BMS Forecasting of Bridge Health Condition Degradation Using AI Machine Learning
References
- Y. DENG, Analysis of Government Financial Responsibility for Highway Maintenance, PHD THESIS, Research Institute for Fiscal Science, Ministry of Finance, P.R, CHINA, BEIJING, 2014.
- IRALDA XHAFERAJ, ARIAN LAKO AND NERITAN SHKODRANI, SEISMIC RISK ASSESSMENT OF SIMPLY SUPPORTED GIRDERS BRIDGES, CIVIL AND ENVIRONMENTAL ENGINEERING, VOL. 19, 2023, PP. 30-38, DOI: 10.2478/CEE-2023-0003.
- MONICA SANTAMARIA ARIZA, IVAN ZAMBON, HÉLDER S. SOUSA, JOSÉ ANTÓNIO CAMPOS E MATOS, and ALFRED STRAUSS, Comparison of Forecasting Models to Predict Concrete Bridge Decks Performance, International Federation for Structural Concrete (fib), Structural Concrete, Vol. 21, 2020, pp.1240–1253.
- ADMINISTRATION, F.H. Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation’s Bridges; US Department of Transportation: Washington, DC, USA, 1995.
- ISHWARYA SRIKANTH and MADASAMY AROCKIASAMY, Deterioration Models for Prediction of Remaining Useful Life of Timber and Concrete Bridges-A Review, Journal of Traffic and Transportation Engineering (English Edition), Vol. 7 (2), 2020, pp. 152-173.
- LIMING JIANG, QIZHI TANG, YAN JIANG, HUAISONG CAO, and ZHE XU, Bridge Condition Deterioration Prediction Using the Whale Optimization Algorithm and Extreme Learning Machine, Buildings, Vol. 13, 2023, pp. 2730, doi: 10.3390/buildings13112730.
- FARIBA FARD and FERESHTEH SADEGHI NAIENI FARD, Development and Utilization of Bridge Data Of The United States For Predicting Deck Condition Rating Using Random Forest, Xgboost, and Artificial Neural Network, Remote Sensing, Vol. 16, 2024, pp. 367, doi: 10.3390/rs16020367.
- LIU, H. and ZHANG, Y., Bridge Condition Rating Data Modelling Using Deep Learning Algorithm, Structure and Infrastructure Engineering, Vol. 16, NO. 10, 2020, pp. 1447-1460, doI: 10.1080/15732479.2020.1712610.
- XIA, Y., LEI, X., WANG, P. and SUN, L., A Data-Driven Approach for Regional Bridge Condition Assessment Using Inspection Reports, Structural Control and Health Monitoring, Vol. 29, NO. 4, 2022, doi: 10.1002/STC.2915.
- TRACH, R., MOSHYNSKYI, V., CHERNYSHEV, D., BORYSYUK, O., TRACH, Y., STRILETSKYI, P. and TYVONIUK, V, Modelling the Quantitative Assessment of the Condition of Bridge Components Made of Reinforced Concrete Using ANN, Sustainability, Vol. 14, NO. 23, 15779, 2022, doi: 10.3390/SU142315779.
- MARTINEZ, P., MOHAMED, E., MOHSEN, O. and MOHAMED, Y., Comparative Study of Data Mining Models for Prediction of Bridge Future Conditions, Journal of Performance of Constructed Facilities, Vol. 34 NO. 1, 2020, doi: 10.1061/(ASCE)CF.1943-5509.0001395.
- LI, Q. and SONG, Z., Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition, Applied Sciences, Vol. 12, NO. 11, 2020, P. 5442, doi: 10.3390/APP12115442.
- MIA, M.M. and KAMESHWAR, S., MACHINE LEARNING APPROACH FOR PREDICTING BRIDGE COMPONENTS’ CONDITION RATINGS, Frontiers in Built Environment, Vol. 9 NO. 1254269, 2023, pp. 1-15, doi: 10.3389/FBUIL.2023.1254269.
- FENG, D.-C., WANG, W.-J., MANGALATHU, S. and SUN, Z., Condition Assessment of Highway Bridges Using Textual Data and Natural Language Processing- (Nlp) Based Machine Learning Models, In Mukhopadhyay, S. (Ed.), Structural Control and Health Monitoring. Wiley-Hindawi, Vol. 2023, 2023, pp. 1-17, doi: 10.1155/2023/9761154.
- QINGFU LI, and ZONGMING SONG, Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition, Applied Science, Vol. 12, 2022, pp. 5442, DOI: 10.3390/app12115442.
- J. ELITH, J. R. LEATHWICK, and T. HASTIE, A Working Guide To Boosted Regression Trees, Journal of Animal Ecology, Vol. 77, 2008, pp. 802–813.
