Leveraging Bridge and Environmental Features to Analyze Coating Conditions of Steel Bridges in Florida Using Neural Network Models
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 6
Abstract
The safety, integrity, and longevity of steel bridge elements are affected by various environmental factors, such as moisture, atmospheric pollutants, and temperature. Protective coatings of steel bridge elements are especially sensitive to the service environments (e.g., atmospheric environments, water environments) of bridges, as many environmental stressors may accelerate premature failures of coatings. By leveraging the data on both bridge-related features (e.g., bridge age, type of service under bridge) and environmental features (e.g., chloride, moisture, atmospheric pollutant, temperature), this study focuses on bringing a data-driven understanding of steel bridge coating deterioration patterns and how the service environments of bridges may impact such patterns. Steel bridge coating performance prediction (SBCPP) models were built based on multilayer perceptron (MLP)–based artificial neural network (ANN) algorithm to predict and assess the coating conditions of girder elements of steel bridges in Florida. The results show that the SBCPP model with the best performance can precisely predict steel bridge coating conditions with a mean absolute error (MAE) of 0.0807. As compared to the models that do not account for environmental features, the performance of the proposed SBCPP models was significantly improved. Shapley additive explanations (SHAP) analysis was further conducted to interpret and analyze the influences of input features on the performance of the SBCPP model. The study offers an effective decision-making tool that has the potential to benefit state transportation agencies by allowing for easier and more efficient analysis or prediction of steel bridge coating performance.
Practical Applications
This study proposes data-driven models that analyze and predict the coating conditions of steel bridge elements based on the data of bridge-related features (e.g., bridge age, average daily traffic, type of service under bridge) and environmental features (e.g., chloride, moisture, atmospheric pollutant, temperature), thus providing knowledge and insights on corrosion-induced coating degradation for steel bridges in Florida. The proposed models serve as the foundations for an automatic coating performance assessment and prediction tool that can help state transportation agencies and other bridge owners easily evaluate the coating conditions of their steel bridges and identify those bridges that are in critical maintenance needs. Inspection of coating conditions traditionally requires significant manual efforts, which are expensive, time-consuming, and cause safety concerns. An automatic tool based on the proposed models can quickly and easily analyze the element-based coating conditions while considering the impacts of the service environments. By using the tool, a bridge owner can quickly identify those bridges that require more attention or funding support and decide which bridges should be given priority in terms of inspection, maintenance, and/or repair. Hence, acting as the foundation of such a decision support tool, the proposed work has the potential to reduce field-level coating inspection costs, efforts, and risks and enhance the efficiency of maintenance and repair-related decision-making.
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Data Availability Statement
Some or all data, models, or codes 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 author(s) and not necessarily those of the FDOT or the US Department of Transportation.
References
Ahmed, M., M. A. Rahman, and A. Rahman. 2021. “Assessment and prediction of mechanical strength of jute fibre reinforced recycled aggregate concrete.” Int. J. Sustainable Mater. Struct. Syst. 5 (3): 225–238. https://doi.org/10.1504/IJSMSS.2021.117750.
Akkaya, B., and N. Çolakoğlu. 2019. “Comparison of multi-class classification algorithms on early diagnosis of heart diseases.” In Proc., y-BIS 2019: Recent Advances in Data Science and Business Analytics, 162–170. Istanbul, Turkey: International Society for Business and Industrial Statistics.
Ali, R., and Y. J. Cha. 2019. “Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer.” Constr. Build Mater. 226 (Nov): 376–387. https://doi.org/10.1016/j.conbuildmat.2019.07.293.
Al-Smadi, S., and H. Al-Bdour. 2023. “Machine learning-aided time and cost overrun prediction in construction projects: Application of artificial neural network.” Asian J. Civ. Eng. 2023 (Apr): 1–11. https://doi.org/10.1007/s42107-023-00665-7.
Althaqafi, E., and E. Chou. 2022. “Developing bridge deterioration models using an artificial neural network.” Infrastructures 7 (8): 101. https://doi.org/10.3390/infrastructures7080101.
Andrade, G., R. Ferreira, and G. Teodoro. 2023. “Spatial-aware data partition for distributed memory parallelization of ANN search in multimedia retrieval.” Parallel Comput. 115 (Feb): 102992. https://doi.org/10.1016/j.parco.2022.102992.
