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A neural network based modelling and sensitivity analysis of Damage Ratio coefficient


Hadzima-Nyarko, Marijana; Nyarko, Emmanuel Karlo; Morić, Dragan
A neural network based modelling and sensitivity analysis of Damage Ratio coefficient // Expert systems with applications, 38 (2011), 10; 13405-13413 doi:10.1016/j.eswa.2011.04.169 (međunarodna recenzija, članak, znanstveni)


Naslov
A neural network based modelling and sensitivity analysis of Damage Ratio coefficient

Autori
Hadzima-Nyarko, Marijana ; Nyarko, Emmanuel Karlo ; Morić, Dragan

Izvornik
Expert systems with applications (0957-4174) 38 (2011), 10; 13405-13413

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
SDOF system; earthquake response; damage ratio; MLP neural network; sensitivity analysis

Sažetak
The level of structural damage after an earthquake can often be expressed using the Damage Ratio (DR) coefficient. This coefficient can be calculated using different formulas. A previously valorised new original formula for damage ratio derived for regular structures is implemented. This formula uses the structure response parameters of a single degree of freedom (SDOF) model. The structure response parameters of the SDOF model are obtained by analysing a large number of non-linear numeric structure responses using earthquakes of different intensities as load input. In this paper, a Multilayer Perceptron (MLP) neural network is used to model the relationship between the structure parameters (natural period, elastic base shear capacity, post-elastic stiffness and damping) of an SDOF model and the Damage Ratio (DR) coefficient. The influence of the individual structure parameters on the damage level of a structure is then determined by performing a sensitivity analysis procedure on the trained MLP neural network.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo



POVEZANOST RADA


Projekt / tema
149-1492966-2547 - Potencijal seizmičke oštetljivosti urbanih područja (Dragan Morić, )

Ustanove
Građevinski i arhitektonski fakultet Osijek,
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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