Please wait a minute...
Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

Postal Subscription Code 80-968

2018 Impact Factor: 1.272

Front. Struct. Civ. Eng.    2022, Vol. 16 Issue (4) : 434-447    https://doi.org/10.1007/s11709-022-0820-6
RESEARCH ARTICLE
Applying the spectral stochastic finite element method in multiple-random field RC structures
Abbas YAZDANI()
Civil Engineering Department, University of Sistan and Baluchestan, Zahedan 98167-45845, Iran
 Download: PDF(7386 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

This paper uses the spectral stochastic finite element method (SSFEM) for analyzing reinforced concrete (RC) beam/slab problems. In doing so, it presents a new framework to study how the correlation length of a random field (RF) with uncertain parameters will affect modeling uncertainties and reliability evaluations. It considers: 1) different correlation lengths for uncertainty parameters, and 2) dead and live loads as well as the elasticity moduli of concrete and steel as a multi-dimensional RF in concrete structures. To show the SSFEM’s efficiency in the study of concrete structures and to evaluate the sensitivity of the correlation length effects in evaluating the reliability, two examples of RC beams and slabs have been investigated. According to the results, the RF correlation length is effective in modeling uncertainties and evaluating reliabilities; the longer the correlation length, the greater the dispersion range of the structure response and the higher the failure probability.

Keywords uncertainty      spectral stochastic finite element method      correlation length      reliability assessment      reinforced concrete beam/slab     
Corresponding Author(s): Abbas YAZDANI   
Just Accepted Date: 15 April 2022   Online First Date: 04 July 2022    Issue Date: 09 August 2022
 Cite this article:   
Abbas YAZDANI. Applying the spectral stochastic finite element method in multiple-random field RC structures[J]. Front. Struct. Civ. Eng., 2022, 16(4): 434-447.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0820-6
https://academic.hep.com.cn/fsce/EN/Y2022/V16/I4/434
Fig.1  A schematic view of the loading and components of a concrete structure.
Fig.2  A 2D FE model and concrete beam loading.
Fig.3  Four modes of combining square elements of concrete beams with rebar bar elements.
Fig.4  A 2D FE model of a concrete slab and its loading.
Fig.5  Four modes of combining square elements of concrete slabs with rebar beam elements.
RF dimensionRF domain (m)b (m)σμRF
2D[4.1 0.4][4.1 0.4]2.1×103 (MPa)2.1×104 (MPa)Ec
1D[*][*] a)2×104 (MPa)2×105 (MPa)Es
1D[4.1][Var] b)5.7 (kN/m)28.5 (kN/m)DL
1D[4.1][Var]2.7 (kN/m)13.5 (kN/m)LL
Tab.1  Statistical features of RFs for the uncertainty of concrete beam parameters
Fig.6  Geometric features of the concrete beam: (a) The 3D model with equivalent rebars; (b) meshing.
Fig.7  A 10-time greater deformation and mean displacements of the RC beam’s nodal points.
Fig.8  PDF diagram of the maximum RC beam deflection for different bDL values and bLL values.
Fig.9  PDF diagram of the maximum RC beam deflection for a bDL = 4.1 m and different bLL values.
Fig.10  CDF diagram of the maximum RC beam deflection for different bDL values and bLL values.
Fig.11  CDF diagram of the maximum RC beam deflection for a bDL = 4.1 m and different bLL values.
Fig.12  The surface of Pf variations of the RC beam for different bDL values and bLL values.
Fig.13  The RC beam’s Pf variations diagram for bDL = 4.1 m and different bLL values.
Fig.14  Failure probability coefficient of variation for RC beam.
RF dimensionRF domain (m)b (m)σμRF
2D[4.1 3.2][4.1 3.2]2.1×103 (MPa)2.1×104 (MPa)Ec
1D[*][*] a)2×104 (MPa)2×105 (MPa)Es
2D[4.1][Var] b)1.2 (kN/m2)6 (kN/m2)DL
2D[4.1][Var]0.6 (kN/m2)3 (kN/m2)LL
Tab.2  Statistical features of the RF related to the concrete slab’s uncertain parameters
Fig.15  Concrete slab’s geometric features: (a) 3D model with equivalent rebars; (b) meshing.
