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Probabilistic Prediction of Failure in Columns of a Steel Structure Under Progressive Collapse Using Response Surface and Artificial Neural Network Methods

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Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

Much attention has recently been paid to the issue of progressive collapse, which is associated with the uncertainties that may affect the accurate assessment of the safety of the structures. Probabilistic analysis can be used to quantify the probabilistic safety of structures under extreme loadings. Since the columns play a key role in the stability of the structures subjected to the progressive collapse and they are very prone to failure, this research focuses on estimation of the failure probability in these structural elements. Monte Carlo simulation is used to perform the probabilistic analysis in a steel structure. The ratio of the axial force demand to the inelastic buckling capacity in columns adjacent to the damaged column is considered as the implicit limit state function. Artificial neural network and response surface methods are used to estimate an explicit function to save computational time. The results obtained from this study can be used to rehabilitate damaged structures using the effective role of each random variable on the structural responses which have been determined by the sensitivity analysis.

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All data, models, and code generated or used during the study appear in the submitted article.

References

  • Abdollahzadeh G, Faghihmaleki H (2018) Proposal of a probabilistic assessment of structural collapse concomitantly subject to earthquake and gas explosion . Front Struct Civ Eng 12:425–437

    Article  Google Scholar 

  • Agarwal A, Varma AH (2014) Fire induced progressive collapse of steel building structures: the role of interior gravity columns. Eng Struct 58:129–140

    Article  Google Scholar 

  • ASCE (2007) Seismic rehabilitation of existing buildings. Reston, VA

  • Bartlett FM, Dexter RJ, Graeser MD, Jelinek JJ, Schmidt BJ, Galambos TV (2003) Updating standard shape material properties database for design and reliability. Eng J AISC 40:2–14

    Google Scholar 

  • Biagi VD, Kiakojouri F, Chiaia B, Sheidaii MR (2020) A Simplified method for assessing the response of rc frame structures to sudden column. Removal Appl Sci 10:3081

    Article  Google Scholar 

  • Cardoso JB, de Almeida JR, Dias JM, Coelho PG (2008) Structural reliability analysis using Monte Carlo simulation and neural networks. Adv Eng Softw 39:505–513

    Article  Google Scholar 

  • Chen CH, Zhu YF, Yao Y, Huang Y, Long X (2016) An evaluation method to predict progressive collapse resistance of steel frame structures. J Constr Steel Res 122:238–250

    Article  Google Scholar 

  • Chojaczyk A, Teixeira A, Neves LC, Cardoso J, Soares CG (2015) Review and application of artificial neural networks models in reliability analysis of steel structures. Struct Saf 52:78–89

    Article  Google Scholar 

  • Conrath EJ, Krauthammer T, Marchand K, Mlakar P (1999) structural design for physical security: state of the practice/task committee structural engineering institute, ASCE Reston

  • Deng J, Gu D, Li X, Yue ZQ (2005) Structural reliability analysis for implicit performance functions using artificial neural network. Struct Saf 27:25–48

    Article  Google Scholar 

  • Ditlevsen O, Madsen HO (1996) Structural reliability methods, vol 178. Wiley, New York

    Google Scholar 

  • Ellingwood B (1980) Development of a probability based load criterion for American National Standard A58: Building code requirements for minimum design loads in buildings and other structures, vol 13. National Bureau of Standards, US Department of Commerce

    Google Scholar 

  • Ellingwood BR, Dusenberry DO (2005) Building design for abnormal loads and progressive collapse Comput-Aided Civ Infrastruct Eng 20:194–205

    Article  Google Scholar 

  • Felipe TR, Haach VG, Beck AT (2018) Systematic reliability-based approach to progressive collapse ASCE-ASME . J Risk Uncertain Eng Syst Part A: Civil Eng 4:04018039

    Article  Google Scholar 

  • Freudenthal AM, Garrelts JM, Shinozuka M (1966) The analysis of structural safety. J Struct Div 92:235–246

    Article  Google Scholar 

  • Fu F (2013) Dynamic response and robustness of tall buildings under blast loading. J Constr Steel Res 80:299–307

    Article  Google Scholar 

  • Gerasimidis S (2014) Analytical assessment of steel frames progressive collapse vulnerability to corner column loss. J Constr Steel Res 95:1–9

