A Novel Decision Support System for Long-Term Management of Bridge Networks
Abstract
:1. Introduction
1.1. State of Practice in Bridge Management
- Technical aspects in the decision making such as condition assessment and performance assessment are mainly held in BMS rather than economic and social analyses that involve life cycle cost analysis, social impact analysis, etc.;
- The decision making based on BMS output is generally for short-term rather than long-term purposes, and the recommended actions are not proactive of future predicted conditions by lacking predictive models and scenario analysis;
- The decision-making models usually do not recommend multiple action strategies with a comparative analysis.
1.2. Long-Term Decision Making
2. Materials and Methods
- ■
- Processing the bridge inventory data of both public and private agencies to retrieve necessary bridge information used in decision support components (e.g., bridge condition, historical data, geolocation);
- ■
- Retrieving local element inspection data directly from NDEs such as Infrared Thermography (IRT), Ground Penetration Radar (GPR), laser scanning, remote sensing, and drone inspections;
- ■
- Element condition assessment based on the quantified damage information and Health index (HI) calculation of the structure. Analysis of historical element condition states to predict the future condition using a time series forecasting model that estimates the damage growth;
- ■
- A novel, adaptive decision ranking implementation for bridge maintenance decisions using bridge appraisals and a deep learning-based ranking algorithm;
- ■
- Adapting the infrastructure owner’s maintenance practice through periodic model updates to fine-tune the decision ranking weights using automatically generated data from users’ decision actions;
- ■
- Decision tree implementation to produce maintenance/repair strategies with alternative actions and associated cost calculation;
- ■
- Damage visualization on realistic 3D bridge model with a timeline feature demonstration of both past and future conditions;
- ■
- Data exchange and synchronization with infrastructure owner’s bridge management software and the NBI database.
2.1. Integration of Nondestructive Evaluation Data
2.2. Deep Learning-Based Prediction of Deterioration Growth
2.3. Adaptive Bridge Decision Ranking
- ■
- Critical condition and worse (r < 2) → CR = 55;
- ■
- Serious condition (r = 3) → CR = 40;
- ■
- Poor condition (r = 4) → CR = 25;
- ■
- Fair condition (r = 5) → CR = 10.
2.4. Decision Strategy Generation
3. Results
4. Discussions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- U.S Department of Transportation Federal Highway Administration (FHWA) and Federal Transit Administration (FTA). Status of the Nation’s Highways, Bridges, and Transit: Conditions and Performance. Report to Congress, 23rd ed.; 2019; FHWA-PL-20-001. Available online: https://rosap.ntl.bts.gov/view/dot/43598 (accessed on 21 May 2019).
- U.S Department of Transportation Federal Highway Administation. Deficient Bridges by Highway System; 2017. Available online: https://www.fhwa.dot.gov/bridge/nbi/no10/defbr17.cfm (accessed on 21 May 2019).
- American Society of Civil Engineers. ASCE 2017 Report Card for America’s Infrastructure; ASCE. 53 (2016). Available online: https://www.infrastructurereportcard.org/ (accessed on 21 May 2019).
- Graybeal, B.A.; Phares, B.M.; Rolander, D.D.; Moore, M.; Washer, G. Visual Inspection of Highway Bridges. J. Nondestruct. Eval. 2002, 21, 67–83. [Google Scholar] [CrossRef]
- Holford, K.M.; Lark, R.J. Acoustic emission testing of bridges. In Inspection and Monitoring Techniques for Bridges and Civil Structures; Elsevier Ltd.: Amsterdam, The Netherlands, 2005; pp. 183–215. ISBN 9781855739390. [Google Scholar]
- Chun, S.C.; Mohsen, S. Understanding Capacity Rating of Bridges from Load Tests. Pract. Period. Struct. Des. Constr. 2003, 8, 209–216. [Google Scholar]
- Catbas, F.N.; Hiasa, S.; Dong, C.; Pan, Y.; Celik, O.; Karaaslan, E. Comprehensive Structural Health Monitoring at Local and Global Level with Vision-based Technologies. 26th ASNT Res. Symp. 2017, 26, 10–21. [Google Scholar]
- FHWA. National Bridge Inspection Standards Regulations (NBIS). Fed. Regist. 2004, 69, 15–35. [Google Scholar]
- FHWA. Specification for the National Bridge Inventory; FHWA, 2014. Available online: https://www.fhwa.dot.gov/bridge/nbi/131216_a1.pdf (accessed on 21 May 2019).