- MOUSA, S. R., and S. ISHAK. An Extreme Gradient Boosting Algorithm for Freeway Short-Term Travel Time Prediction Using Basic Safety Messages of Connected Vehicles. Conference of the Transportation Research Board 96th Annual Meeting, Washington, D.C., 2017.
- MOMEN R. MOUSA, MOUSA, S. R., and Paul Carlson, Predicting the Retroreflectivity Degradation of Waterborne Paint Pavement Markings using Advanced Machine Learning Techniques, Transportation Research Record (TRR), Vol. 2675(9), 2021, pp. 483–494, doi: 10.1177/03611981211002844.
- HIKMAT DAOU and WASSIM RAPHAEL, Ensemble Tree Machine Learning Models for Improvement of Eurocode 2 Creep Model Prediction, Civil and Environmental Engineering, Vol. 18, 2022, pp. 174-184, doi: 10.2478/cee-2022-0016.
- MOUSA, S. R., P. R. BAKHIT, O. A. OSMAN, and S. ISHAK, A Comparative Analysis of Tree-Based Ensemble Methods for Detecting Imminent Lane Change Maneuvers in Connected Vehicle Environments, Journal of the Transportation Research Board, Vol. 2672, 2018, pp. 268–279.
- L. BREIMAN, Bagging Predictors, Machine Learning, Vol. 24, 1996, No. 2, PP. 123–140.
- CHEN, T. and GUESTRIN C., XGBoost: A Scalable Tree Boosting System, In Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, ACM. pp. 785–794.
- CHEN, T., and HE, T., HIGGS BOSON, Discovery With Boosted Trees. In NIPS Workshop on High-energy Physics and Machine Learning, 2015, pp. 69–80.
- MAYR, A., BINDER, H., GEFELLER, O., and SCHMID, M., The evolution of Boosting Algorithms, Methods of Information in Medicine, 53(6), 2014, pp. 419–427, doi:10.3414/ME13-01-0122. PMID:25112367.
- KURT, CARL E., Bridge Management System Software for Local Governments, Transportation Research Record 1184, 1988, Transportation Research Board.
- KUSHIDA, M., MIYAMOTO, A., and KINOSHITA, K., Development Of Concrete Bridge Rating Prototype Expert System With Machine Learning, Journal of Comput. Civ. Eng., 1977.
- RYALL, M.J., Bridge Management, First Edition, Butterworth – Heinemann, Oxford, 2001.
- LIU, M., and FRANGOPOL, D. M., Decision support system for bridge network maintenance planning. Advances in Engineering Structures, Mechanics and Construction, M. Pandey, W-C. Xie, and L. Xu, eds., Springer, The Netherlands, 2006.
- HALLBERG, D., and RACUTANU, G., Development of The Swedish Bridge Management System by Introducing A LMS Concept. Mater Struct., 2007.
- VALENZUELA, S., DE SOLMINIHAC, H., and ECHAVEGUREN, T., Proposal of an Integrated Index for Prioritization of Bridge Maintenance, Journal of Bridge Engineering, 2010.
- CHASE, S.B., ADU-GYAMFI, Y., AKTAN, A.E., and MINAIE, E., Synthesis of National and International Methodologies Used for Bridge Health Indices, FHWA Report FHWA-HRT-15-081, Georgetown, Pike, VA: Federal Highway Administration. 2016.
- NWS, National Weather Service, Heat Index Chapter, 2011.
- FUJIAN WANG, HUILING CHENG, HONGLIANG DAI, and HAIHANG HAN, Freeway Short-Term Travel Time Prediction Based on LightGBM Algorithm, Earth and Environmental Science Conference Series, 2021, IOP Publishing, doi:10.1088/17551315/638/1/012029.
- LI, S., XIN, J., and JIANG, Y., Temperature-Induced Deflection Separation Based On Bridge Deflection Data Using the TVFEMD-PE-KLD Method, Journal of Civil Structural Health Monitoring, Vol. 13, 2023, pp. 781–797.
- TONG, K., ZHANG, H., ZHAO, and R., Investigation of SMFL Monitoring Technique for Evaluating the Load-Bearing Capacity of RC Bridge, Eng. Struct., Vol. 293, 2023, pp. 116667.
- HAMDI, HADIWARDOYO, S.P., CORREIA, A.G., PEREIRA, P., and CORTEZ, Prediction of surface Distress Using Neural Networks, AIP Conf. Proc. 2017, 1855, 040006, doi: 10.1063/1.4985502.
Language: English
Page range: 246 - 253
Published on: Apr 16, 2025
Published by: University of Žilina
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
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© 2025 Mohamed G. Elbaroty, Mohamed A. Zaki, Sherif A. Mourad, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.