服役桥梁构件随机场参数估计及相应方法比较分析

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Abstract Three 36-year-old beams are taken as research objects to estimate the fluctuation scale θ of structural dimensions and material property parameters of reinforced concrete structure in service. First, the protective layer thickness C_d and the concrete compressive strength f_c of service bridge component are measured, and the ordinary kriging method is used to interpolate the measured data and gather effective data. Second, the θ values of C_d and f_c are estimated by two methods, namely autocorrelation function (ACF) and semivariogram function (SVF). Thereafter, the spatial distribution of R of each corroded steel bar is studied on the basis of measured values of the pitting factor R at different positions of 246 corroded steel bars. On the basis of one-dimensional random field theory, each beam is discretized into several small elements. According to the fluctuation scale obtained by different estimation methods, the random variables are transformed into spatial correlation variables, and the parameters of each discrete element are obtained. Then the bending capacity of each discrete beam element and the corresponding whole beam are obtained on the basis of the value of parameters of each discrete element. Finally, the two estimation methods are compared and analyzed by comparing the theoretical moment value and the bearing capacity test results of the corroded beams. Results show that the mean values of θ of f_c and C_d obtained by the SVF method are 2.242 and 2.467, respectively, which are larger than that obtained by the ACF method. Moreover, the fluctuation intensity of concrete compressive strength is smaller with an increase of θ value, and no firm correlation is identified between the spatial distributions of each pit on the surface of a corroded steel reinforcement. The bending moments of three beams from the SVF method are in better agreement with the experimental values than those predicted from the ACF method. In addition, the average relative error of the theoretical bending moment value of all beams based on the SVF estimation is 6.52%, which is 33.19% lower than that obtained by ACF method. Therefore, the SVF method is more effective in describing the spatial properties of material and geometrical parameters compared with the ACF method.

Key words： bridge engineering fluctuation scale autocorrelation function method semi variogram function method reinforced concrete structure corrosion bending moment

Received: 22 August 2017

Fund:Supported by the National Basic Research Program of China (973 Program, No.2015CB057706), the National Natural Science Foundation of China (Nos. 51478050,51378081), the Excellent Young Research Program by the Department of Education at Hunan Province (No.15B015), and the Open Fund of Industry Key Laboratory of Traffic Infrastructure Security Risk Management (CSUST, No. 16BCX08)

Corresponding Authors: YANG Yi-ming E-mail: yimingyang91@outlook.com

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Cite this article:

YANG Yi-ming,PENG Jian-xin,ZHANG Jian-ren. Random Field Parameter Estimation of Service Bridge Component and Comparative Analysis of Estimation Methods[J]. Journal of Highway and Transportation Research and Development, 2019, 13(1): 38-49.

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http://manu27.magtech.com.cn/Jwk_gljtkj_en/EN/ OR http://manu27.magtech.com.cn/Jwk_gljtkj_en/EN/Y2019/V13/I1/38

