Analyzing Coating Conditions of Steel Bridges at Florida: A Data-Driven Approach
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 2
Abstract
Even with the continuous development of coating technologies, coating systems are susceptible to corrosion-induced premature failures and unable to meet the anticipated service life. Coating premature failures will negatively impact the safety, integrity, and longevity of steel bridge structural elements. The advancement of the data analytics approach (e.g., machine learning) offers an opportunity to leverage the wealth of historical bridge inspection and maintenance data collected by Departments of Transportation to evaluate coating conditions of steel bridges effectively and efficiently. This paper focuses on presenting a data-driven study that analyzes the conditions of coating systems of steel bridges in the state of Florida. Three machine learning algorithms were used in developing models that can be used to analyze steel bridge coating conditions based on data collected from the Bridge Management System of the Florida Department of Transportation. The result showed that the machine learning–based models were able to effectively predict steel bridge coating conditions. The -nearest neighbor (KNN) regression algorithm offered the best performance. Although the approach was currently applied to the steel bridges of Florida, it can be easily replicable for similar data sets from other states. The research contributes to the body of knowledge by offering a data-driven understanding of coating performance of steel bridge elements. The research has the potential to offer a valuable decision-making tool for transportation agencies to effectively and easily analyze or predict steel bridge coating conditions.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This material is partially based upon work supported by the Florida Department of Transportation (FDOT). The opinions, findings, and conclusions expressed in this material are those of the authors and not necessarily those of the FDOT or the US Department of Transportation.
References
Ahlgren, P., B. Jarneving, and R. Rousseau. 2003. “Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient.” JASIS&T 54 (6): 550–560. https://doi.org/10.1002/asi.102.42.
Almaliki, Z. 2019. “Do you know how to choose the right machine learning algorithm among 7 different types?” Accessed January 12, 2022. https://towardsdatascience.com/do-you-know-how-to-choose-the-right-machine-learning-algorithm-among-7-different-types-295d0b0c7f60.
Al-Sodani, K. A. A., O. S. B. Al-Amoudi, M. Maslehuddin, and M. Shameem. 2018. “Efficiency of corrosion inhibitors in mitigating corrosion of steel under elevated temperature and chloride concentration.” Constr. Build. Mater. 163 (Feb): 97–112. https://doi.org/10.1016/j.conbuildmat.2017.12.097.
Amra, I. A. A., and A. Y. Maghari. 2017. “Students performance prediction using KNN and Naïve Bayesian.” In Proc., 8th Int. Conf. on Information Technology, 909–913. New York: IEEE.
Appel, R., T. Fuchs, P. Dollár, and P. Perona. 2013. “Quickly boosting decision trees—Pruning underachieving features early.” In Proc., Int. Conf. on Machine Learning, 594–602. Brookline, MA: Journal of Machine Learning Research.
Asaithambi, S. 2017. “Why, how and when to scale your features.” Accessed January 12, 2022. https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e.
Assaad, R., and I. H. El-adaway. 2020. “Bridge infrastructure asset management system: Comparative computational machine learning approach for evaluating and predicting deck deterioration conditions.” J. Infrastruct. Syst. 26 (3): 04020032. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000572.
Awada, M., F. J. Srour, and I. M. Srour. 2021. “Data-driven machine learning approach to integrate field submittals in project scheduling.” J. Manage. Eng. 37 (1): 04020104. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000873.
Bi, Q., K. E. Goodman, J. Kaminsky, and J. Lessler. 2019. “What is machine learning? A primer for the epidemiologist.” Am. J. Epidemiol. 188 (12): 2222–2239. https://doi.org/10.1093/aje/kwz189.
Borchani, H., G. Varando, C. Bielza, and P. Larranaga. 2015. “A survey on multi-output regression.” Wiley Interdiscip. Rev.: Data. Min. Knowl. Discov. 5 (5): 216–233. https://doi.org/10.1002/widm.1157.
