An application of convolutional neural network for deterioration modeling of highway bridge components in the United States

References

• Agrawal, A. K., Kawaguchi, A., & Chen, Z. (2010). Deterioration rates of typical bridge elements in New York. Journal of Bridge Engineering, 15(4), 419–429. doi:10.1061/(ASCE)BE.1943-5592.0000123
   Web of Science ®Google Scholar
• Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer-Verlag.
   Google Scholar
• Bocchini, P., Saydam, D., & Frangopol, D. M. (2013). Efficient, accurate, and simple Markov chain model for the life-cycle analysis of bridge groups. Structural Safety, 40, 51–64. doi:10.1016/j.strusafe.2012.09.004
   Web of Science ®Google Scholar
• Bu, G. P., Lee, J. H., Guan, H., Loo, Y. C., & Blumenstein, M. (2015). Prediction of long-term bridge performance: Integrated deterioration approach with case studies. Journal of Performance of Constructed Facilities, 29(3), 04014089. doi:10.1061/(ASCE)CF.1943-5509.0000591
   Web of Science ®Google Scholar
• Cesare, M. A., Santamarina, C., Turkstra, C., & Vanmarcke, E. H. (1992). Modeling bridge deterioration with Markov chains. Journal of Transportation Engineering, 118(6), 820–833. doi:10.1061/(ASCE)0733-947X(1992)118:6(820)
   Web of Science ®Google Scholar
• Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). Deep learning‐based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. doi:10.1111/mice.12263
   Web of Science ®Google Scholar
• Chang, M., Maguire, M., & Sun, Y. (2017). Framework for mitigating human bias in selection of explanatory variables for bridge deterioration modeling. Journal of Infrastructure Systems, 23(3), 04017002. doi:10.1061/(ASCE)IS.1943-555X.0000352
   Web of Science ®Google Scholar
• Cheng, C., Shang, Z., & Shen, Z. (2019). CNN-based deep architecture for reinforced concrete delamination segmentation through thermography. In ASCE International Conference on Computing in Civil Engineering 2019 (pp. 50–57). Atlanta, GA: ASCE. doi:10.1061/9780784482445.007.
   Google Scholar
• Dinh, K., Gucunski, N., & Duong, T. H. (2018). An algorithm for automatic localization and detection of Rebars from GPR data of concrete bridge decks. Automation in Construction, 89, 292–298. doi:10.1016/j.autcon.2018.02.017
   Web of Science ®Google Scholar
• Dorafshan, S., & Azari, H. (2020). Evaluation of bridge decks with overlays using impact echo, a deep learning approach. Automation in Construction, 113, 103133. doi:10.1016/j.autcon.2020.103133
   Web of Science ®Google Scholar
• FHWA. (1995). Recording and Coding Guide for the Structure Inventory and Appraisal of the Nations Bridges. U.S. Department of Transportation, Federal Highway Adminsitration. Retrieved from https://www.fhwa.dot.gov/bridge/mtguide.pdf.
   Google Scholar
• FHWA. (2017). National performance management measures; assessing pavement condition for the national highway performance program and bridge condition for the national highway performance program. Federal Register, 5886–5970. Retrieved from https://www.govinfo.gov/content/pkg/FR-2017-01-18/pdf/2017-00550.pdf.
   Google Scholar
• FHWA. (2020). InfoBridge. Retrieved from https://infobridge.fhwa.dot.gov/.
   Google Scholar
• Frangopol, D. M., & Soliman, M. (2016). Life-cycle of structural systems: recent achievements and future directions. Structure and Infrastructure Engineering, 12(1), 1–20. doi:10.1080/15732479.2014.999794
   Web of Science ®Google Scholar
• Frangopol, D. M., Kallen, M. J., & Noortwijk, J. M. (2004). Probabilistic models for life‐cycle performance of deteriorating structures: review and future directions. Progress in Structural Engineering and Materials, 6(4), 197–212. doi:10.1002/pse.180
   Google Scholar
• Gao, Y., & Mosalam, K. M. (2018). Deep transfer learning for image‐based structural damage recognition. Computer‐Aided Civil and Infrastructure Engineering, 32(9), 748–768. doi:10.1111/mice.12363.