- Rashidi, M.; Samali, B.; Sharafi, P. A new model for bridge management: Part A: Condition assessment and priority ranking of bridges. Aust. J. Civ. Eng. 2016, 14, 35–45. [Google Scholar] [CrossRef] [Green Version]
- Small, E.P.; Philbin, T.; Fraher, M.; Romack, G.P. Current Status of Bridge Management System Implementation in the United States. In Proceedings of the Eighth Transportation Research Board Conference on Bridge Management, Denver, CO, USA, 26–28 April 1999; TRB Transportation Research Circular 498. p. A-1/1-16. [Google Scholar]
- Yin, Z.; Li, Y. Intelligent decision support system for bridge monitoring. In Proceedings of the International Conference on Machine Vision and Human-Machine Interface, Kaifeng, China, 24–25 April 2010; pp. 491–494. [Google Scholar]
- Rashidi, M.; Samali, B.; Sharafi, P. A new model for bridge management: Part B: Decision support system for remediation planning. Aust. J. Civ. Eng. 2016, 14, 46–53. [Google Scholar] [CrossRef] [Green Version]
- Hiasa, S.; Catbas, F.N.; Matsumoto, M.; Mitani, K. Considerations and Issues in the Utilization of Infrared Thermography for Concrete Bridge Inspection at Normal Driving Speeds. J. Bridge Eng. 2017, 22, 4017101. [Google Scholar] [CrossRef]
- Karaaslan, E.; Hiasa, S.; Catbas, F.N. FIST: Framework for Infrastructure Support Technologies, a Decision Support Implementation for Bridge Networks. In Proceedings of the Transportation Research Board 97th Annual Meeting, Washington, DC, USA, 7–11 January 2018. [Google Scholar]
- Hiasa, S.; Karaaslan, E.; Shattenkirk, W.; Mildner, C.; Catbas, F.N. Bridge Inspection and Condition Assessment Using Image-Based Technologies with UAVs. In Proceedings of the Structures Congress 2018: Bridges, Transportation Structures, and Nonbuilding Structures—Selected Papers from the Structures Congress, Fort Worth, TX, USA, 19–21 April 2018. [Google Scholar]
- Ozer, E.; Feng, M.Q. Structural Reliability Estimation with Participatory Sensing and Mobile Cyber-Physical Structural Health Monitoring Systems. Appl. Sci. 2019, 9, 2840. [Google Scholar] [CrossRef] [Green Version]
- Ghiasi, R.; Noori, M.; Altabey, W.A.; Silik, A.; Wang, T.; Wu, Z. Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach. Appl. Sci. 2021, 11, 770. [Google Scholar] [CrossRef]
- Lu, N.; Liu, Y.; Noori, M.; Xiao, X. System Reliability Assessment of Cable-Supported Bridges under Stochastic Traffic Loads Based on Deep Belief Networks. Appl. Sci. 2020, 10, 8049. [Google Scholar] [CrossRef]
- Quintela, H.; Santos, M.F.; Cortez, P. Real-Time Intelligent Decision Support System for Bridges Structures Behavior Prediction. In Proceedings of the Progress in Artificial Intelligence: 13th Portuguese Conference on Aritficial Intelligence, EPIA 2007, Workshops: GAIW, AIASTS, ALEA, AMITA, BAOSW, BI, CMBSB, IROBOT, MASTA, STCS, and TEMA, Guimar{ã}es, Portugal, 3–7 December 2007; Neves, J., Santos, M.F., Machado, J.M., Eds.; Springer: Berlin/Heidelberg, Germany, 2007; pp. 124–132, ISBN 978-3-540-77002-2. [Google Scholar]
- Jiao, Y.; Liu, H.; Zhang, P.; Wang, X.; Wei, H. Unsupervised performance evaluation strategy for bridge superstructure based on fuzzy clustering and field data. Sci. World J. 2013, 2013, 427072. [Google Scholar] [CrossRef]
- Lee, J.; Sanmugarasa, K.; Blumenstein, M.; Loo, Y.C. Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM). Autom. Constr. 2008, 17, 758–772. [Google Scholar] [CrossRef] [Green Version]
- Bocchini, P.; Saydam, D.; Frangopol, D.M. Efficient, accurate, and simple Markov chain model for the life-cycle analysis of bridge groups. Struct. Saf. 2013, 40, 51–64. [Google Scholar] [CrossRef]
- Mishra, A. Using Google TensorFlow with Amazon SageMaker. In Machine Learning in the AWS Cloud; Sybex: Hoboken, NJ, USA, 2019. [Google Scholar]
- Gucunski, N.; Basily, B.; Kim, J.; Yi, J.; Duong, T.; Dinh, K.; Kee, S.-H.; Maher, A. RABIT: Implementation, performance validation and integration with other robotic platforms for improved management of bridge decks. Int. J. Intell. Robot. Appl. 2017, 1, 271–286. [Google Scholar] [CrossRef]
- Bolukbasi, M.; Mohammadi, J.; Arditi, D. Estimating the Future Condition of Highway Bridge Components Using National Bridge Inventory Data. Pract. Period. Struct. Des. Constr. 2004, 9, 16–25. [Google Scholar] [CrossRef]
- Shepard, R.W.; Johnson, M.B.; Board, T.R. Evaluating Bridge Health: California’s diognastic tool. TR News 2001, 215, 6–11. [Google Scholar]
- American Association of State Highway and Transportation Officials (AASHTO). Manual for Bridge Element Inspection (MBEI), AASHTO, 2nd ed.; 2019, p. 172, ISBN 9781560517238. Available online: file:///C:/Users/MDPI/AppData/Local/Temp/MBEI-2_TableOfContents.pdf. (accessed on 21 May 2019).