Botchkarev, A. 2018. “Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology.” Accessed January 12, 2022. https://arxiv.org/abs/1809.03006.
Brassington, G. 2017. “Mean absolute error and root mean square error: Which is the better metric for assessing model performance?” In Proc., 19th EGU General Assembly Conf. Abstracts, 3574. Vienna, Austria: European Geosciences Union.
Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 2017. Classification and regression trees. Boca Raton, FL: Routledge.
Bu, J. Q., S. S. Law, and X. Q. Zhu. 2006. “Innovative bridge condition assessment from dynamic response of a passing vehicle.” J. Eng. Mech. 132 (12): 1372–1379. https://doi.org/10.1061/(ASCE)0733-9399(2006)132:12(1372.
Bush, S. J. W., T. F. P. Henning, A. Raith, and J. M. Ingham. 2017. “Development of a bridge deterioration model in a data-constrained environment.” J. Perform. Constr. Facil. 31 (5): 04017080. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001074.
Callow, D., J. Lee, M. Blumenstein, H. Guan, and Y. C. Loo. 2013. “Development of hybrid optimisation method for Artificial Intelligence based bridge deterioration model—Feasibility study.” Autom. Constr. 31 (May): 83–91. https://doi.org/10.1016/j.autcon.2012.11.016.
Chen, Z., Y. Wu, L. Li, and L. Sun. 2015. “Application of artificial intelligence for bridge deterioration model.” Sci. World. J. 2015 (Jan): 1–6. https://doi.org/10.1155/2015/743643.
Conceição, R. C., H. Medeiros, D. M. Godinho, M. O’Halloran, D. Rodriguez-Herrera, D. Flores-Tapia, and S. Pistorius. 2020. “Classification of breast tumor models with a prototype microwave imaging system.” Med. Phys. 47 (4): 1860–1870. https://doi.org/10.1002/mp.14064.
Deng, Y., A. Q. Li, D. M. Feng, X. Chen, and M. Zhang. 2020. “Service life prediction for steel wires in hangers of a newly built suspension bridge considering corrosion fatigue and traffic growth.” Struct. Control. Health. Monit. 27 (12): 2642. https://doi.org/10.1002/stc.2642.
Dhakal, S., L. Zhang, and X. Lv. 2021. “Understanding infrastructure resilience, social equity, and their interrelationships: Exploratory study using social media data in hurricane Michael.” Nat. Hazard. Rev. 22 (4): 04021045. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000512.
Ding, Q., Z. Li, S. Haeri, and L. Trajković. 2018. “Application of machine learning techniques to detecting anomalies in communication networks: Datasets and feature selection algorithms.” In Cyber Threat Intelligence, 47–70. Cham, Switzerland: Springer.
Dy, J. G., and C. E. Brodley. 2004. “Feature selection for unsupervised learning.” J. Mach. Learn. Res. 5 (Aug): 845–889. https://doi.org/10.5555/1005332.1016787.
El Aghoury, I. M., and K. Galal. 2014. “Corrosion-fatigue strain-life model for steel bridge girders under various weathering conditions.” J. Struct. Eng. 140 (6): 04014026. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000992.
Esteva, A., B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun. 2017. “Dermatologist-level classification of skin cancer with deep neural networks.” Nature 542 (7639): 115–118. https://doi.org/10.1038/nature21056.
Fancy, S. F. 2019. “Corrosion durability of a nano-particle enriched zinc-rich coating system for highway steel bridges.” FIU Electronic Theses and Dissertations. 4344. Accessed January 12, 2022. https://digitalcommons.fiu.edu/etd/4344.
Fancy, S. F., M. A. Sabbir, K. Lau, and D. Derord. 2018. “Three-coat & epoxy mastic bridge coating systems in adverse environments.” J. Prot. Coat. Linings 35 (5): 36–38.
FDOT (Florida DOT). 2021a. Florida bridge inventory, 2021 annual report. Tallahassee, FL: FDOT.