Apriyono, A., and P. B. Santoso. 2022. “Landslide susceptible areas identification using IDW and ordinary kriging interpolation techniques from hard soil depth at middle western Central Java, Indonesia.” Nat. Hazards 110 (2): 1405–1416. https://doi.org/10.1007/s11069-021-04982-5.
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.
Bartier, P. M., and C. P. Keller. 1996. “Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW).” Comput. Geosci. 22 (7): 795–799. https://doi.org/10.1016/0098-3004(96)00021-0.
Bayliss, D. A., and D. H. Deacon. 2002. Steelwork corrosion control, 2nd ed. London: CRC Press.
Benavides, C., L. Marıa, J. Alexander, T. Arias, D. Burgos, and A. Martens. 2022. “Measuring digital transformation in higher education institutions–content validity instrument.” Appl. Comput. Inform. https://doi.org/10.1108/ACI-03-2022-006910.1108/ACI-03-2022-0069.
Ben Seghier, M. E. A., J. A. Corriea, J. Jafari-Asl, A. Malekjafarian, V. Plevris, and N. T. Trung. 2021. “On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm.” Neural Comput. Appl. 33 (23): 15969–15985. https://doi.org/10.1007/s00521-021-06199-w.
Ben Seghier, M. E. A., V. Plevris, and A. Malekjafarian. 2023. “Development of hybrid adaptive neural fuzzy inference system-based evolutionary algorithms for flexural capacity prediction in corroded steel reinforced concrete beam.” Arab J. Sci. Eng. 2023 (Mar): 1–17. https://doi.org/10.1007/s13369-023-07708-w.
Brownlee, J. 2023. “When to use MLP, CNN, and RNN neural networks.” Accessed February 5, 2023. https://machinelearningmastery.com/when-to-use-mlp-cnn-and-rnn-neural-networks/.
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.
Chakkingal, A., P. Janssens, J. Poissonnier, A. J. Barrios, M. Virginie, A. Y. Khodakov, and J. W. Thybaut. 2022. “Machine learning based interpretation of microkinetic data: A Fischer–Tropsch synthesis case study.” React. Chem. Eng. 7 (1): 101–110. https://doi.org/10.1039/D1RE00351H.
Chen, X., G. Wang, F. Gao, Y. Wang, and C. He. 2015. “Effects of sulphate-reducing bacteria on crevice corrosion in X70 pipeline steel under disbonded coatings.” Corros. Sci. 101 (Dec): 1–11. https://doi.org/10.1016/j.corsci.2015.06.015.
Dang, H. V., M. Raza, T. V. Nguyen, T. Bui-Tien, and H. X. Nguyen. 2021. “Deep learning-based detection of structural damage using time-series data.” Struct. Infrastruct. Eng. 17 (11): 1474–1493. https://doi.org/10.1080/15732479.2020.1815225.
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. Hazards Rev. 22 (4): 04021045. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000512.
Ejenstam, L., M. Tuominen, J. Haapanen, J. M. Mäkelä, J. Pan, A. Swerin, and P. M. Claesson. 2015. “Long-term corrosion protection by a thin nano-composite coating.” Appl. Surf. Sci. 357 (Dec): 2333–2342. https://doi.org/10.1016/j.apsusc.2015.09.238.
Fancy, S. F., K. Lau, M. A. Sabbir, and D. Derord. 2020. “Influence of nano-particles on water intrusion of a nanoparticle enriched zinc-rich coating by EIS analysis.” In Proc., Corrosion. Nashville, TN: National Association of Corrosion Engineers.
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.
FAWN (Florida Automated Weather Network). 2023. “Data access.” Accessed February 5, 2023. https://fawn.ifas.ufl.edu/data/.
FDOT (Florida DOT). 2021. Florida department of transportation bridge management system coding guide. Tallahassee, FL: Florida DOT.
FHWA (Federal Highway Administration). 2023a. “NBI record format.” Accessed February 5, 2023. https://www.fhwa.dot.gov/bridge/nbi/format.cfm.
FHWA (Federal Highway Administration). 2023b. “Specification for the national bridge inventory bridge elements.” Accessed February 05, 2023. https://www.fhwa.dot.gov/bridge/nbi/131216_a1.pdf.
