Machine Learning: Application of Decision Trees and Random Forests
Publication: Journal of Bridge Engineering
Volume 22, Issue 10
Abstract
The functionality of the U.S. transportation infrastructure system is dependent upon the health of an aging network of over 600,000 bridges, and agencies responsible for maintaining these bridges rely on the process of load rating to assess the adequacy of individual structures. This paper presents a new approach for safety screening and load-capacity evaluation of large bridge populations that seeks to uncover heretofore unseen patterns within the National Bridge Inventory database and establish relationships between select bridge attributes and their load-capacity status. Decision-tree and random-forest classification models were trained on the national concrete slab bridge data set of over 40,000 structures. The resulting models were validated on an independent data set and then compared with a number of existing judgment-based schemes found in an extensive survey of the current state of practice in the United States. The proposed approach offers a method that provides guidance for improved allocation of resources by informing maintenance decisions through rapid identification of candidate bridges that require further scrutiny for either possible load restriction or restriction removal.
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Acknowledgments
This work was part of a project sponsored by the Virginia Department of Transportation (VDOT) on load rating bridges with missing or insufficient as-built information. The authors thank Dr. Bernie Kassner from the Virginia Transportation Research Council (VTRC), Jonathan Mallard from the VDOT, and Dr. Amir Gheitasi for their assistance during the study and manuscript preparation. The contents of this paper reflect the views of the authors, who are responsible for the facts and accuracy of the presented data, but do not necessarily reflect the official views of the VDOT.
References
AASHTO. (2011). The manual for bridge evaluation, 2nd Ed., Washington, DC.
AASHTO. (2012). LRFD bridge design specifications, 6th Ed., Washington, DC.
Alipour, M., Gheitasi, A., Harris, D. K., Ozbulut, O. E., and Barnes, L. E. (2016). “A data-driven approach for automated operational safety evaluation of the national inventory of reinforced concrete slab bridges.” Transportation Research Board (TRB) 95th Annual Meeting, Transportation Research Board, Washington, DC.
Amiri, M., Ardeshir, A., Fazel Zarandi, M. H. and Soltanaghaei, E. (2016). “Pattern extraction for high-risk accidents in the construction industry: A data-mining approach.” Int. J. Inj. Control Saf. Promotion, 23(3), 264–276.
ASCE. (2013). “Report card for America’s infrastructure.” 〈http://www.infrastructurereportcard.org/〉 (July 25, 2015).
Bektas, B. A., Carriquiry, A., and Smadi, O. (2013). “Using classification trees for predicting national bridge inventory condition ratings.” J. Infrastruct. Syst., 425–433.
Breiman, L. (2001). “Random forests.” Mach. Learn., 45(1), 5–32.
Chawla, N. V. (2005). “Data mining for imbalanced datasets: An overview.” Data mining and knowledge discovery handbook, Springer, New York, 853–867.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). “SMOTE: Synthetic minority over-sampling technique.” J. Artif. Intell. Res., 16, 321–357.
CODOT (Colorado Dept. of Transportation). (2011). Bridge rating manual, Staff Bridge Branch, Denver, CO.
Dasu, T., and Johnson, T. (2003). Exploratory data mining and data cleaning, Vol. 479, John Wiley and Sons, Hoboken, NJ.
DHS (Dept. of Homeland Security). (2013). “What is critical infrastructure?” 〈http://www.dhs.gov/what-critical-infrastructure〉 (July 25, 2015).
Farrar, C. R., and Worden, K. (2012). Structural health monitoring: A machine learning perspective, John Wiley and Sons, Chichester, U.K.
Fawcett, T. (2006). “An introduction to ROC analysis.” Pattern Recognit. Lett., 27(8), 861–874.
FDOT (Florida Dept. of Transportation). (2014). Bridge load rating manual, Office of Maintenance, Tallahassee, FL.
FHWA (Federal Highway Administration). (2014). “National Bridge Inventory Database.” 〈https://www.fhwa.dot.gov/bridge/nbi.cfm〉. (July 25, 2015).
Gheitasi, A., and Harris, D. K. (2015). “Implications of overload distribution behavior on load rating practices in steel stringer bridges.” Transp. Res. Rec., 2522, 47–56.
Gullien, J., et al. (2015). “Predictive models for severe sepsis in adult ICU patients.” Proc., Systems and Information Engineering Design Symp. (SIEDS), IEEE, New York.
Harris, D. K., Ozbulut, O. E., Alipour, M., Usmani, S., and Kassner, B. L. (2015). “Implications of load rating bridges in Virginia with limited design or as-built details.” Proc., 7th Int. Conf. on Structural Health Monitoring of Intelligent Infrastructure, Curran Associates, Inc., Red Hook, NY.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (Springer series in statistics), 2nd Ed., Springer, New York.
Hergenroeder, K., et al. (2014). “Automated prediction of adverse post-surgical outcomes.” Proc., Systems and Information Engineering Design Symp. (SIEDS), IEEE, New York.