FDOT (Florida DOT). 2021b. Florida Department of Transportation bridge management system coding guide. Tallahassee, FL: FDOT.
FHWA (Federal Highway Administration). 1995. Recording and coding guide for the structure inventory and appraisal of the nation’s bridges, 119. Washington, DC: FHWA.
FHWA (Federal Highway Administration). 2022a. “National bridge inspection standards.” Accessed January 12, 2022. https://www.fhwa.dot.gov/bridge/nbis.cfm.
FHWA (Federal Highway Administration). 2022b. “NBI record format.” Accessed January 12, 2022. https://www.fhwa.dot.gov/bridge/nbi/format.cfm.
FHWA (Federal Highway Administration). 2022c. “Specification for the national bridge inventory bridge elements.” Accessed January 12, 2022. https://www.fhwa.dot.gov/bridge/nbi/131216_a1.pdf.
Galvin, R., C. Hanley, K. Ruane, J. J. Murphy, and V. Jaksic. 2020. “Environmental impact on corrosion rates of steel piles employed in marine environment.” Accessed January 12, 2022. https://sword.cit.ie/ceri/2020/10/1/.
Gao, Z., R. Y. Liang, and T. Xuan. 2019. “VIKOR method for ranking concrete bridge repair projects with target-based criteria.” Results Eng. 3 (Sep): 100018. https://doi.org/10.1016/j.rineng.2019.100018.
Gonçalves, P., M. Araújo, F. Benevenuto, and M. Cha. 2013. “Comparing and combining sentiment analysis methods.” In Proc., 1st ACM Conf. on Online Social Networks, 27–38. Boston: Association for Computing Machinery.
Gondia, A., A. Siam, W. El-Dakhakhni, and A. H. Nassar. 2020. “Machine learning algorithms for construction projects delay risk prediction.” J. Constr. Eng. Manage. 146 (1): 04019085. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001736.
Gong, C., and D. M. Frangopol. 2020. “Condition-based multiobjective maintenance decision making for highway bridges considering risk perceptions.” J. Struct. Eng. 146 (5): 04020051. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002570.
Gou, J., H. Ma, W. Ou, S. Zeng, Y. Rao, and H. Yang. 2019. “A generalized mean distance-based k-nearest neighbor classifier.” Expert Syst. Appl. 115 (Jan): 356–372. https://doi.org/10.1016/j.eswa.2018.08.021.
Graybeal, B. A., B. M. Phares, D. D. Rolander, M. Moore, and G. Washer. 2002. “Visual inspection of highway bridges.” J. Nondestr. Eval. 21 (3): 67–83. https://doi.org/10.1023/A:1022508121821.
Guo, Z., Y. Ma, L. Wang, and J. Zhang. 2019. “Modelling guidelines for corrosion-fatigue life prediction of concrete bridges: Considering corrosion pit as a notch or crack.” Eng. Fail. Anal. 105 (Nov): 883–895. https://doi.org/10.1016/j.engfailanal.2019.07.046.
Gupta, S., W. Zhang, and F. Wang. 2016. “Model accuracy and runtime tradeoff in distributed deep learning: A systematic study.” In Proc., 16th Int. Conf. on Data Mining, 171–180. Barcelona, Spain: IEEE.
Han, B., L. N. Qiao, J. L. Chen, X. D. Zhang, Y. X. Zhang, and Y. H. Zhao. 2021. “GeneticKNN: A weighted KNN approach supported by genetic algorithm for photometric redshift estimation of quasars.” Res. Astron. Astrophys. 21 (1): 017. https://doi.org/10.1088/1674-4527/21/1/17.
Handhal, A. M., A. M. Al-Abadi, H. E. Chafeet, and M. J. Ismail. 2020. “Prediction of total organic carbon at Rumaila oil field, southern Iraq using conventional well logs and machine learning algorithms.” Mar. Pet. Geol. 116 (Jul): 104347. https://doi.org/10.1016/j.marpetgeo.2020.104347.
