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Abstract: This research aims to develop an advanced deep learning-based ensemble algorithm, utilizing environmental temperature and solar radiation as feature factors, to conduct hourly temperature field predictions for steel-concrete composite decks (SCCDs). The proposed model comprises feature parameter lag selection, two non-stationary time series decomposition methods (empirical mode decomposition (EMD) and time-varying filtering-based empirical mode decomposition (TVFEMD)), and a stacking ensemble prediction model. To validate the proposed model, five machine learning (ML) models (random forest (RF), support vector regression (SVR), multilayer perceptron (MLP), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost)) were tested as base learners and evaluations were conducted within independent, mixed, and ensemble frameworks. Finally, predictions are made based on engineering cases. The results indicate that consideration of lag variables and modal decomposition can significantly improve the prediction performance of learners, and the stacking framework, which combines multiple learners, achieves superior prediction results. The proposed method demonstrates a high degree of predictive robustness and can be applied to statistical analysis of the temperature field in SCCDs. Incorporating time lag features helps account for the delayed heat dissipation phenomenon in concrete, while decomposition techniques assist in feature extraction.

基于TVFEMD-Stacking集成算法的钢-混凝土组合桥面板温度场预测

[1]BoudraaAO, CexusJC, 2007. EMD-based signal filtering. IEEE Transactions on Instrumentation and Measurement, 56(6):2196-2202.

[2]BrancoFA, MendesPA, 1993. Thermal actions for concrete bridge design. Journal of Structural Engineering, 119(8):2313-2331.

[3]BrooDG, Bravo-HaroM, SchoolingJ, 2022. Design and implementation of a smart infrastructure digital twin. Automation in Construction, 136:104171.

[4]CatbasFN, SusoyM, FrangopolDM, 2008. Structural health monitoring and reliability estimation: long span truss bridge application with environmental monitoring data. Engineering Structures, 30(9):2347-2359.

[5]ChenFH, ZhangHP, LiZC, et al., 2024. Residual stresses effects on fatigue crack growth behavior of rib-to-deck double-sided welded joints in orthotropic steel decks. Advances in Structural Engineering, 27(1):35-50.

[6]FanJS, LiuYF, LiuC, 2021. Experiment study and refined modeling of temperature field of steel-concrete composite beam bridges. Engineering Structures, 240:112350.

[7]FanJS, LiBL, LiuC, et al., 2022. An efficient model for simulation of temperature field of steel-concrete composite beam bridges. Structures, 43:1868-1880.

[8]FigueiredoE, SantosLO, MoldovanI, et al., 2023. A roadmap for an integrated assessment approach to the adaptation of concrete bridges to climate change. Journal of Bridge Engineering, 28(6):03123002.

[9]FlahM, NunezI, ChaabeneWB, et al., 2021. Machine learning algorithms in civil structural health monitoring: a systematic review. Archives of Computational Methods in Engineering, 28(4):2621-2643.

[10]FriedmanJH, 2001. Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29(5):‍‍1189-1232.

[11]FuWW, SunBC, WanHP, et al., 2022. A Gaussian processes-based approach for damage detection of concrete structure using temperature-induced strain. Engineering Structures, 268:114740.

[12]GiussaniF, 2009. The effects of temperature variations on the long-term behaviour of composite steel–concrete beams. Engineering Structures, 31(10):2392-2406.

[13]HanQH, MaQ, XuJ, et al., 2021. Structural health monitoring research under varying temperature condition: a review. Journal of Civil Structural Health Monitoring, 11(1):149-173.

[14]InnocenziRD, NicolettiV, ArezzoD, et al., 2022. A good practice for the proof testing of cable-stayed bridges. Applied Sciences, 12(7):3547.

[15]JameiM, KarbasiM, AliM, et al., 2023. A novel global solar exposure forecasting model based on air temperature: designing a new multi-processing ensemble deep learning paradigm. Expert Systems with Applications, 222:119811.

[16]LeeJH, 2012. Investigation of extreme environmental conditions and design thermal gradients during construction for prestressed concrete bridge girders. Journal of Bridge Engineering, 17(3):547-556.

[17]LiuHJ, ChenC, GuoZQ, et al., 2021. Overall grouting compactness detection of bridge prestressed bellows based on RF feature selection and the GA-SVM model. Construction and Building Materials, 301:124323.

[18]LiuJ, LiuYJ, ZhangCY, et al., 2020. Temperature action and effect of concrete-filled steel tubular bridges: a review. Journal of Traffic and Transportation Engineering, 7(2):174-191.