- American Association of State Highway and Transportation Officials (AASHTO). AASHTOWare Bridge Management 1990. Available online: https://www.aashtowarebridge.com. (accessed on 21 May 2019).
- Federal Highway Administration. LTBP InfoBridge: An Intuitive and User-Friendly Interface to Access, Visualize, and Analyze Bridge Performance Data. Long Term Bridg. Perform. Progr. 2. 2019. Available online: https://highways.dot.gov/research/long-term-infrastructure-performance/ltbp/long-term-bridge-performance. (accessed on 21 May 2019).
- Sainath, T.N.; Vinyals, O.; Senior, A.; Sak, H. Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks. In Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing—Proceedings, Brisbane, QCL, Australia, 19–24 April 2015. [Google Scholar]
- Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef]
- Gers, F. Long short-term memory in recurrent neural networks. Neural Comput. 2001. [Google Scholar] [CrossRef]
- Gers, F.A.; Schmidhuber, J.; Cummins, F. Learning to forget: Continual prediction with LSTM. Neural Comput. 2000, 12, 2451–2471. [Google Scholar] [CrossRef]
- Graves, A.; Mohamed, A.; Hinton, G. Speech Recognition with Deep Recurrent Neural Networks. 2013. Available online: https://arxiv.org/abs/1303.5778. (accessed on 21 May 2019).
- Roondiwala, M.; Patel, H.; Varma, S. Predicting Prices Using LSTM. Int. J. Sci. Res. 2015, 6, 1754–1756. [Google Scholar]
- Guo, J.; Liang, Z.; Ditzler, G.; Bouaynaya, N.C.; Scribner, E.; Fathallah-Shaykh, H.M. Nonlinear Brain Tumor Model Estimation with Long Short-Term Memory Neural Networks. In Proceedings of the International Joint Conference on Neural Networks, Dhaka, Bangladesh, 14–15 December 2018. [Google Scholar]
- Bisong, O.E.; Oommen, B.J. Optimizing Self-organizing Lists-on-Lists Using Enhanced Object Partitioning. In Proceedings of the IFIP Advances in Information and Communication Technology; Springer: Berlin, Germany, 2019. [Google Scholar]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84–89. [Google Scholar] [CrossRef]
- Karaaslan, E.; Bagci, U.; Catbas, F.N. Artificial Intelligence Assisted Infrastructure Assessment using Mixed Reality Systems. Transp. Res. Record 2019, 2673, 413–424. [Google Scholar] [CrossRef] [Green Version]
- Karaaslan, E.; Bagci, U.; Necati, C.F. Attention-Guided Analysis of Infrastructure Damage with Semi-Supervised Deep Learning. Autom. Constr. 2021, 125, 103634. [Google Scholar] [CrossRef]
- Chollet, F. Keras: The Python Deep Learning library. Keras.Io 2015. Available online: https://keras.io. (accessed on 21 May 2019).
- Ai, Q.; Wang, X.; Bruch, S.; Golbandi, N.; Bendersky, M.; Najork, M. Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks. 2019. Available online: https://arxiv.org/abs/1811.04415. (accessed on 21 May 2019).
- Pasumarthi, R.K.; Bruch, S.; Wang, X.; Li, C.; Bendersky, M.; Najork, M.; Pfeifer, J.; Golbandi, N.; Anil, R.; Wolf, S. TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. Available online: https://arxiv.org/abs/1812.00073. (accessed on 21 May 2019).