    Article  Google Scholar 

  • Gerasimidis S, Deodatis G, Kontoroupi T, Ettouney M (2015) Loss-of-stability induced progressive collapse modes in 3D steel moment frames. Struct Infrastruct Eng 11:334–344

    Article  Google Scholar 

  • Goswami S, Ghosh S, Chakraborty S (2016) Reliability analysis of structures by iterative improved response surface method. Struct Saf 60:56–66

    Article  Google Scholar 

  • Haldar A, Mahadevan S (2000) Reliability assessment using stochastic finite element analysis. Wiley, New York

    Google Scholar 

  • Hariri-Ardebili MA, Seyed-Kolbadi SM, Noori M (2018) Response surface method for material uncertainty quantification of infrastructures Shock Vib 2018

  • Izzuddin BA, Pereira MF, Kuhlmann U, Rölle L, Vrouwenvelder T, Leira BJ (2012) Application of probabilistic robustness framework: risk assessment of multi-storey buildings under extreme loading. Struct Eng Int 22:79–85

    Article  Google Scholar 

  • Javidan MM, Kang H, Isobe D, Kim J (2018) Computationally efficient framework for probabilistic collapse analysis of structures under extreme actions. Eng Struct 172:440–452

    Article  Google Scholar 

  • Jiang X, Chen Y (2012) Progressive collapse analysis and safety assessment method for steel truss roof. J Perform Constr Facil 26:230–240

    Article  Google Scholar 

  • Jin J, El-Tawil S (2005) Evaluation of FEMA-350 seismic provisions for steel panel zones. J Struct Eng 131:250–258

    Article  Google Scholar 

  • Karimiyan S (2020) Collapse distribution scenario in seismic progressive collapse of rc buildings caused by internal column elimination IJST-T CIV ENG:1–12

  • Khandelwal K, El-Tawil S, Kunnath SK, Lew H (2008) Macromodel-based simulation of progressive collapse: steel frame structures. J Struct Eng 134:1070–1078

    Article  Google Scholar 

  • Kim J, An D (2009) Evaluation of progressive collapse potential of steel moment frames considering catenary action. Struct Des Tall Spec 18:455–465

    Article  Google Scholar 

  • Kirçil MS, Polat Z (2006) Fragility analysis of mid-rise R/C frame buildings. Eng Struct 28:1335–1345

    Article  Google Scholar 

  • Lagaros ND, Tsompanakis Y, Psarropoulos PN, Georgopoulos EC (2009) Computationally efficient seismic fragility analysis of geostructures. Comput Struct 87:1195–1203

    Article  Google Scholar 

  • Li Y, Lu X, Guan H, Ren P, Qian L (2016) Probability-based progressive collapse-resistant assessment for reinforced concrete frame structures Adv. Struct Eng 19:1723–1735

    Google Scholar 

MATLAB ( 2016).

  • Mazzoni S, McKenna F, Scott MH, Fenves GL (2006) OpenSees command language manual Pacific Earthquake Engineering Research (PEER) Center 264

  • Mehdizadeh K, Karamodin A, Sadeghi A (2020) Progressive Sidesway Collapse Analysis of Steel Moment-Resisting Frames Under Earthquake Excitations IJST-T CIV ENG:1–13

  • Moradi M, Tavakoli H, AbdollahZade G (2020) Sensitivity analysis of the failure time of reinforcement concrete frame under postearthquake fire loading. Struct Concr 21:625–641

    Article  Google Scholar 

  • Moradi M, Tavakoli H, Abdollahzadeh G (2019) Probabilistic assessment of failure time in steel frame subjected to fire load under progressive collapses scenario. Eng Failure Anal 102:136–147

    Article  Google Scholar 

  • Nica GB, Lupoae M, Pavel F, Baciu C (2018) Numerical analysis of RC column failure due to blast and collapse scenarios for an irregular RC-framed structure . Int J Civ Eng 16:1125–1136

    Article  Google Scholar 

  • Pantidis P, Gerasimidis S (2017) New Euler-type progressive collapse curves for steel moment-resisting frames: Analytical method. J Struct Eng 143:04017113