Hirsch, J. A., G. F. Green, M. Peterson, D. A. Rodriguez, and P. Gordon-Larsen. 2016. “Neighborhood sociodemographics and change in built infrastructure.” J. Urban.: Int. Res. Placemaking Urban Sustainability 10 (2): 181–197. https://doi.org/10.1080/17549175.2016.1212914.
Inkoom, S., J. Sobanjo, A. Barbu, and X. Niu. 2019. “Pavement crack rating using machine learning frameworks: Partitioning, bootstrap forest, boosted trees, naïve Bayes, and k-nearest neighbors.” J. Transp. Eng. Part B: Pavements 145 (3): 04019031. https://doi.org/10.1061/JPEODX.0000126.
Jain, A. 2015. “Machine learning techniques for medical diagnosis: A review.” In Proc., Conf. on Science, Technology and Management. New Delhi, India: Univ. of Delhi.
Jebelli, H., B. Choi, and S. Lee. 2019. “Application of wearable biosensors to construction sites. I: Assessing workers’ stress.” J. Constr. Eng. Manage. 145 (12): 04019079. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001729.
Jiang, F., A. R. Smith, M. Kutia, G. Wang, H. Liu, and H. Sun. 2020. “A modified KNN method for mapping the Leaf Area Index in arid and semi-arid areas of China.” Remote Sens. 12 (11): 1884. https://doi.org/10.3390/rs12111884.
John, G. H. 1995. “Robust decision trees: Removing outliers from databases.” In Vol. 95 of Proc., 1st Int. Conf. on Knowledge Discovery and Data Mining, 174–179. Menlo Park, CA: AAAI Press.
Kalisch, M., M. Michalak, M. Sikora, L. Wróbel, and P. Przystałka. 2015. “Influence of outliers introduction on predictive models quality.” In Proc., 12th Int. Conf. BDAS, 79–93. Ustroń, Poland: Springer.
Kallias, A. N., B. Imam, and M. K. Chryssanthopoulos. 2017. “Performance profiles of metallic bridges subject to coating degradation and atmospheric corrosion.” Struct. Infrastruct. Eng. 13 (4): 440–453. https://doi.org/10.1080/15732479.2016.1164726.
Kanevski, M., R. Parkin, A. Pozdnukhov, V. Timonin, M. Maignan, V. Demyanov, and S. Canu. 2004. “Environmental data mining and modeling based on machine learning algorithms and geostatistics.” Environ. Modell. Software 19 (9): 845–855. https://doi.org/10.1016/j.envsoft.2003.03.004.
Kim, J. H. 2009. “Estimating classification error rate: Repeated cross–validation, repeated hold–out and bootstrap.” Comput. Stat. Data Anal. 53 (11): 3735–3745. https://doi.org/10.1016/j.csda.2009.04.009.
Kocev, D., S. Džeroski, M. D. White, G. R. Newell, and P. Griffioen. 2009. “Using single-and multi-target regression trees and ensembles to model a compound index of vegetation condition.” Ecol. Modell. 220 (8): 1159–1168. https://doi.org/10.1016/j.ecolmodel.2009.01.037.
Koch, G. H., M. P. Brongers, N. G. Thompson, Y. P. Virmani, and J. H. Payer. 2002. Corrosion cost and preventive strategies in the United States. FHWA-RD-01-156. Washington, DC: Transportation Research Board.
Kogler, R. 2015. Steel bridge design handbook: Corrosion protection of steel bridges. Washington, DC: FHWA.
Kreislova, K., and H. Geiplova. 2012. “Evaluation of corrosion protection of steel bridges.” Procedia Eng. 40 (Jan): 229–234. https://doi.org/10.1016/j.proeng.2012.07.085.
Kuhn, M., and K. Johnson. 2013. Applied predictive modeling, 26. New York: Springer.