- Pasumarthi, R.K.; Bruch, S.; Bendersky, M.; Wang, X. Neural Learning to Rank Using TensorFlow Ranking: A Hands-on Tutorial. In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval; ACM: New York, NY, USA, 2019; pp. 253–254. [Google Scholar]
- Chang, C.C.; Lin, C.J. LIBSVM: A Library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2, 1–27. [Google Scholar] [CrossRef]
- Bektas, B.A.; Carriquiry, A.; Smadi, O. Using Classification Trees for Predicting National Bridge Inventory Condition Ratings. J. Infrastruct. Syst. 2014, 19, 425–433. [Google Scholar] [CrossRef]
- FHWA. Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation’s Bridges; 1995. Available online: https://www.fhwa.dot.gov/bridge/mtguide.pdf. (accessed on 21 May 2019).
- Sobanjo, J.O.; Thompson, P.D.; Kerr, R.; Board, T.R. Element-to-Component Translation of Bridge Condition Ratings; 2008; 25p. Available online: https://www.fhwa.dot.gov/bridge/mtguide.pdf. (accessed on 21 May 2019).
- Karaaslan, E.; Zhao, Y.; Tatari, O. Comparative life cycle assessment of sport utility vehicles with different fuel options. Int. J. Life Cycle Assess. 2016, 23, 1–15. [Google Scholar] [CrossRef]
- Necati, C.F.; Gul, M.; Zaurin, R.; Gokce, H.B.; Maier, D.; Terrell, T. Structural health monitoring for life cycle management of bridges. In Life-Cycle Civil Engineering; CRC Press: Boca Raton, FL, USA, 2008; p. 6. ISBN 9780429207259. [Google Scholar]
- Hawk, H. Bridge Life-Cycle Cost Analysis; NCHRP Report 483; Springer: Berlin, Germany, 2002. [Google Scholar]
- Mohammadi, J.; Guralnick, S.A.; Yan, L. Incorporating Life-Cycle Costs in Highway-Bridge Planning and Design. J. Transp. Eng. 1995, 121, 417–424. [Google Scholar] [CrossRef]
- Abu Dabous, S.; Alkass, S. Decision support method for multi-criteria selection of bridge rehabilitation strategy. Constr. Manag. Econ. 2008, 26, 883–893. [Google Scholar] [CrossRef]
- Transportation FD of FDOT Work Program Instructions; 2019; Volume 8. Available online: https://www.fdot.gov/workprogram/development/wp-instructions.shtm. (accessed on 21 May 2019).
- Kalervo, J.; Jaana, K. IR evaluation methods for retrieving highly relevant documents. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ‘00); Association for Computing Machinery: New York City, NY, USA, 2000; pp. 41–48. [Google Scholar] [CrossRef]
- Florida Department of Transportation (FDOT) Office of Maintenance. Bridge Maintenance and Repair Handbook; 2019; FDOT. Available online: https://www.fdot.gov/maintenance/publications.shtm. (accessed on 21 May 2019).
- Hearn, G.; Thompson, P.D.; Mystkowski, W.; Hyman, W. Framework for a National Database System for Maintenance Actions on Highway Bridges; 2011; ISBN 9780309155243. Available online: http://www.trb.org/Publications/Blurbs/164203.aspx (accessed on 21 May 2019).
District | Intersected Feature | Type | Structural Adequacy | Serviceability | Bridge Importance | Value Index | Available Fund | Decision Ranking |
---|---|---|---|---|---|---|---|---|
FL-2 | Brown Creek | Prestressed | 37% | 20% | 10% | 14.8 | $18.3M | 40 |
FL-4 | Palm Avenue | Prestressed | 45% | 25% | 12% | 15.4 | $15.9M | 42 |
FL-5 | Lake Jesup | Prestressed | 54% | 29% | 18% | 20.1 | $9.2M | 38 |
FL-1 | Gum Creek | Concrete | 55% | 30% | 15% | 17.8 | $10.3M | 45 |
... |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Karaaslan, E.; Bagci, U.; Catbas, N. A Novel Decision Support System for Long-Term Management of Bridge Networks. Appl. Sci. 2021, 11, 5928. https://doi.org/10.3390/app11135928
Karaaslan E, Bagci U, Catbas N. A Novel Decision Support System for Long-Term Management of Bridge Networks. Applied Sciences. 2021; 11(13):5928. https://doi.org/10.3390/app11135928
Chicago/Turabian StyleKaraaslan, Enes, Ulas Bagci, and Necati Catbas. 2021. "A Novel Decision Support System for Long-Term Management of Bridge Networks" Applied Sciences 11, no. 13: 5928. https://doi.org/10.3390/app11135928