Skip to main content
Log in

Fuzzy rule based seismic risk assessment of one-story precast industrial buildings

  • Published:
Earthquake Engineering and Engineering Vibration Aims and scope Submit manuscript

Abstract

Efficient tools capable of using uncertain data to produce fast and approximate results are more practical in rapid decision-making applications when compared to conventional methods. From this point of view, this study introduces a risk assessment model for one-story precast industrial buildings by fuzzy logic which builds a bridge between uncertainty and precision. The input, output and relations of the fuzzy based risk assessment model (FBRAM) were determined by reference buildings. The Monte Carlo simulation method was used to handle uncertainties associated with the structural characteristics of the reference buildings. Section dimension, longitudinal reinforcement ratio, column height related to building elevation, confinement ratio and seismic hazard are regarded as input and the plastic demand ratio is considered as the output parameter by the mathematical formulation of strength and deformation capacity of the buildings. The supervised learning method was used to determine the membership function of fuzzy sets. Fuzzy rules of FBRAM were constructed from Monte Carlo simulation by mapping of inputs and output. FBRAM was evaluated by a group of simulated buildings and two existing precast industrial buildings. Comparisons have shown significant agreement with analytical model results in both cases. Consequently, it is anticipated that the proposed model can be used for the seismic risk mitigation of precast buildings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Alvanitopoulos PF, Andreadis I and Elenas A (2010), “Neuro-Fuzzy Techniques for the Classification of Earthquake Damage in Buildings,” Measurement, 43 (6): 797–809.

    Article  Google Scholar 

  • Amani J and Moeini R (2012), “Prediction of Shear Strength of Reinforced Concrete Beams Using Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network,” Scientia Iranica, 19 (2): 242–248.

    Article  Google Scholar 

  • ATC (1985), Earthquake Damage Evaluation Data for California, Applied Technology Council, ATC-13, Redwood City, C.A.

    Google Scholar 

  • ATC (1996), Seismic Evaluation and retrofit of Concrete Buildings, Applied Technology Council, ATC-40, Redwood City, C.A.

    Google Scholar 

  • Aydin K and Kisi O (2015), “Damage Detection in Structural Beam Elements Using Hybrid Neuro Fuzzy Systems,” Smart Structures and Systems, 16 (6): 1107–1132.

    Article  Google Scholar 

  • Babič A and Dolšek M (2016), “Seismic Fragility Functions of Industrial Precast Building Classes,” Engineering Structures, 118: 357–370.

    Article  Google Scholar 

  • Bachi IO Abdulrazzaq N and He Z (2014), “Neuro Fuzzy Model for Predicting the Dynamic Characteristics of Beams,” Acta Mechanica Solida Sinica, 27 (1): 85–96.

    Article  Google Scholar 

  • Benazouz C, Moussa L and Ali Z (2012), “Ductility and Inelastic Deformation Demands of Structures,” Structural Engineering and Mechanics, 42 (5): 631–644.

    Article  Google Scholar 

  • Bosiljkov V, Kržan M and D’Ayala D (2012), “Vulnerability Study of Urban and Rural Heritage Masonry in Slovenia through the Assessment of Local and Global Seismic Response of Buildings,” Proceedings of the 15th World Conference on Earthquake Engineering, Lisbon, Portugal.

  • Bracci JM, Reinhorn AM, Mander JB and Kunnath SK (1989), “Deterministic Model for Seismic Damage Evaluation of RC Structures,” Technical Report NCEER-89–0033, State University of New York, N.Y.

    Google Scholar 

  • CEN (2005), Design Provisions for Earthquake Resistance of Structures, Eurocode 8, Part 3, European Committee for Standardization, Brussels.

    Google Scholar 

  • Cevik A (2011), “Neuro-Fuzzy Modeling of Rotation Capacity of Wide Flange Beams,” Expert Systems with Applications, 38 (5): 5650–5661.

    Article  Google Scholar 

  • Chandrashekhar M and Ganguli R (2009a), “Uncertainty Handling in Structural Damage Detection Using Fuzzy Logic and Probabilistic Simulation,” Mechanical Systems and Signal Processing, 23 (2): 384–404.

    Article  Google Scholar 

  • Chandrashekhar M and Ganguli R (2009b), “Damage Assessment Of Structures with Uncertainty by Using Mode- Shape Curvatures and Fuzzy Logic,” Journal of Sound and Vibration, 326: 939–957.

    Article  Google Scholar 

  • Chopra A and Chintanapakdee C (2004), “Inelastic Deformation Ratios for Design and Evaluation of Structures: Single-Degree-of-Freedom Bilinear Systems,” J. Struct. Eng., https://doi.org/10.1061/(ASCE)0733-9445(2004)130:9(1309).