Franchini, G., V. Ruggiero, F. Porta, and L. Zanni. 2023. “Neural architecture search via standard machine learning methodologies.” Math. Eng. 5 (1): 1–21. https://doi.org/10.3934/mine.2023012.
Géron, A. 2019. Hands-on machine learning with Scikit-learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. Sebastopol, CA: O’Reilly.
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. New York: IEEE.
Gyaneshwar, A., A. Mishra, U. Chadha, P. D. Raj Vincent, V. Rajinikanth, G. G. Pattukandan Ganapathy, and K. Srinivasan. 2023. “A contemporary review on deep learning models for drought prediction.” Sustainability 15 (7): 6160. https://doi.org/10.3390/su15076160.
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/10.1080/17549175.2016.12129.14.
Huang, Y.-H. 2010. “Artificial neural network model of bridge deterioration.” J. Perform. Constr. Facil. 24 (6): 597–602. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000124.
Hussain, M. M., S. H. Bari, I. Mahmud, and M. I. H. Siddiquee. 2021. “Application of different artificial neural network for streamflow forecasting.” In Advances in streamflow forecasting, 149–170. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/B978-0-12-820673-7.00006-8.
Jacobson, S., D. Reichman, J. Bjornstad, L. M. Collins, and J. M. Malof. 2019. “Reliable training of convolutional neural networks for GPR-based buried threat detection using the Adam optimizer and batch normalization.” In Proc., Detection and Sensing of Mines, Explosive Objects, and Obscured Targets, 29–38. Baltimore: International Society for Optics and Photonics. https://doi.org/10.1117/12.2519798.
Jafari-Asl, J., M. E. A. Ben Seghier, S. Ohadi, Y. Dong, and V. Plevris. 2021. “A comparative study on the efficiency of reliability methods for the probabilistic analysis of local scour at a bridge pier in clay-sand-mixed sediments.” Modelling 2 (1): 63–77. https://doi.org/10.3390/modelling2010004.
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.
Jeng, C.-H., and Y. L. Mo. 2004. “Quick seismic response estimation of prestressed concrete bridges using artificial neural networks.” J. Comput. Civ. Eng. 18 (4): 360–372. https://doi.org/10.1061/(ASCE)0887-3801(2004)18:4(360).
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.
Karanci, E., and R. Betti. 2018. “Modeling corrosion in suspension bridge main cables. I: Annual corrosion rate.” J. Bridge Eng. 23 (6): 04018025. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001233.
Kim, B., N. Yuvaraj, H. W. Park, K. S. Preethaa, R. A. Pandian, and D. E. Lee. 2021. “Investigation of steel frame damage based on computer vision and deep learning.” Autom. Constr. 132 (Dec): 103941. https://doi.org/10.1016/j.autcon.2021.103941.
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. Houston: National Association of Corrosion Engineers.
Lau, K., S. F. Fancy, and S. A. Sabbir. 2018. Corrosion evaluation of novel coatings for steel components of highway bridges: Phase II. Tallahassee, FL: Florida DOT.
Lau, K., and S. Permeh. 2022. “Assessment of durability and zinc activity of zinc-rich primer coatings by electrochemical noise technique.” Prog. Org. Coat. 167 (Jun): 106840. https://doi.org/10.1016/j.porgcoat.2022.106840.
Legendre, P. 2005. “Species associations: The Kendall coefficient of concordance revisited.” J. Agric. Biol. Environ. Stat. 10 (2): 226–245. https://doi.org/10.1198/108571105X46642.
Li, Z., and R. Burgueno. 2019. “Structural information integration for predicting damages in bridges.” J. Ind. Inf. Integr. 15 (Sep): 174–182. https://doi.org/10.1016/j.jii.2018.11.004.
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.
Liu, S., S. Li, and H. Cheng. 2021. “Towards an end-to-end visual-to-raw-audio generation with GAN.” IEEE Trans. Circuits Syst. Video Technol. 32 (3): 1299–1312. https://doi.org/10.1109/TCSVT.2021.3079897.