[19]LuoY, LiuXF, ChenFH, et al., 2023. Numerical simulation on crack‍–‍inclusion interaction for rib-to-deck welded joints in orthotropic steel deck. Metals, 13(8):1402.

[20]NarasimhanTN, 1999. Fourier’s heat conduction equation: history, influence, and connections. Reviews of Geophysics, 37(1):151-172.

[21]NguyenH, VuT, VoTP, et al., 2021. Efficient machine learning models for prediction of concrete strengths. Construction and Building Materials, 266:120950.

[22]NicolettiV, QuarchioniS, TentellaL, et al., 2023. Experimental tests and numerical analyses for the dynamic characterization of a steel and wooden cable-stayed footbridge. Infrastructures, 8(6):100.

[23]OpokuDGJ, PereraS, Osei-KyeiR, et al., 2021. Digital twin application in the construction industry: a literature review. Journal of Building Engineering, 40:102726.

[24]QinYH, HillerJE, 2011. Modeling temperature distribution in rigid pavement slabs: impact of air temperature. Construction and Building Materials, 25(9):3753-3761.

[25]RichmanJS, MoormanJR, 2000. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6):H2039-H2049.

[26]ShengXW, ZhouTM, HuangSJ, et al., 2022. Prediction of vertical temperature gradient on concrete box-girder considering different locations in China. Case Studies in Construction Materials, 16:e01026.

[27]ShiT, LouP, ZhengWQ, et al., 2022. A hybrid approach to predict vertical temperature gradient of ballastless track caused by solar radiation. Construction and Building Materials, 352:129063.

[28]ShimCS, LeePG, ChangSP, 2001. Design of shear connection in composite steel and concrete bridges with precast decks. Journal of Constructional Steel Research, 57(3):203-219.

[29]SohnH, DzwonczykM, StraserEG, et al., 1999. An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge. Earthquake Engineering & Structural Dynamics, 28(8):879-897. https://doi.‍org/10.1002/(SICI)1096-9845(199908)28:‍8<879::AID-EQE845>3.0.CO;2-V

[30]SugumaranV, MuralidharanV, RamachandranKI, 2007. Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing, 21(2):930-942.

[31]TaylorKE, 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7):7183-7192.

[32]TongM, ThamLG, AuFTK, 2002. Extreme thermal loading on steel bridges in tropical region. Journal of Bridge Engineering, 7(6):357-366.

[33]WangD, LiuYM, LiuY, 2018. 3D temperature gradient effect on a steel-concrete composite deck in a suspension bridge with field monitoring data. Structural Control and Health Monitoring, 25(7):e2179.

[34]WangD, TanBK, WangX, et al., 2021. Experimental study and numerical simulation of temperature gradient effect for steel-concrete composite bridge deck. Measurement and Control, 54(5-6):681-691.

[35]WangJ, DuXY, QiX, 2022. Strain prediction for historical timber buildings with a hybrid Prophet-XGBoost model. Mechanical Systems and Signal Processing, 179:109316.

[36]WangZW, ZhangWM, TianGM, et al., 2020. Joint values determination of wind and temperature actions on long-span bridges: copula-based analysis using long-term meteorological data. Engineering Structures, 219:110866.

[37]WedelF, MarxS, 2022. Application of machine learning methods on real bridge monitoring data. Engineering Structures, 250:113365.

[38]XinJZ, ZhouCY, JiangY, et al., 2023. A signal recovery method for bridge monitoring system using TVFEMD and encoder-decoder aided LSTM. Measurement, 214:112797.

[39]ZhangCY, LiuYJ, LiuJ, et al., 2020. Validation of long-term temperature simulations in a steel-concrete composite girder. Structures, 27:1962-1976.

[40]ZhangPJ, WangCS, WuGS, et al., 2022. Temperature gradient models of steel-concrete composite girder based on long-term monitoring data. Journal of Constructional Steel Research, 194:107309.

[41]ZhangZJ, LiuYJ, LiuJ, et al., 2023. Experimental study and analysis for the long-term behavior of the steel–concrete composite girder bridge. Structures, 51:1305-1327.

[42]ZhaoHW, DingYL, LiAQ, et al., 2023. Digital modeling approach of distributional mapping from structural temperature field to temperature-induced strain field for bridges. Journal of Civil Structural Health Monitoring, 13(1):251-267.

[43]ZouH, HastieT, 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2):301-320.