[1] LEI Yu, LI Xi. Study of Stability of Long-span Composite Deck Cable-stayed Bridge during Construction Process[J]. Journal of Highway and Transportation Research and Development, 2009, 26(10):71-75. (in Chinese)
[2] PENG Jian-xin, ZHANG Jian-ren, ZHANG Ke-bo, et al. Probabilistic Resistance Model and Experimental Investigation for Corroded RC Bridges[J]. Engineering Mechanics, 2012, (6):125-132. (in Chinese)
[3] GUO Tong, RICHARD S, FRANGOPOL D M, et al. Time-dependent Reliability of PSC Box-Girder Bridge Considering Creep, Shrinkage, and Corrosion[J]. Journal of Bridge Engineering, 2011, 16(1):29-43.
[4] MA Ya-li, ZHANG Ai-lin. Durability Life Prediction of Concrete Structure Based on the Regulated Reliability Index under Chloride Environment[J]. China Civil Engineering Journal, 2006, 39(2):36-41. (in Chinese)
[5] YANG Yi-ming, PENG Jian-xin, CAI Chun-sheng, et al. Improved Interval Evidence Theory-Based Fuzzy AHP Approach for Comprehensive Condition Assessment of Long-span PSC Box Girder Bridges[J]. Journal of Bridge Engineering, in submission.
[6] PENG Jian-xin, HU Shou-wang, ZHANG Jian-ren, et al. Experimental Study and Prediction Model of Corrosion-induced Crack Width in RC Structure[J]. Journal of Experimental Mechanics, 2014, 29(1):33-41. (in Chinese)
[7] VU K A, STEWART M G. Predicting the Likelihood and Extent of Reinforced Concrete Corrosion-induced Cracking[J]. Journal of Structural Engineering, 2005, 131(11):1681-1689.
[8] MULLARD J A, STEWART M G. Stochastic Assessment of the Timing and Efficiency of Maintenance for Corroding RC Structures[J]. Journal of Structural Engineering, 2009, 135(8):887-895.
[9] YANG Yi-ming, PENG Jian-xin, ZHANG Jian-ren, et al. A new method for estimating the scale of fluctuation in reliability assessment of reinforced concrete structures considering spatial variability[J]. Advances in Structural Engineering, 2018,21(13):1951-1962.
[10] PENG Jian-xin, YANG Yi-ming, CAI Chun-sheng, et al. A Probabilistic Analysis of Web Cracking for Long-span Prestressed Concrete Box Girder Bridges Subjected to Corrosion[J]. Advances in Structural Engineering, in submission.
[11] VANMARCKE E H. Probabilistic Modeling of Soil Profiles[J]. Journal of the Geotechnical Engineering Division, 1977, 103(11):1227-1245.
[12] KARIMI, R A. Probabilistic Assessment of Deterioration and Strength of Concrete Bridge Beams and Slabs[J]. Japanese Journal of Leprosy, 2002, 53(2):8-14.
[13] LI Y. Effect of Spatial Variability on Maintenance and Repair Decisions for Concrete Structures[D]. Delft:Delft University of technology, 2004.
[14] LI Y, VROUWENVELDER T, WIJNANTS G H, et al. Spatial Variability of Concrete Deterioration and Repair Strategies[J]. Structural Concrete, 2004, 5(3):121-129.
[15] KENSHEL O. Influence of Spatial Variability on Whole Life Management of Reinforced Concrete Bridges[D]. Dublin:Trinity College Dublin, 2009.
[16] O'CONNOR A, KENSHEL O. Experimental Evaluation of the Scale of Fluctuation for Spatial Variability Modeling of Chloride-induced Reinforced Concrete Corrosion[J]. Journal of Bridge Engineering, 2009, 18(1):3-14.
[17] STEWART M G. Spatial Variability of Pitting Corrosion and Its Influence on Structural Fragility and Reliability of RC Beams in Flexure[J]. Structural Safety, 2004, 26(4):453-470.
[18] VANMARCKE E. Random Fields:Analysis and Synthesis[M]. Cambridge:MIT Press, 1983.
[19] RAMACHANDRAN K, KARIMI A, ZINGONI A. Estimation of Corrosion Initiation Time with Observed Data[J]. Structural Engineering Mechanics and Computation, 2001(2):1431-1437.
[20] GOMES H M, AWRUCH A M. Reliability of Reinforced Concrete Structures Using Stochastic Finite Elements[J]. Engineering Computations, 2002, 19(7):764-786.
[21] PHOON K K, KULHAWY F H, GRIGORIU, M D. Development of a Reliability-Based Design Framework for Transmission Line Structure Foundations[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2003, 129(9):798-806.
[22] KIM H. Spatial Variability in Soils:Stiffness and Strength[D]. Atlanta:Georgia Institute of Technology, 2005.
[23] UZIELLI M, VANNUCCHI G, PHOON K K. Random Field Characterization of Stress-Normalised Cone Penetration Testing Parameters[J]. Geotechnique, 2005, 55(1):3-20.
[24] CLARK I, HARPER W V. Practical Geostatistics 2000(CD-ROM Version)[J]. Technometrics, 2012, 43(4):492.
[25] GOOVAERTS P. Geostatistics for Natural Resources Evaluation[D]. Oxford:Oxford University Press, 1997.
[26] KELKAR M, PEREZ G. Applied Geostatistics for Reservoir Characterization[D]. Richardson:Society for Petroleum Engineers, 2002.
[27] DEUTSCH C V. Geostatistical Reservoir Modeling[D]. Oxford:Oxford University Press, 2002.
[28] KRIGE D G. Two-dimensional Weighted Moving Averaging Trend Surfaces for Ore Evaluation[J]. South African Institute of Mining and Metallurgy, 1966,66:13-38.
[29] WACKERNAGEL H. Multivariate Geostatistics:An Introduction with Applications Seconded[D]. Berlin:Springer-Verlag, 1998.
[30] DURACRETE. Probabilistic Performance-based Durability Design of Concrete Structures-Statistical Quantification of the Variables in Limit State Functions[J]. The European Union-Brite Eu Ram Ш, Project BE, 2000, 95-1347:75-96.
[31] VAL D V, STEWART M G. Life-Cycle Cost Analysis of Reinforced Concrete Structures in Marine Environments[J]. Structural Safety, 2003, 25(4):343-362.