    Article  Google Scholar 

  • Park J, Kim J (2010) Fragility analysis of steel moment frames with various seismic connections subjected to sudden loss of a column. Eng Struct 32:1547–1555

    Article  Google Scholar 

  • Pioldi F, Ferrari R, Rizzi E (2017) Seismic FDD modal identification and monitoring of building properties from real strong-motion structural response signals. Struct Control Health Monit 24:e1982

    Article  Google Scholar 

  • Rajashekhar MR, Ellingwood BR (1993) A new look at the response surface approach for reliability analysis. Struct Saf 12:205–220

    Article  Google Scholar 

  • Sadek F, Main JA, Lew HS, Robert SD, Chiarito VP, El-Tawil S (2010) An experimental and computational study of steel moment connections under a column removal scenario NIST Technical Note 1669

  • Santosh T, Saraf R, Ghosh A, Kushwaha H (2006) Optimum step length selection rule in modified HL–RF method for structural reliability. Int J Pres Ves Pip 83:742–748

    Article  Google Scholar 

  • Sheela KG, Deepa SN (2013) Review on methods to fix number of hidden neurons in neural networks Mathematical Problems in Engineering 2013

  • Šipoš TK, Sigmund V, Hadzima-Nyarko M (2013) Earthquake performance of infilled frames using neural networks and experimental database. Eng Struct 51:113–127

    Article  Google Scholar 

  • Tavakoli H, Afrapoli MM (2018) Robustness analysis of steel structures with various lateral load resisting systems under the seismic progressive collapse. Eng Fail Anal 83:88–101

    Article  Google Scholar 

  • Tavakoli H, Alashti AR (2013) Evaluation of progressive collapse potential of multi-story moment resisting steel frame buildings under lateral loading . Sci Iran 20:77–86

    Google Scholar 

  • Tavakoli H, Kiakojouri F (2014) Progressive collapse of framed structures: Suggestions for robustness assessment . Sci Iran 21:329–338

    Google Scholar 

  • Tavakoli H, Kiakojouri F (2015) Threat-independent column removal and fire-induced progressive collapse: Numerical study and comparison. Civ Eng Infrastruct 48:121–131

    Google Scholar 

  • Tavakoli HR, Afrapoli MM (2014) The effect of retrofitting types on behavior of steel frames subjected to progressive collapse under seismic load

  • Tavakoli HR, Hasani AH (2017) Effect of Earthquake characteristics on seismic progressive collapse potential in steel moment resisting frame. Earthq Struct 12:529–541

    Google Scholar 

  • Tavakoli HR, Naghavi F, Goltabar AR (2015) Effect of base isolation systems on increasing the resistance of structures subjected to progressive collapse. Earthquakes Struct 9:639–656

    Article  Google Scholar 

  • Waszczyszyn Z (1999) Neural networks in the analysis and design of structures. Springer, Berlin

    Book  Google Scholar 

  • Xia P-Q, Brownjohn JM (2004) Bridge structural condition assessment using systematically validated finite-element model. J Bridge Eng 9:418–423

    Article  Google Scholar 

  • Yu XH, Lu DG, Qian K, Li B (2016) Uncertainty and sensitivity analysis of reinforced concrete frame structures subjected to column loss. J Perform Constr Facil 31:04016069

    Article  Google Scholar 

Download references

Acknowledgement

The work presented in this paper was supported by Babol Noshirvani University of Technology through Grant No. BUT/388011/99.

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Authors and Affiliations

  1. Department of Earthquake Engineering, Babol Noshirvani University of Technology, Babol, Iran

    Fahime Naghavi & Hamid Reza Tavakoli

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  1. Fahime Naghavi
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  2. Hamid Reza Tavakoli
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Correspondence to Hamid Reza Tavakoli.

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Naghavi, F., Tavakoli, H.R. Probabilistic Prediction of Failure in Columns of a Steel Structure Under Progressive Collapse Using Response Surface and Artificial Neural Network Methods. Iran J Sci Technol Trans Civ Eng 46, 801–817 (2022). https://doi.org/10.1007/s40996-021-00593-z

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  • DOI: https://doi.org/10.1007/s40996-021-00593-z

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