Liu, Y., D. Su, Q. Duan, and Z. Cao. 2022. “Recommendations for refined preventive maintenance management of concrete bridges in China based on environmental risk zoning.” Transp. Res. Rec.: J. Trans. Res. Board 2676 (5): 460–479. https://doi.org/10.1177/03611981211067978.
Lundberg, S. M., and S. I. Lee. 2017. “A unified approach to interpreting model predictions.” In Vol. 30 of Proc., Advances in Neural Information Processing Systems, 4765–4774. Long Beach, CA: Advances in Neural Information Processing Systems.
Luo, S., J. Yao, J. Hu, Y. Wang, and S. Chen. 2022. “Using deep learning-based defect detection and 3D quantitative assessment for steel deck pavement maintenance.” IEEE Trans. Intell. Transp. Syst. 2022 (May): 1–10. https://doi.org/10.1109/TITS.2022.3169164.
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.
McCulloch, W. S., and W. Pitts. 1943. “A logical calculus of the ideas immanent in nervous activity.” Bull. Math. Biophys. 5 (4): 115–133. https://doi.org/10.1007/BF02478259.
McKnight, P. E., and J. Najab. 2010. “Mann-Whitney U test.” Corsini Encycl. Psychol. 2010 (Jan): 1. https://doi.org/10.1002/9780470479216.corpsy0524.
McMullen, K. F., and A. E. Zaghi. 2020. “Experimental evaluation of full-scale corroded steel plate girders repaired with UHPC.” J. Bridge Eng. 25 (4): 04020011. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001535.
Morchid, M. 2018. “Parsimonious memory unit for recurrent neural networks with application to natural language processing.” Neurocomputing 314 (Nov): 48–64. https://doi.org/10.1016/j.neucom.2018.05.081.
NADP (National Atmospheric Deposition Program). 2023. “National trends network.” Accessed February 5, 2023. https://nadp.slh.wisc.edu/networks/national-trends-network/.
Nguyen, M. N., X. Wang, and R. H. Leicester. 2013. “An assessment of climate change effects on atmospheric corrosion rates of steel structures.” Corros. Eng. Sci. Technol. 48 (5): 359–369. https://doi.org/10.1179/1743278213Y.0000000087.
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.
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, and M. Duncan. 2021b. “Effect of crevice morphology on SRB activity and steel corrosion under marine foulers.” Bioelectrochemistry 142 (Dec): 107922. https://doi.org/10.1016/j.bioelechem.2021.107922.
Persson, D., D. Thierry, and O. Karlsson. 2017. “Corrosion and corrosion products of hot dipped galvanized steel during long term atmospheric exposure at different sites world-wide.” Corros. Sci. 126 (Sep): 152–165. https://doi.org/10.1016/j.corsci.2017.06.025.
Ragmani, A., A. Elomri, N. Abghour, K. Moussaid, M. Rida, and E. Badidi. 2020. “Adaptive fault-tolerant model for improving cloud computing performance using artificial neural network.” Procedia Comput. Sci. 170 (Jan): 929–934. https://doi.org/10.1016/j.procs.2020.03.106.
Rahman, M. A., L. Zhang, K. Lau, X. Lv, and P. Gosain. 2022. “Ontology-based semantic modeling for steel bridge coating systems.” In Proc., Construction Research Congress, 1106–1115. Reston, VA: ASCE.
Rahman, M. A., L. Zhang, X. Lv, and K. Lau. 2023. “Analyzing coating conditions of steel bridges at Florida: A data-driven approach.” J. Perform. Constr. Facil. 37(2): 04023010. https://doi.org/10.1061/JPCFEV.CFENG-4111.
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 India.
Ren, X., M. M. Sherif, Y. Wei, Y. Lyu, Y. Sun, and O. E. Ozbulut. 2022. “Effect of corrosion on the tensile and fatigue performance of CFRP strand sheet/steel double strap joints.” Eng. Struct. 260 (Jun): 114240. https://doi.org/10.1016/j.engstruct.2022.114240.
Roberge, P. R. 2010. “Impact of climate change on corrosion risks.” Corros. Eng. Sci. Technol. 45 (1): 27–33. https://doi.org/10.1179/174327809X442621.
Seghier, M. E. A. B., V. Plevris, and G. Solorzano. 2022. “Random forest-based algorithms for accurate evaluation of ultimate bending capacity of steel tubes.” Structures 44 (Oct): 261–273. https://doi.org/10.1016/j.istruc.2022.08.007.