Fig.16  Deformation and mean deflection of the RC slabs’ nodal points.
Fig.17  Deformation and mean x-direction rotation of the RC slabs’ nodal points.
Fig.18  Deformation and mean y-direction rotation of the RC slabs’ nodal points.
Fig.19  PDF diagram of the maximum RC slab deflection for different bDL and bLL values.
Fig.20  PDF diagram of the maximum RC slab deflection for a bDL = 4.1 m and different bLLs.
Fig.21  CDF diagram of the maximum RC slab deflection for different bDL and bLL values.
Fig.22  CDF diagram of the maximum RC slab deflection for a bDL = 4.1 m and different bLL values.
Fig.23  The surface of Pf variations of the RC slab for different bDL values and bLL values.
Fig.24  The RC slab’s Pf variations diagram for bDL = 4.1 m and different bLL values.
Fig.25  Failure probability coefficient of variation for RC slab.
1 A Der Kiureghian, T Haukaas, K Fujimura. Structural reliability software at the University of California, Berkeley. Structural Safety, 2006, 28( 1−2): 44–67
2 A Der Kiureghian. Analysis of structural reliability under parameter uncertainties. Probabilistic Engineering Mechanics, 2008, 23( 4): 351–358
https://doi.org/10.1016/j.probengmech.2007.10.011
3 I Depina, T M H Le, G Fenton, G Eiksund. Reliability analysis with metamodel line sampling. Structural Safety, 2016, 60 : 1–15
https://doi.org/10.1016/j.strusafe.2015.12.005
4 S Sakata, K Okuda, K Ikeda. Stochastic analysis of laminated composite plate considering stochastic homogenization problem. Frontiers of Structural and Civil Engineering, 2015, 9( 2): 141–153
https://doi.org/10.1007/s11709-014-0286-2
5 N Soltani, M Alembagheri, M H Khaneghahi. Risk-based probabilistic thermal-stress analysis of concrete arch dams. Frontiers of Structural and Civil Engineering, 2019, 13( 5): 1007–1019
https://doi.org/10.1007/s11709-019-0521-y
6 A Ghavidel, M Rashki, H Ghohani Arab, M Azhdary Moghaddam. Reliability mesh convergence analysis by introducing expanded control variates. Frontiers of Structural and Civil Engineering, 2020, 14( 4): 1012–1023
https://doi.org/10.1007/s11709-020-0631-6
7 M Rashki, A Ghavidel, H Ghohani Arab, S R Mousavi. Low-cost finite element method-based reliability analysis using adjusted control variate technique. Structural Safety, 2018, 75 : 133–142
https://doi.org/10.1016/j.strusafe.2017.11.005
8 K Song, Y Zhang, X Zhuang, X Yu, B Song. Reliability-based design optimization using adaptive surrogate model and importance sampling-based modified SORA method. Engineering with Computers, 2021, 37( 2): 1295–1314
9 I Papaioannou, D Straub. Combination line sampling for structural reliability analysis. Structural Safety, 2021, 88 : 102025
https://doi.org/10.1016/j.strusafe.2020.102025
10 V Papadopoulos, D G Giovanis. Stochastic Finite Element Methods: An Introduction. Cham: Springer, 2018, 47–70
11 R G Ghanem, P D Spanos. Stochastic Finite Elements: A Spectral Approach. New York: Springer, 1991, 101–119
12 H R Bae, E E Forster. Improved Neumann expansion method for stochastic finite element analysis. Journal of Aircraft, 2017, 54( 3): 967–979
https://doi.org/10.2514/1.C033883
13 A Sofi, E Romeo. A unified response surface framework for the interval and stochastic finite element analysis of structures with uncertain parameters. Probabilistic Engineering Mechanics, 2018, 54 : 25–36
https://doi.org/10.1016/j.probengmech.2017.06.004
