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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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
L. BREIMAN, Bagging Predictors, Machine Learning, Vol. 24, 1996, No. 2, PP. 123–140.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
KURT, CARL E., Bridge Management System Software for Local Governments, Transportation Research Record 1184, 1988, Transportation Research Board.Search in Google Scholar
KUSHIDA, M., MIYAMOTO, A., and KINOSHITA, K., Development Of Concrete Bridge Rating Prototype Expert System With Machine Learning, Journal of Comput. Civ. Eng., 1977.Search in Google Scholar
RYALL, M.J., Bridge Management, First Edition, Butterworth – Heinemann, Oxford, 2001.Search in Google Scholar
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.Search in Google Scholar
HALLBERG, D., and RACUTANU, G., Development of The Swedish Bridge Management System by Introducing A LMS Concept. Mater Struct., 2007.Search in Google Scholar
VALENZUELA, S., DE SOLMINIHAC, H., and ECHAVEGUREN, T., Proposal of an Integrated Index for Prioritization of Bridge Maintenance, Journal of Bridge Engineering, 2010.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