Huang, J., and Ling, C. X. (2005). “Using AUC and accuracy in evaluating learning algorithms.” IEEE Trans. Knowl. Data Eng., 17(3), 299–310.
Huang, Y. H. (2010). “Artificial neural network model of bridge deterioration.” J. Perform. Constr. Facil., 597–602.
ITD (Idaho Transportation Dept.). (2014). Idaho manual for bridge evaluation, Boise, ID.
Jootoo, A., and Lattanzi D. (2016). “A machine learning approach to bridge prototyping.” Proc., Int. Conf. on Sustainable design, Engineering and Construction, ASCE Architectural Engineering Institute and Arizona State Univ.
Karl, T., and Koss, W. J. (1984) “Regional and national monthly, seasonal, and annual temperature weighted by area (Historical climatology series), National Climatic Data Center, Asheville, NC.
Kim, Y. J., and Yoon, D. K. (2010). “Identifying critical sources of bridge deterioration in cold regions through the constructed bridges in North Dakota.” J. Bridge Eng., 542–552,
KYTC (Kentucky Transportation Cabinet). (2015). Kentucky bridge inspection procedures manual, Division of Maintenance, Bridge Preservation Branch, Frankfort, KY.
Li, Z., and Burgueño, R. (2010). “Using soft computing to analyze inspection results for bridge evaluation and management.” J. Bridge Eng., 430–438.
MassDOT (Massachusetts Dept. of Transportation). (2013). LRFD bridge manual: Part I, Boston.
Melhem, H. G., and Cheng, Y. (2003). “Prediction of remaining service life of bridge decks using machine learning.” J. Comput. Civ. Eng., 1–9.
Melhem, H. G., Cheng, Y., Kossler, D., and Scherschligt, D. (2003). “Wrapper methods for inductive learning: Example application to bridge decks.” J. Comput. Civ. Eng., 46–57.
Mitchell, T. M. (1997). Machine learning, McGraw-Hill, Columbus, OH.
Morcous, G. (2005). “Modeling bridge deck deterioration by using decision tree algorithms.” Transp. Res. Rec., 11s, 509–516.
NBIS (National Bridge Inspection Standards). (2004). Code of Federal Regulations, No. 23CFR650, U.S. Government Printing Office, Washington, DC.
NCHRP (National Cooperative Highway Research Program). (2014). “State bridge load posting processes and practices—A synthesis of highway practice.” Synthesis 453, Washington, DC.
NDOR (Nebraska Dept. of Roads). (2010). Bridge inspection program manual, Bridge Division, Lincoln, NE.
ODOT (Oregon Dept. of Transportation). (2015). LRFR manual, Salem, OR.
OIG (Office of Inspector General). (2006). “Audit of oversight of load ratings and postings on structurally deficient bridges on the national highway system.” Rep. No. MH-2006-043, U.S. Dept. of Transportation, Washington, DC.
PennDOT (Pennsylvania Dept. of Transportation). (2010). Bridge safety inspection manual, Harrisburg, PA.
Quinlan, J. R. (1993). C4. 5: Programs for machine learning, Morgan Kauffmann, San Mateo, CA.
Saitta, S., Benny, R., and Smith, I. (2009). Data mining: Applications in civil engineering, VDM Verlag, Saarbrücken, Germany.
TxDOT (Texas Dept. of Transportation). (2013). Bridge inspection manual, Austin, TX.
UDOT (Utah Dept. of Transportation). (2014). Bridge management manual, Salt Lake City.
Visa, S., and Ralescu, A. (2005). “Issues in mining imbalanced data sets-a review paper.” Proc., 16th Midwest Artificial Intelligence and Cognitive Science Conf., Univ. of Dayton, Dayton, OH.
Weka 3.6 [Computer software]. University of Waikato, Hamilton, New Zealand.
Witten, I. H., Frank, E., and Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques, 3rd Ed., Morgan Kaufmann, Burlington, MA.
Witten, I. H., Frank, E., Trigg, L. E., Hall, M. A., Holmes, G., and Cunningham, S. J. (1999). “Weka: Practical machine learning tools and techniques with Java implementations.” Proc., ICONIP/ANZIIS/ANNES99 Workshop Future Directions for Intelligent Systems and Information Sciences, Asia-Pacific Neural Network Assembly, Dept. of Industry, Science and Tourism, Murdoch Univ., and Univ. of New South Wales.
WSDOT (Washington State Dept. of Transportation). (2015). Bridge inspection manual, Bridge Preservation Office, Olympia, WA.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 22 • Issue 10 • October 2017
Copyright
© 2017 American Society of Civil Engineers.
History
Received: Sep 8, 2016
Accepted: Apr 21, 2017
Published online: Aug 9, 2017
Published in print: Oct 1, 2017
Discussion open until: Jan 9, 2018
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