  • Colombo A and Negro P (2005), “A Damage Index of Generalised Applicability,” Engineering Structures, 27 (8): 1164–1174.

    Article  Google Scholar 

  • Cripstyani MP, Kristiawan SA and Purwanto E (2016), “Influence of Span-Length on Seismic Vulnerability of Reinforced Concrete Buildings Based on Their Fragility Curves,” in Kim D-K, Jung J and Seo S, editors, Advances in Civil, Architectural, Structural and Constructional Engineering, 289–293, London: Taylor and Francis Group.

    Chapter  Google Scholar 

  • Decanini L, Liberatore D, Liberatore L and Sorrentino L (2012), “Preliminary Report on the 2012, May 20th, Emilia Earthquake 1.0,” (https://doi.org/www.eqclearinghouse.org/2012-05-20-italy-it/), (Aug. 21, 2016).

  • Doran B, Yetilmezsoy K and Murtazaoglu S (2015), “Application of Fuzzy Logic Approach in Predicting the Lateral Confinement Coefficient for RC Columns Wrapped With CFRP,” Engineering Structures, 88 (1): 74–91.

    Article  Google Scholar 

  • Durucan C and Gümüs M (2018), “Direct Use of Peak Ground Motion Parameters for the Estimation of Inelastic Displacement Ratio of SDOF Systems Subjected to Repeated Far Fault Ground Motions,” Earthquake Engineering and Engineering Vibration, 17 (4): 771–785. https://doi.org/10.1007/s11803-018-0475-4

    Article  Google Scholar 

  • FEMA (2012), Multi-hazard Loss Estimation Methodology Earthquake Model, Federal Emergency Management Agency, Hazus®-MH 2.1, Washington.

    Google Scholar 

  • FEMA (1997), NEHRP Guidelines for the Seismic Rehabilitation of Buildings, Federal Emergency Management Agency, FEMAA-273, Washington.

    Google Scholar 

  • FEMA (2004), NEHRP Recommended Provisions and Commentary for Seismic Regulations for New Buildings and Other Structures, Federal Emergency Management Agency, FEMAA 450, Washington.

    Google Scholar 

  • FEMA (2016), Seismic Performance Assessment of Buildings Volume 1 — Methodology, Federal Emergency Management Agency, FEMA P-58, Washington.

    Google Scholar 

  • Garcia JR and Miranda E (2007), “Probabilistic Estimation of Maximum Inelastic Displacement Demands for Performance-Based Design,” Earthquake Engineering & Structural Dynamics, 36 (9): 1235–1254.

    Article  Google Scholar 

  • Iancu I (2012), “A Mamdani Type Fuzzy Logic Controller,” in Elmer D, editor, Fuzzy Logic — Controls, Concepts, Theories and Applications, Chapter 16, InTech.

  • Kramar M, Isakovic T and Fischinger M (2010), “Seismic Collapse Risk of Precast Industrial Buildings with Strong Connections,” Earthquake Engineering & Structural Dynamics, 39 (8): 847–868.

    Google Scholar 

  • Magliulo G, Ercolino M and Manfredi G (2015), “Influence of Cladding Panels on the First Period of One-Story Precast Buildings,” Bulletin of Earthquake Engineering, 13(5):1531–1555.

    Article  Google Scholar 

  • Mamdani E and Assilian S (1975), “An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller,” Int. J. Man Mach. Stud., 7: 1–13.

    Article  Google Scholar 

  • Mehanny S and Deierlein G. (2001), “Seismic Damage and CollapseAssessment of Composite Moment Frames,” Journal of Structural Engineering, 127 (9): 1045–1053, https://doi.org/10.1061/(ASCE)0733-9445(2001)127:9(1045).

    Article  Google Scholar 

  • Ozden S, Akpinar E, Erdogan H and Atalay HM (2014), “Performance of Precast Concrete Structures in October 2011 Van Earthquake, Turkey,” Magazine of Concrete Research, 66 (11): 543–552.

    Article  Google Scholar 

  • Ozkul S, Ayoub A and Altunkaynak A (2014), “Fuzzy-Logic Based Inelastic Displacement Ratios of Degrading RC Structures,” Engineering Structures, 75 (15): 590–603.

    Article  Google Scholar 

  • Palanci M (2010), Seismic Performance Estimation of Existing Industrial Precast Structures Based on Building Inventories, Department of Civil Engineering, Pamukkale University, Denizli MSc Dissertation (in Turkish)

    Google Scholar 

  • Palanci M and Senel SM (2013), “Rapid Seismic Performance Assessment Method for One Story Hinged Precast Buildings,” Structural Engineering and Mechanics, 48 (2): 257–274.