   Google Scholar
• Ghonima, O., Anderson, J. C., Schumacher, T., & Unnikrishnan, A. (2020). Performance of US concrete highway bridge decks characterized by random parameters binary logistic regression. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6(1), 04019025. doi:10.1061/AJRUA6.0001031
   Google Scholar
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press.
   Google Scholar
• Goyal, R., Whelan, M. J., & Cavalline, T. L. (2017). Characterising the effect of external factors on deterioration rates of bridge components using multivariate proportional hazards regression. Structure and Infrastructure Engineering, 13(7), 894–905. doi:10.1080/15732479.2016.1217888
   Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). New York, NY: Springer. doi:10.1007/978-0-387-84858-7.
   Google Scholar
• Hatami, A., & Morcous, G. (2012). Deterioration models for life-cycle cost analysis of bridge decks in Nebraska. Transportation Research Record: Journal of the Transportation Research Board, 2313(1), 3–11. doi:10.3141/2313-01
   Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778). Nevada: Las Vegas: Computer Vision Foundation.
   Google Scholar
• Huang, Y. H. (2010). Artificial neural network model of bridge deterioration. Journal of Performance of Constructed Facilities, 24(6), 597–602. doi:10.1061/(ASCE)CF.1943-5509.0000124
   Web of Science ®Google Scholar
• Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning (pp. 448–456), Lille, France, July 2015.
   Google Scholar
• Jiang, Y., & Sinha, K. C. (1989). Bridge service life prediction model using the Markov chain. Transportation Research Record, 1223, 24–30. Retrieved from http://onlinepubs.trb.org/Onlinepubs/trr/1989/1223/1223-003.pdf.
   Google Scholar
• Jiang, Y., Saito, M., & Sinha, K. C. (1988). Bridge performance prediction model using the Markov chain. Transportation Research Record, 1180, 25–32. Retrieved from http://onlinepubs.trb.org/Onlinepubs/trr/1988/1180/1180-005.pdf.
   Google Scholar
• Khodabandehlou, H., Pekcan, G., & Fadali, M. S. (2019). Vibration‐based structural conditionassessment using convolution neural networks. Structural Control and Health Monitoring, 26(2), e2308. doi:10.1002/stc.2308.
   Web of Science ®Google Scholar
• Kobayashi, K., & Kaito, K. (2017). Big data-based deterioration prediction models and infrastructure management: towards assetmetrics. Structure and Infrastructure Engineering, 13(1), 84–93. doi:10.1080/15732479.2016.1198407
   Web of Science ®Google Scholar
• Kobayashi, K., Kaito, K., & Lethanh, N. (2012). A statistical deterioration forecasting method using hidden Markov model for infrastructure management. Transportation Research Part B: Methodological, 46(4), 544–561. doi:10.1016/j.trb.2011.11.008
   Web of Science ®Google Scholar
• Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems (pp. 1097–1105). Lake Tahoe: NIPS.
   Google Scholar
• Kumar, S. S., Abraham, D. M., Jahanshahi, M. R., Iseley, T., & Starr, J. (2018). Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks. Automation in Construction, 91, 273–283. doi:10.1016/j.autcon.2018.03.028
   Web of Science ®Google Scholar
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. doi:10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Lee, J., Sanmugarasa, K., Blumenstein, M., & Loo, Y. C. (2008). Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM). Automation in Construction, 17(6), 758–772. doi:10.1016/j.autcon.2008.02.008
   Web of Science ®Google Scholar
• Liang, X. (2019). Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization. Computer-Aided Civil and Infrastructure Engineering, 34(5), 415–430. doi:10.1111/mice.12425
   Web of Science ®Google Scholar
• Liu, H., & Zhang, Y. (2019). Image-driven structural steel damage condition assessment method using deep learning algorithm. Measurement, 133, 168–181. doi:10.1016/j.measurement.2018.09.081
   Web of Science ®Google Scholar