Lau, K., S. F. Fancy, and S. A. Sabbir. 2018. Corrosion evaluation of novel coatings for steel components of highway bridges: Phase II. FDOT Project BDV29-977-22. Tallahassee, FL: FDOT.
Liang, Z., and A. K. N. Parlikad. 2016. “Deterioration and maintenance models for concrete decks with chloride-induced deterioration.” In Proc., Int. Research Conf. on Systems Engineering and Management Science. Cambridge, UK: Univ. of Cambridge.
Liu, H., and Y. Zhang. 2020. “Bridge condition rating data modeling using deep learning algorithm.” Struct. Infrastruct. Eng. 16 (10): 1447–1460. https://doi.org/10.1080/15732479.2020.1712610.
Manafpour, A., I. Guler, A. Radlińska, F. Rajabipour, and G. Warn. 2018. “Stochastic analysis and time-based modeling of concrete bridge deck deterioration.” J. Bridge Eng. 23 (9): 04018066. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001285.
Mangal, A., and E. A. Holm. 2018. “A comparative study of feature selection methods for stress hotspot classification in materials.” Integr. Mater. Manuf. Innovation 7 (3): 87–95. https://doi.org/10.1007/s40192-018-0109-8.
Mangalathu, S., and J. S. Jeon. 2019. “Machine learning-based failure mode recognition of circular reinforced concrete bridge columns: Comparative study.” J. Struct. Eng. 145 (10): 04019104. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002402.
Melhem, H. G., and Y. Cheng. 2003. “Prediction of remaining service life of bridge decks using machine learning.” J. Comput. Civ. Eng. 17 (1): 1–9. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:1(1).
Miner, L., P. Bolding, J. Hilbe, M. Goldstein, T. Hill, R. Nisbet, N. Walton, and G. Miner. 2015. Practical predictive analytics and decisioning systems for medicine: Informatics accuracy and cost-effectiveness for healthcare administration and delivery including medical research. New York: Academic Press.
Morcous, G. 2002. “Comparing the use of artificial neural networks and case-based reasoning in modeling bridge deterioration.” In Proc., Annual Conf. of the Canadian Society of Civil Engineering. Montréal: Canadian Society for Civil Engineering.
Munger, C. G., and L. D. Vincent. 2014. Corrosion prevention by protective coatings. 3rd ed. Houston: NACE International.
Myers, J. J., G. Washer, and W. Zheng. 2010. Structural steel coatings for corrosion mitigation. MODOT Project NUTC R233 & R238. Jefferson City, MO: Missouri DOT.
NACE (National Association of Corrosion Engineers). 2022. “Highways and bridges.” Accessed January 12, 2022. https://www.nace.org/resources/what-is-corrosion/corrosion-reference-library/highways-bridges.
Nicolai, R. P., R. Dekker, and J. M. van Noortwijk. 2007. “A comparison of models for measurable deterioration: An application to coatings on steel structures.” Reliab. Eng. Syst. Saf. 92 (12): 1635–1650. https://doi.org/10.1016/j.ress.2006.09.021.
Omar, T., and M. L. Nehdi. 2018. “Condition assessment of reinforced concrete bridges: Current practice and research challenges.” Infrastructures 3 (3): 36. https://doi.org/10.3390/infrastructures3030036.
Peng, Y., and C. Unluer. 2022. “Analyzing the mechanical performance of fly ash-based geopolymer concrete with different machine learning techniques.” Constr. Build. Mater. 316 (Jan): 125785. https://doi.org/10.1016/j.conbuildmat.2021.125785.
Permeh, S., K. Lau, M. E. Boan, and M. Duncan. 2021a. “Electrochemical characteristics of antifouling coated steel structure submerged in Florida natural waters to mitigate micro-and macrofouling.” Constr. Build. Mater. 274 (Mar): 122087. https://doi.org/10.1016/j.conbuildmat.2020.122087.
Permeh, S., K. Lau, M. E. Boan, and M. Duncan. 2021b. “Influence of macro-and microfouling on corrosion of steel bridge piles submerged in natural waters.” J. Mater. Civ. 33 (6): 04021105. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003687.