14 F Wu, L Y Yao, M Hu, Z C He. A stochastic perturbation edge-based smoothed finite element method for the analysis of uncertain structural-acoustics problems with random variables. Engineering Analysis with Boundary Elements, 2017, 80 : 116–126
https://doi.org/10.1016/j.enganabound.2017.03.008
15 V Papadopoulos, I Kalogeris, D G Giovanis. A spectral stochastic formulation for nonlinear framed structures. Probabilistic Engineering Mechanics, 2019, 55 : 90–101
https://doi.org/10.1016/j.probengmech.2018.11.002
16 N Z Chen, C G Soares. Spectral stochastic finite element analysis for laminated composite plates. Computer Methods in Applied Mechanics and Engineering, 2008, 197( 51-52): 4830–4839
17 G Stefanou, M Papadrakakis. Stochastic finite element analysis of shells with combined random material and geometric properties. Computer Methods in Applied Mechanics and Engineering, 2004, 193( 1−2): 139–160
18 G Kandler, J Füssl, J Eberhardsteiner. Stochastic finite element approaches for wood-based products: theoretical framework and review of methods. Wood Science and Technology, 2015, 49( 5): 1055–1097
https://doi.org/10.1007/s00226-015-0737-5
19 K Li, D Wu, W Gao. Spectral stochastic isogeometric analysis for static response of FGM plate with material uncertainty. Thin-walled Structures, 2018, 132 : 504–521
https://doi.org/10.1016/j.tws.2018.08.028
20 X Y Zhou, P D Gosling, Z Ullah, L Kaczmarczyk, C J Pearce. Stochastic multi-scale finite element based reliability analysis for laminated composite structures. Applied Mathematical Modelling, 2017, 45 : 457–473
https://doi.org/10.1016/j.apm.2016.12.005
21 B SudretA Der Kiureghian. Stochastic Finite Element Methods and Reliability: A State-of-the-Art Report. Berkeley, CA: University of California, 2000
22 A YazdaniH G ArabM Rashki. Simplified spectral stochastic finite element formulations for uncertainty quantification of engineering structures. Structures, 2020, 28: 1924–1945
23 F N Schietzold, A Schmidt, M M Dannert, A Fau, R M Fleury, W Graf, M Kaliske, C Könke, T Lahmer, U Nackenhorst. Development of fuzzy probability based random fields for the numerical structural design. GAMM-Mitteilungen, 2019, 42( 1): e201900004
24 A Schmidt, C Henning, S Herbrandt, C Könke, K Ickstadt, T Ricken, T Lahmer. Numerical studies of earth structure assessment via the theory of porous media using fuzzy probability based random field material descriptions. GAMM-Mitteilungen, 2019, 42( 1): e201900007
25 P Zakian, N Khaji. A stochastic spectral finite element method for wave propagation analyses with medium uncertainties. Applied Mathematical Modelling, 2018, 63 : 84–108
https://doi.org/10.1016/j.apm.2018.06.027
26 N Wiener. The homogeneous chaos. American Journal of Mathematics, 1938, 60( 4): 897–936
https://doi.org/10.2307/2371268
27 T R ChandrupatlaA D Belegundu. Introduction to Finite Elements in Engineering. Upper Saddle River, NJ: Prentice Hall, 2002
28 N A Nariman, K Hamdia, A M Ramadan, H Sadaghian. Optimum design of flexural strength and stiffness for reinforced concrete beams using machine learning. Applied Sciences, 2021, 11( 18): 8762
29 S P TimoshenkoS Woinowsky-Krieger. Theory of Plates and Shells. New York: McGraw-hill, 1959
30 N Vu-Bac, T X Duong, T Lahmer, X Zhuang, R A Sauer, H S Park, T Rabczuk. A NURBS-based inverse analysis for reconstruction of nonlinear deformations of thin shell structures. Computer Methods in Applied Mechanics and Engineering, 2018, 331 : 427–455