Senderowski, C., W. Rejmer, and P. Bilko. 2022. “Effect of low chloride and sulfate concentrations on corrosion behavior of aluminum and zinc arc thermal sprayed coatings.” Coatings 12 (5): 653. https://doi.org/10.3390/coatings12050653.
Shifler, D. A. 2018. “Hot corrosion: A modification of reactants causing degradation.” Mater. High Temp. 35 (1–3): 225–235. https://doi.org/10.1080/09603409.2017.1404692.
Sousa, M. L., et al. 2020. Expected implications of climate change on the corrosion of structures. Luxembourg: Publications Office of the European Union.
Suh, G., and Y. J. Cha. 2018. “Deep faster R-CNN-based automated detection and localization of multiple types of damage.” In Proc., Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 197–204. Denver: SPIE. https://doi.org/10.1117/12.2295954.
Toubia, E. A., and S. Emami. 2016. “Experimental evaluation of structural steel coating systems.” J. Mater. Civ. Eng. 28 (12): 04016147. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001670.
USEPA. 2023. “Outdoor air quality data.” Accessed February 5, 2023. https://www.epa.gov/outdoor-air-quality-data.
Wang, D., Y. Zhang, Y. Pan, B. Peng, H. Liu, and R. Ma. 2020. “An automated inspection method for the steel box girder bottom of long-span bridges based on deep learning.” IEEE Access 8 (May): 94010–94023. https://doi.org/10.1109/ACCESS.2020.2994275.
Wang, X., C. Miao, and R. Chen. 2022. “Time-variant fatigue reliability assessment of rib-to-deck welded joints using ANN-based methods.” Structures 42 (Aug): 244–254. https://doi.org/10.1016/j.istruc.2022.06.020.
Witten, I. H., E. Frank, M. A. Hall, and C. Pal. 2016. “Data mining: Practical machine learning tools and techniques.” In Morgan Kaufmann series in data management systems. Cambridge, MA: Elsevier.
Xu, J. G., W. Hong, J. Zhang, S. T. Hou, and G. Wu. 2022. “Seismic performance assessment of corroded RC columns based on data-driven machine-learning approach.” Eng. Struct. 255 (Mar): 113936. https://doi.org/10.1016/j.engstruct.2022.113936.
Yao, Y., P. Kodumuri, and S. K. Lee. 2011. Performance evaluation of one-coat systems for new steel bridges. Washington, DC: Federal Highway Administration.
Yu, L., R. Zhou, R. Chen, and K. K. Lai. 2022. “Missing data preprocessing in credit classification: One-hot encoding or imputation?” Emerging Mark. Finance Trade 58 (2): 472–482. https://doi.org/10.1080/1540496X.2020.1825935.
Zaid, B., D. Saidi, A. Benzaid, and S. Hadji. 2008. “Effects of pH and chloride concentration on pitting corrosion of AA6061 aluminum alloy.” Corros. Sci. 50 (7): 1841–1847. https://doi.org/10.1016/j.corsci.2008.03.006.
Zhang, Y.-C., T.-H. Yi, S. Lin, H.-N. Li, and S. Lv. 2022. “Automatic corrosive environment detection of RC bridge decks from ground-penetrating radar data based on deep learning.” J. Perform. Constr. Facil. 36 (2): 04022011. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001712.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 37 • Issue 6 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 20, 2023
Accepted: Jun 26, 2023
Published online: Oct 6, 2023
Published in print: Dec 1, 2023
Discussion open until: Mar 6, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Bridge engineering
- Bridge failures
- Bridges
- Bridges (by material)
- Bridges (by type)
- Coating
- Computer programming
- Computing in civil engineering
- Disaster risk management
- Disasters and hazards
- Engineering fundamentals
- Environmental engineering
- Failures (by type)
- Girder bridges
- Hydrologic engineering
- Hydrology
- Man-made disasters
- Materials engineering
- Materials processing
- Measurement (by type)
- Moisture
- Neural networks
- Pollutants
- Steel bridges
- Structural engineering
- Temperature effects
- Temperature measurement
- Water and water resources
- Wood bridges
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