Priyam, A., G. R. Abhijeeta, A. Rathee, and S. Srivastava. 2013. “Comparative analysis of decision tree classification algorithms.” Int. J. Curr. Eng. Technol. 3 (2): 334–337.
Radhika, K., and D. M. Latha. 2019. “Machine learning model for automation of soil texture classification.” Indian J. Agric. Res. 53 (1): 78–82. https://doi.org/10.18805/IJARe.A-5053.
Ravichandran, S., and S. C. Nair. 2016. “Overcoming challenges in coating application using simulators.” In Proc., Int. Corrosion Conf. and Expo September. New Delhi, India: NACE International Gateway India Section.
Rokach, L., and O. Z. Maimon. 2014. Data mining with decision trees: Theory and applications. Singapore: World scientific.
Rossow, M. 2022. “Inspection of bridge decks (BIRM).” Accessed January 12, 2022. https://www.cedengineering.com/userfiles/Inspection%20of%20Bridge%20Decks.pdf.
Santos, M. S., J. P. Soares, P. H. Abreu, H. Araujo, and J. Santos. 2018. “Cross-validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches [Research Frontier].” IEEE Comput. Intell. Mag. 13 (4): 59–76. https://doi.org/10.1109/MCI.2018.2866730.
Scikit-learn. 2022. “MinMaxScaler.” Accessed January 12, 2022. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html?highlight=minmaxscaler#sklearn.preprocessing.MinMaxScaler.
Shen, H. K., P. H. Chen, and L. M. Chang. 2013. “Automated steel bridge coating rust defect recognition method based on color and texture feature.” Autom. Constr. 31 (May): 338–356. https://doi.org/10.1016/j.autcon.2012.11.003.
Shen, L., M. Soliman, S. Ahmed, and C. Waite. 2019. “Life-cycle cost analysis of reinforced concrete bridge decks with conventional and corrosion resistant reinforcement.” In Vol. 271 of Proc., MATEC Web of Conf., 01009. Les Ulis, France: EDP Sciences. https://doi.org/10.1051/matecconf/201927101009.
Sohanghpurwala, A. A. 2005. “Condition and performance of epoxy-coated rebars in bridge decks of the state of Pennsylvania and New York.” In Proc., Structures Congress 2005, 1–9. Reston, VA: ASCE. https://doi.org/10.1061/40753(171)18.
Solorio-Fernández, S., J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad. 2020. “A review of unsupervised feature selection methods.” Artif. Intell. Rev. 53 (2): 907–948. https://doi.org/10.1007/s10462-019-09682-y.
Steelman, J., and P. M. Shakya. 2017. Condition factor calibration for load and resistance factor rating of steel girder bridges. Lincoln, NE: Nebraska DOT.
Tator, K. B., and R. Lanterman. 2016. “Coating deterioration—A mechanistic overview.” In Proc., NACE Int. Corrosion Conf. Series, OnePetro, 225–236. Houston: National Association of Corrosion Engineers.
Tiong, U. H., and G. Clark. 2010. “The structural environment as a factor affecting coating failure in aircraft joints.” Procedia Eng. 2 (1): 1393–1401. https://doi.org/10.1016/j.proeng.2010.03.151.
Tixier, A. J. P., M. R. Hallowell, B. Rajagopalan, and D. Bowman. 2017. “Construction safety clash detection: Identifying safety incompatibilities among fundamental attributes using data mining.” Autom. Constr. 74 (Feb): 39–54. https://doi.org/10.1016/j.autcon.2016.11.001.
Tung, P. C., Y. R. Hwang, and M. C. Wu. 2002. “The development of a mobile manipulator imaging system for bridge crack inspection.” Autom. Constr. 11 (6): 717–729. https://doi.org/10.1016/S0926-5805(02)00012-2.
