TY - GEN
T1 - Deep learning-based site amplification models for central and eastern north america
AU - Ilhan, Okan
AU - Harmon, Joseph A.
AU - Numanoglu, Ozgun A.
AU - Hashash, Youssef M.A.
N1 - Publisher Copyright:
© 2019 Associazione Geotecnica Italiana, Rome, Italy.
PY - 2019
Y1 - 2019
N2 - This paper presents deep learning-based site amplification models developed from large-scale simulated site amplification in Central and Eastern North America (CENA). The error evaluation of conventional simulation-based linear and nonlinear response spectrum (RS) and smoothed Fourier amplitude spectrum (FAS) amplification models highlights that fitting whole dataset to predetermined functional forms cannot capture the complex behavior inherent in the simulated amplification in CENA. Deep learning through Artificial Neural Network (ANN) is adopted for a new set of RS and FAS amplification models without the limitations of conventional regression models. This study shows significant improvements over conventional functions by use of ANN-based models: (i) the error in estimation is reduced up to 30% relative to conventional linear and total RS models, (ii) the simulated shallow site response is captured more accurately, and (iii) a continuous model for linear FAS amplification, previously provided as tabulated functions of VS30 and soil depth, is produced.
AB - This paper presents deep learning-based site amplification models developed from large-scale simulated site amplification in Central and Eastern North America (CENA). The error evaluation of conventional simulation-based linear and nonlinear response spectrum (RS) and smoothed Fourier amplitude spectrum (FAS) amplification models highlights that fitting whole dataset to predetermined functional forms cannot capture the complex behavior inherent in the simulated amplification in CENA. Deep learning through Artificial Neural Network (ANN) is adopted for a new set of RS and FAS amplification models without the limitations of conventional regression models. This study shows significant improvements over conventional functions by use of ANN-based models: (i) the error in estimation is reduced up to 30% relative to conventional linear and total RS models, (ii) the simulated shallow site response is captured more accurately, and (iii) a continuous model for linear FAS amplification, previously provided as tabulated functions of VS30 and soil depth, is produced.
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M3 - Conference contribution
AN - SCOPUS:85081169165
SN - 9780367143282
T3 - Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019
SP - 2980
EP - 2987
BT - Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019
A2 - Silvestri, Francesco
A2 - Moraci, Nicola
PB - CRC Press/Balkema
T2 - 7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019
Y2 - 17 January 2019 through 20 January 2019
ER -