• Liu, H., & Zhang, Y. (2020). Bridge condition rating data modeling using deep learning algorithm. Structure and Infrastructure Engineering, 16(10), 1447–1460. doi:10.1080/15732479.2020.1712610
   Web of Science ®Google Scholar
• Madanat, S., Mishalani, R., & Ibrahim, W. H. (1995). Estimation of infrastructure transition probabilities from condition rating data. Journal of Infrastructure Systems, 1(2), 120–125. doi:10.1061/(ASCE)1076-0342(1995)1:2(120)
   Google Scholar
• Mauch, M., & Madanat, S. (2001). Semiparametric hazard rate models of reinforced concrete bridge deck deterioration. Journal of Infrastructure Systems, 7(2), 49–57. doi:10.1061/(ASCE)1076-0342(2001)7:2(49)
   Google Scholar
• Micevski, T., Kuczera, G., & Coombes, P. (2002). Markov model for storm water pipe deterioration. Journal of Infrastructure Systems, 8(2), 49–56. doi:10.1061/(ASCE)1076-0342(2002)8:2(49)
   Google Scholar
• Moomen, M., Qiao, Y., Agbelie, B. R., Labi, S., & Sinha, K. C. (2016). Bridge deterioration models to support Indiana’s bridge management system. West Lafayette, IN: Purdue University. doi:10.5703/1288284316348
   Google Scholar
• Morcous, G. (2006). Performance prediction of bridge deck systems using Markov chains. Journal of Performance of Constructed Facilities, 20(2), 146–155. doi:10.1061/(ASCE)0887-3828(2006)20:2(146)
   Web of Science ®Google Scholar
• Morcous, G., Lounis, Z., & Mirza, M. S. (2003). Identification of environmental categories for Markovian deterioration models of bridge decks. Journal of Bridge Engineering, 8(6), 353–361. doi:10.1061/(ASCE)1084-0702(2003)8:6(353)
   Web of Science ®Google Scholar
• Nehme, J. (2019). A new home for bridge data. Public Roads, 4-6. Retrieved from https://www.fhwa.dot.gov/publications/publicroads/19summer/01.cfm.
   Google Scholar
• Nehme, J. (2020). InfoBridge: easy access to the National Bridge Inventory and much more – part 1. ASPIRE, 14(1), 44–45. Retrieved from http://www.aspirebridge.com/magazine/2020Winter/FHWA-InfoBridge.pdf.
   Google Scholar
• Rafiei, M. H., & Adeli, H. (2018). A novel unsupervised deep learning model for global and local health condition assessment of structures. Engineering Structures, 156, 598–607. doi:10.1016/j.engstruct.2017.10.070
   Web of Science ®Google Scholar
• Ranjith, S., Setunge, S., Gravina, R., & Venkatesan, S. (2013). Deterioration prediction of timber bridge elements using the Markov chain. Journal of Performance of Constructed Facilities, 27(3), 319–325. doi:10.1061/(ASCE)CF.1943-5509.0000311
   Web of Science ®Google Scholar
• Sanders, D. H., & Zhang, Y. J. (1994). Bridge deterioration models for states with small bridge inventories. Transportation Research Record, 1442, 101–109. Retrieved from http://onlinepubs.trb.org/Onlinepubs/trr/1994/1442/1442-011.pdf.
   Google Scholar
• Saydam, D., Bocchini, P., & Frangopol, D. M. (2013). Time-dependent risk associated with deterioration of highway bridge networks. Engineering Structures, 54, 221–233. doi:10.1016/j.engstruct.2013.04.009
   Web of Science ®Google Scholar
• Tsuda, Y., Kaito, K., Aoki, K., & Kobayashi, K. (2006). Estimating Markovian transition probabilities for bridge deterioration forecasting. Structural Engineering/Earthquake Engineering, 23(2), 241s–256s. doi:10.2208/jsceseee.23.241s
   Google Scholar
• Wellalage, N. K., Zhang, T., & Dwight, R. (2015). Calibrating Markov chain–based deterioration models for predicting future conditions of railway bridge elements. Journal of Bridge Engineering, 20(2), 04014060. doi:10.1061/(ASCE)BE.1943-5592.0000640
   Web of Science ®Google Scholar
• Zhang, A., Wang, K. C. P., Li, B., Yang, E., Dai, X., Peng, Y., … Chen, C. (2017). Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network. Computer-Aided Civil and Infrastructure Engineering, 32(10), 805–819. doi:10.1111/mice.12297
   Web of Science ®Google Scholar
• Zhang, X., Rajan, D., & Story, B. (2019). Concrete crack detection using context ‐aware deep semantic segmentation network. Computer-Aided Civil and Infrastructure Engineering, 34(11), 951–971. doi:10.1111/mice.12477
   Web of Science ®Google Scholar