Usukura, M., T. Yamaguchi, Y. Suzuki, and Y. Mitsugi. 2013. “Strength evaluation for a corroded damaged steel girder end considering its collapse mechanism.” In Proc., 13th East Asia-Pacific Conf. on Structural Engineering and Construction (EASEC-13). Sapporo, Hokkaido, Japan: Hokkaido Univ. Collection of Scholarly and Academic Papers (HUSCAP).
Wan, H., D. Song, X. Li, D. Zhang, J. Gao, and C. Du. 2017. “Failure mechanisms of the coating/metal interface in waterborne coatings: The effect of bonding.” Mater. Multidiscip. Digital Publ. Inst. 10 (4): 397. https://doi.org/10.3390/ma10040397.
Witten, I. H., E. Frank, M. A. Hall, and C. Pal. 2016. Data mining: Practical machine learning tools and techniques (Morgan Kaufmann series in data management systems). Cambridge, MA: Elsevier.
Worthey, H., J. J. Yang, and S. S. Kim. 2021. “Tree-based ensemble methods: Predicting asphalt mixture dynamic modulus for flexible pavement design.” KSCE J. Civ. Eng. 25 (11): 4231–4239. https://doi.org/10.1007/s12205-021-2306-9.
Yan, C., J. Liang, M. Zhao, X. Zhang, T. Zhang, and H. Li. 2019. “A novel hybrid feature selection strategy in quantitative analysis of laser induced breakdown spectroscopy.” Anal. Chim. Acta 1080 (Nov): 35–42. https://doi.org/10.1016/j.aca.2019.07.012.
Yang, D. H., T. H. Yi, and H. N. Li. 2017. “Coupled fatigue-corrosion failure analysis and performance assessment of RC bridge deck slabs.” J. Bridge Eng. 22 (10): 04017077. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001108.
Zhang, C., and Y. Ma. 2012. Ensemble machine learning: Methods and applications. New York: Springer.
Zhang, L., X. Lv, K. Lau, S. Viswanathan, M. Li, and P. Gosain. 2021. Assessment of structural steel coating applications. Final Rep. for BE935. Tallahassee, FL: Florida DOT.
Zhang, X. D. 2020. A matrix algebra approach to artificial intelligence. Singapore: Springer.
Zheng, A., and A. Casari. 2018. Feature engineering for machine learning. Sebastopol, CA: O’Reilly Media.
Zmetra, K., K. McMullen, A. Zaghi, and K. Wille. 2017. “Experimental study of UHPC repair for corrosion damaged steel girder ends.” J. Bridge Eng. 22 (8): 04017037. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001067.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 37 • Issue 2 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 2, 2022
Published online: Feb 9, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 9, 2023
Accepted: Sep 26, 2023
ASCE Technical Topics:
- Analysis (by type)
- Architectural engineering
- Bridge engineering
- Bridge failures
- Bridge management
- Bridges
- Bridges (by material)
- Building management
- Coating
- Data collection
- Disaster risk management
- Disasters and hazards
- Engineering fundamentals
- Failure analysis
- Failures (by type)
- Maintenance and operation
- Man-made disasters
- Material failures
- Materials characterization
- Materials engineering
- Materials processing
- Methodology (by type)
- Research methods (by type)
- Steel bridges
- Structural engineering
- Wood bridges
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Md. Ashiqur Rahman, Lu Zhang, Kingsley Lau, Xuan Lv, A Multilayer Perceptron-Based Neural Network Model for Predicting Steel Bridge Coating Conditions by Integrating Bridge and Environmental Features, Computing in Civil Engineering 2023, 10.1061/9780784485248.125, (1047-1054), (2024).
- Md. Ashiqur Rahman, Lu Zhang, Kingsley Lau, Xuan Lv, Leveraging Bridge and Environmental Features to Analyze Coating Conditions of Steel Bridges in Florida Using Neural Network Models, Journal of Performance of Constructed Facilities, 10.1061/JPCFEV.CFENG-4502, 37, 6, (2023).