    Article  Google Scholar 

  • Palanci M, Senel SM and Kalkan A (2016), “Assessment of One-Story Existing Precast Industrial Buildings in Turkey Based on Fragility Curves,” Bull Earthquake Engineering, DOI: https://doi.org/10.1007/s10518-016-9956-x.

  • Park Y and Ang A (1985), “Mechanistic Seismic Damage Model for Reinforced Concrete,” J. Struct. Eng., https://doi.org/10.1061/(ASCE)0733-9445(1985)111:4(722).

  • Park Y, Ang A and Wen Y (1985), “Seismic Damage Analysis of Reinforced Concrete Buildings,” J. Struct. Eng., https://doi.org/10.1061/(ASCE)0733-9445(1985)111:4(740).

  • Pawar PM and Ganguli R (2003), “Genetic Fuzzy System for Damage Detection in Beams and Helicopter Rotor Blades,” Computer Methods in Applied Mechanics and Engineering, 192 (16–18): 2031–2057.

    Article  Google Scholar 

  • Porter KA (2016), “Beginner’s Guide to Fragility, Vulnerability, and Risk,” University of Colorado Boulder, (https://doi.org/spot.colorado.edu/∼porterka/Porter-beginnersguide.pdf/∼porterka/Porter-beginnersguide.pdf) (Aug. 21, 2016).

  • Priestley MJN, Seible F and Calvi GM (1996), Seismic Design and Retrofit of Bridges, Wiley, Canada.

    Book  Google Scholar 

  • Ross T (2010), Fuzzy Logic with Engineering Applications, John Wiley & Sons Ltd, N.Y.

    Book  Google Scholar 

  • Rossetto T, Ioannou I and Grant DN (2013), “Existing Empirical Fragility and Vulnerability Functions: Compendium and Guide for Selection,” GEM Technical Report 2013-X, GEM Foundation, Pavia, Italy.

    Google Scholar 

  • Sawyer JP and Rao SS (2000), “Structural Damage Detection and Identification Using Fuzzy Logic,” AIAA Journal, 38 (12): 2328–2335.

    Article  Google Scholar 

  • Sen Z (2010), “Rapid Visual Earthquake Hazard Evaluation of Existing Buildings by Fuzzy Logic Modeling,” Expert Systems with Applications, 37 (8): 5653–5660.

    Article  Google Scholar 

  • Sen Z (2011), “Supervised Fuzzy Logic Modeling for Building Earthquake Hazard Assessment,” Expert Systems with Applications, 38 (12): 14564–14573.

    Article  Google Scholar 

  • Senel SM and Palanci M (2011), “Structural Aspects and Seismic Performance of 1-Story Precast Buildings in Turkey,” J. Perform. Constr. Fac., https://doi.org/10.1061/(ASCE)CF.1943-5509.0000316.

  • Senel SM, Palanci M, Kalkan A and Yılmaz Y (2013), “Investigation of Shear and Overturning Safety of Hinged Connections in Existing Precast Buildings,” Teknik Dergi, 24 (4): 6505–6528. (in Turkish)

    Google Scholar 

  • Shiradhonka S and Sinha R (2012), “Detailed Evaluation of Available Seismic Damage Indices,” Proceedings of the Iset Golden Jubilee Symposium, Roorkee, India, Paper No. I003.

  • Sivanandam SN, Deepa SN and Sumathi S (2007), Introduction to Fuzzy Logic Using MATLAB, Springer, New York.

    Book  Google Scholar 

  • Sugeno M and Kang G (1988), “Structure Identification of Fuzzy Model,” Fuzzy Sets Syst., 28: 15–33.

    Article  Google Scholar 

  • Takagi T and Sugeno M (1985), “Fuzzy Identification of Systems And Its Applications to Modeling and Control,” IEEE Transactions on Systems, Man, and Cybernetics, 15: 116–132.

    Article  Google Scholar 

  • TEC (2007), Specification for Buildings to be Built in Seismic Zones, Turkish Earthquake Code, TEC-2007, Ankara.

    Google Scholar 

  • Zadeh LA (1965), “Fuzzy sets,” Inform Control, 8: 338–353.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mehmet Palanci.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Palanci, M. Fuzzy rule based seismic risk assessment of one-story precast industrial buildings. Earthq. Eng. Eng. Vib. 18, 631–648 (2019). https://doi.org/10.1007/s11803-019-0526-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11803-019-0526-5

Keywords

Navigation