https://doi.org/10.1016/j.cma.2017.09.034
31 N Vu-Bac, T X Duong, T Lahmer, P Areias, R A Sauer, H S Park, T Rabczuk. A NURBS-based inverse analysis of thermal expansion induced morphing of thin shells. Computer Methods in Applied Mechanics and Engineering, 2019, 350 : 480–510
https://doi.org/10.1016/j.cma.2019.03.011
32 318-08 ACI. Building Code Requirements for Structural Concrete and Commentary. Farmington Hills: American Concrete Institute, 2008
33 N Vu-Bac, M Silani, T Lahmer, X Zhuang, T Rabczuk. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96 : 520–535
https://doi.org/10.1016/j.commatsci.2014.04.066
34 N Vu-Bac, T Lahmer, X Zhuang, T Nguyen-Thoi, T Rabczuk. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100 : 19–31
https://doi.org/10.1016/j.advengsoft.2016.06.005
35 N Vu-Bac, X Zhuang, T Rabczuk. Uncertainty quantification for mechanical properties of polyethylene based on fully atomistic model. Materials, 2019, 12( 21): 3613
36 B Liu, N Vu-Bac, X Zhuang, T Rabczuk. Stochastic multiscale modeling of heat conductivity of polymeric clay nanocomposites. Mechanics of Materials, 2020, 142 : 103280
https://doi.org/10.1016/j.mechmat.2019.103280
37 B Liu, N Vu-Bac, T Rabczuk. A stochastic multiscale method for the prediction of the thermal conductivity of polymer nanocomposites through hybrid machine learning algorithms. Composite Structures, 2021, 273 : 114269
https://doi.org/10.1016/j.compstruct.2021.114269
38 D M Frangopol. Probability concepts in engineering: Emphasis on applications to civil and environmental engineering. Structure and Infrastructure Engineering, 2008, 4(5): 413–414
[1] Ahmad SHARAFATI, Masoud HAGHBIN, Mohammadamin TORABI, Zaher Mundher YASEEN. Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties[J]. Front. Struct. Civ. Eng., 2021, 15(3): 665-681.
[2] Farhoud KALATEH, Farideh HOSSEINEJAD. Uncertainty assessment in hydro-mechanical-coupled analysis of saturated porous medium applying fuzzy finite element method[J]. Front. Struct. Civ. Eng., 2020, 14(2): 387-410.
[3] Hai-Bin MA, Wei-Dong ZHUO, Davide LAVORATO, Camillo NUTI, Gabriele FIORENTINO, Giuseppe Carlo MARANO, Rita GRECO, Bruno BRISEGHELLA. Probabilistic seismic response and uncertainty analysis of continuous bridges under near-fault ground motions[J]. Front. Struct. Civ. Eng., 2019, 13(6): 1510-1519.
[4] Fengjie TAN, Tom LAHMER. Shape design of arch dams under load uncertainties with robust optimization[J]. Front. Struct. Civ. Eng., 2019, 13(4): 852-862.
[5] Arash SEKHAVATIAN, Asskar Janalizadeh CHOOBBASTI. Application of random set method in a deep excavation: based on a case study in Tehran cemented alluvium[J]. Front. Struct. Civ. Eng., 2019, 13(1): 66-80.
[6] Ahmad IDRIS, Indra Sati Hamonangan HARAHAP, Montasir Osman Ahmed ALI. Jack up reliability analysis: An overview[J]. Front. Struct. Civ. Eng., 2018, 12(4): 504-514.
[7] Chu MAI, Katerina KONAKLI, Bruno SUDRET. Seismic fragility curves for structures using non-parametric representations[J]. Front. Struct. Civ. Eng., 2017, 11(2): 169-186.
[8] Prishati RAYCHOWDHURY,Sumit JINDAL. Shallow foundation response variability due to soil and model parameter uncertainty[J]. Front. Struct. Civ. Eng., 2014, 8(3): 237-251.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed