Skip to main content
SpringerLink
Log in

Robust attenuation relations for peak time-domain parameters of strong ground motions

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

This study presents new attenuation models for the estimation of peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD) using a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The PGA, PGV, and PGD were formulated in terms of earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms. A worldwide database of strong ground motions released by Pacific Earthquake Engineering Research Center (PEER) was employed to establish the models. A traditional genetic programming analysis was performed to benchmark the proposed models. For more validity verification, the GP/SA models were employed to predict the ground-motion parameters of the Iranian plateau earthquakes. Sensitivity and parametric analyses were carried out and discussed. The results show that the GP/SA attenuation models can offer precise and efficient solutions for the prediction of estimates of the peak time-domain characteristics of strong ground motions. The performance of the proposed models is better than or comparable with the attenuation relationships found in the literature.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Aarts E (1989) Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing. Wiley, New York

    Google Scholar 

  • Akgun A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6):1127–1143

    Google Scholar 

  • Alavi AH, Gandomi AH (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28(3):242–274

    Article  Google Scholar 

  • Alavi AH, Gandomi AH, Sahab MG, Gandomi M (2010) Multi expression programming: a new approach to formulation of soil classification. Eng Comput 26(2):111–118

    Article  Google Scholar 

  • Alavi AH, Ameri M, Gandomi AH, Mirzahosseini MR (2011) Formulation of flow number of asphalt mixes using a hybrid computational method. Constr Build Mater 25(3):1338–1355

    Article  Google Scholar 

  • Ambraseys NN, Simpson KA, Bommer JJ (1996) Prediction of horizontal response spectra in Europe. Earthq Eng Struct Dyn 25:371–400

    Article  Google Scholar 

  • ATC-63 (2008) Recommended methodology for quantification of building system performance and response parameters. 90% draft report. Applied Technology Council, Redwood City

    Google Scholar 

  • Boore DM, Atkinson GM (2007) Boore-Atkinson nga ground motion relations for the geometric mean horizontal component of peak and spectral ground motion parameters. PEER Report 2007/01, Pacific Engineering Research Center. University of California, Berkeley

    Google Scholar 

  • Boore DM, Joyner WB, Fumal TE (1997) Equations for estimating horizontal response spectra and peak acceleration from western North American earthquakes: a summary of recent work. Seismol Res Lett KNZ 68:128–153

    Article  Google Scholar 

  • Cabalar AF, Cevik A (2009) Genetic programming-based attenuation relationship: an application of recent earthquakes in Turkey. Comput Geosci 35:1884–1896

    Article  Google Scholar 

  • Campbell KW (1981) Near-source attenuation of peak horizontal acceleration. Bull Seismol Soc Am 71:2039–2070

    Google Scholar 

  • Campbell KW (2002) Strong-motion attenuation relations. In: Lee WHK, Kanamori H, Jennings PC, Kisslinger C (eds) International handbook of earthquake and engineering seismology, part B. Academic Press, Amsterdam, pp 1003–1012

    Google Scholar 

  • Campbell KW, Bozorgnia Y (2007) Campbell-Bozorgnia NGA ground motion relations for the geometric mean horizontal component of peak and spectral ground motion parameters. PEER Report 2007/02, Pacific Engineering Research Center. University of California, Berkeley

    Google Scholar 

  • Cerny V (1985) Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J Optim Theory Appl 45:41–52

    Article  Google Scholar 

  • Chu D, Kerh T, Wu CH (2003) Analysis of strong motion information at checking stations of Kaohsiung area by back-propagation neural networks. J Civil Eng Tech 7:45–62

    Google Scholar 

  • Conrads M, Dolezal O, Francone FD, Nordin P (2004) Discipulus-fast genetic programming based on AIM learning technology. Register Machine Learning Technologies Inc., Littleton

    Google Scholar 

  • Das SK, Biswal RK, Sivakugan N, Das B (2011) Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environ Earth Sci 64(1):201–210

    Article  Google Scholar 

  • Deschaine LM (2000) Using genetic programming to develop a C/C++ simulation model of a waste incinerator science applications. International Corp, Draft Technical Report

  • Deschaine LM, Zafran FA, Patel JJ, Amick D, Pettit R, Francone FD, Nordin P, Dilkes E, Fausett LV (2000) Solving the unsolved using machine learning, data mining and knowledge discovery to model a complex production process. In: Ades M (ed) Proceedings of advanced technology simulation conference, Washington

  • Douglas J (2003) Earthquake ground motion estimation using strong-motion records: a review of equations for the estimation of peak ground acceleration and response spectral ordinates. Earth Sci Rev 61:43–104

    Article  Google Scholar 

  • Douglas J (2004) Ground motion estimation equations 1964–2003. Research Report Number: 04-001-SM, Imperial College

  • Dunlop P, Smith S (2003) Estimating key characteristics of the concrete delivery and placement process using linear regression analysis. Civil Eng Environ Syst 20:273–290

    Article  Google Scholar 

  • Folino G, Pizzuti C, Spezzano G (2000) Genetic programming and simulated annealing: a hybrid method to evolve decision trees. In: Proceedings of EuroGP’2000, 1802, pp 294–303

  • Francone F (2004) Discipulus Lite™ Owner’s Manual, Version 4.0. Register Machine Learning Technologies

  • Frank IE, Todeschini R (1994) The data analysis handbook. Elsevier, Amsterdam

    Google Scholar 

  • Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227–5239

    Article  Google Scholar 

  • Gandomi AH, Sahab MG, Alavi AH, Heshmati AAR, Gandomi M, Arjmandi P (2008) Application of a coupled simulated annealing-genetic programming algorithm to the prediction of bolted joints behavior. Am Eur J Sci Res 3(2):153–162

    Google Scholar 

  • Gandomi AH, Alavi AH, Mirzahosseini R, Moghdas Nejad F (2011) Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J Mater Civil Eng ASCE 23(3):248–263

    Article  Google Scholar 

  • Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20(4):269–276

    Article  Google Scholar 

  • Güllü H, Erçelebi E (2007) A neural network approach for attenuation relationships: an application using strong-ground-motion data from Turkey. Eng Geol 93:65–81

    Article  Google Scholar 

  • Gunaydın K, Gunaydın A (2008) Peak ground acceleration prediction by artificial neural networks for Northwestern Turkey. Math Prob Eng ID 919420. doi:10.1155/2008/919420

  • Haselton CB, Deierlein GG (2007) Assessing seismic collapse safety of modern reinforced concrete frame. PEER Report 2007/08, Pacific Engineering Research Center. University of California, Berkeley

    Google Scholar 

  • Ingber L (1993) Simulated annealing: practice versus theory. Math Comput Model 8:29–57

    Article  Google Scholar 

  • Johari A, Habibagahi G, Ghahramani (2006) A prediction of soil–water characteristic curve using genetic programming. J Geotech Geoenviron ASCE 132(5):661–665

    Article  Google Scholar 

  • Kalkan E, Akbulut S, Tortum A, Celik S (2009) Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environ Geol 58(7):1429–1440

    Google Scholar 

  • Kerh T, Chu D (2002) Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion. Adv Eng Softw 33:733–742

    Article  Google Scholar 

  • Kerh T, Ting SB (2005) Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system. Eng Appl Artif Intell 8:857–866

    Article  Google Scholar 

  • Kermani E, Jafarian Y, Baziar MH (2009) New predictive models for the v max/a max ratio of strong ground motions using genetic programming. Int J Civil Eng 7:236–249

    Google Scholar 

  • Khademi MH (2002) Attenuation of peak and spectral accelerations in the Persian plateau. In: Proceedings of twelfth European conference on earthquake engineering, Paper ref 330

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimisation by simulated annealing. Science 220(4598):671–680

    Article  Google Scholar 

  • Koza J (1992) Genetic programming, on the programming of computers by means of natural selection. MIT Press, Cambridge

    Google Scholar 

  • Kramer SL (1996) Geotechnical earthquake engineering. Prentice-Hall, New Jersey

    Google Scholar 

  • Lee S, Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ Earth Sci 44(7):820–833

    Google Scholar 

  • Luco N, Cornell CA (2007) Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions. Earthq Spectra 23:357–392

    Article  Google Scholar 

  • Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing mechanics. J Chem Phys 21(6):1087–1092

    Article  Google Scholar 

  • Mirzaei N, Rezapour M, Siahkali Moradi A (2004) Attenuation relationships of seismic intensity in IRAN. J Earth Space Phys 30:1–10

    Google Scholar 

  • Mollahasani A, Alavi AH, Gandomi AH, Rashed A (2011) Nonlinear neural-based modeling of soil cohesion intercept. KSCE J Civil Eng 15(5):831–840

    Article  Google Scholar 

  • Mousavi SM, Gandomi AH, Alavi AH, Vesalimahmood M (2010) Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares. Struct Eng Mech 36(2):225–241

    Google Scholar 

  • Mousavi SM, Alavi AH, Gandomi AH, Mollahasani A (2011) A hybrid computational approach to formulate soil deformation moduli obtained from PLT. Eng Geol 123:324–332

    Article  Google Scholar 

  • Nowroozi AA (2005) Attenuation relations for peak horizontal and vertical accelerations of earthquake ground motion in Iran: a preliminary analysis. JSEE 7:109–128

    Google Scholar 

  • Poudyal CP, Chang C, Oh HJ, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61(5):1049–1064

    Article  Google Scholar 

  • Power MB, Chiou NA, Abrahamson N, Roblee C (2006) The next generation of ground motion attenuation models, NGA project: an overview. In: Proceedings of the eighth national conference on earthquake engineering, San Francisco, paper no. 2022 http://peer.berkeley.edu/nga/flatfile.html

  • Rajabi AM, Khamehchiyan M, Mahdavifar MR, Del Gaudio V (2010) Attenuation relation of Arias intensity for Zagros Mountains region (Iran). Soil Dyn Earthq Eng 30:110–118

    Article  Google Scholar 

  • Silva S (2007) GPLAB, a genetic programming toolbox for MATLAB, ITQB/UNL[M]. http://gplab.sourceforge.net

  • Smit P, Arzoumanian V, Javakhishvili Z, Arefiev S, Mayer-Rosa D, Balassanian S, Chelidze T (2000) The digital accelerograph network in the Caucasus. In: Proceedings of 2nd international conference on earthquake hazard and seismic risk reduction—advances in natural and technological hazards research, Kluwer Academic Publishers, Yerevan

  • Smith GN (1986) Probability and statistics in civil engineering. Collins, London

    Google Scholar 

  • Yilmaz I (2010a) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environ Earth Sci 60(3):505–519

    Article  Google Scholar 

  • Yilmaz I (2010b) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836

    Article  Google Scholar 

  • Zare M, Ghafory-Ashtiany M, Bard PY (1999) Attenuation law for the strong-motions in Iran. In: Proceedings of the third international conference on seismology and earthquake engineering, Tehran, pp 345–354

Download references

Acknowledgments

The authors are thankful to Professor Mohammad Ghasem Sahab for his support and stimulating discussions.

Author information

Authors and Affiliations

  1. Faculty of Entrepreneurship, University of Tehran, Tehran, Iran

    Ali Kafaei Mohammadnejad

  2. Department of Geography and Urban Planning, Faculty of Humanities and Social Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Seyyed Mohammad Mousavi

  3. Department of Transportation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mohammad Torabi

  4. Department of Civil Engineering, Faculty of Engineering, Arak University, Arak, Iran

    Mehdi Mousavi

  5. Young Researchers Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Amir Hossein Alavi

Authors
  1. Ali Kafaei Mohammadnejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyyed Mohammad Mousavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Torabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mehdi Mousavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amir Hossein Alavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Hossein Alavi.

Appendix: the optimum GP/SA programs

Appendix: the optimum GP/SA programs

The optimum GP/SA programs can be compiled in any C++ environment (see Tables 6, 7, and 8).

Table 6 The optimum GP/SA program for the PGA prediction
Full size table
Table 7 The optimum GP/SA program for the PGV prediction
Full size table
Table 8 The optimum GP/SA program for the PGD prediction
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kafaei Mohammadnejad, A., Mousavi, S.M., Torabi, M. et al. Robust attenuation relations for peak time-domain parameters of strong ground motions. Environ Earth Sci 67, 53–70 (2012). https://doi.org/10.1007/s12665-011-1479-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-011-1479-9

Keywords

Search

Navigation