Article
A Novel Method for Predicting Local Site Amplification Factors
Using 1-D Convolutional Neural Networks
Xiaomei Yang, Yongshan Chen, Shuai Teng and Gongfa Chen *
Citation: Yang, X.; Chen, Y.; Teng, S.;
Chen, G. A Novel Method for
Predicting Local Site Amplification
Factors Using 1-D Convolutional
Neural Networks. Appl. Sci. 2021, 11,
11650. https://doi.org/10.3390/
app112411650
Academic Editor: Nikos D. Lagaros
Received: 3 November 2021
Accepted: 3 December 2021
Published: 8 December 2021
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4.0/).
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;
xmyang@gdut.edu.cn (X.Y.); 2111909059@mail2.gdut.edu.cn (Y.C.); 1112009002@mail2.gdut.edu.cn (S.T.)
* Correspondence: gongfa.chen@gdut.edu.cn; Tel.: +86-136-6248-3527
Abstract:
The analysis of site seismic amplification characteristics is one of the important tasks
of seismic safety evaluation. Owing to the high computational cost and complex implementation
of numerical simulations, significant differences exist in the prediction of seismic ground motion
amplification in engineering problems. In this paper, a novel prediction method for the amplification
characteristics of local sites was proposed, using a state-of-the-art convolutional neural network
(CNN) combined with real-time seismic signals. The amplification factors were computed by the
standard spectral ratio method according to the observed records of seven stations in the Lower
Hutt Valley, New Zealand. Based on the geological exploration data from the seven stations and
the geological hazard information of the Lower Hutt Valley, eight parameters related to the seismic
information were presumed to influence the amplification characteristics of the local site. The CNN
method was used to establish the relationship between the amplification factors of local sites and
the eight parameters, and the training samples and testing samples were generated through the
observed and geological data other than the estimated values. To analyze the CNN prediction ability
for amplification factors on unrecorded domains, two CNN models were established for comparison.
One CNN model used about 80% of the data from 44 seismic events of the seven stations for training
and the remaining data for testing. The other CNN model used the data of six stations to train and
the remaining station’s data to test the CNN. The results showed that the CNN method based on the
observation data can provide a powerful tool for predicting the amplification factors of local sites
both for recorded positions and for unrecorded positions, while the traditional standard spectral
ratio method only predicts the amplification factors for recorded positions. The comparison of the
two CNN models showed that both can effectively predict the amplification factors of local ground
motion without records, and the accuracy and stability of predictions can meet the requirements.
With increasing seismic records, the CNN method becomes practical and effective for prediction
purposes in earthquake engineering.
Keywords:
amplification factor; ground motion; 1-D convolutional neural network; site amplification
1. Introduction
The seismic amplification effects in earthquake-prone areas need to be considered
in building or structure designs. The relationship between the site condition and seismic
ground motion has been researched for over one hundred years [
1
]. Pioneer researchers
gathered a great deal of observational evidence to establish this relationship in the earlier
studies [
2
]. Subsequently, many researchers [
3
–
10
] attempted to evaluate the amplification
characteristics of strong ground motions at a given site according to the acceleration records.
For unrecorded locations, it is common to rely on the regression relationship obtained
from the recorded results. This approach is regarded as reliable because the earthquake
records [
11
] include all the influences of the earthquake source, transmission path and
site features. However, for many local site amplification zones with no ground motion
records, a simple regression relationship based on a large-sized site and inadequate data
seems unreasonable.
Appl. Sci. 2021, 11, 11650. https://doi.org/10.3390/app112411650 https://www.mdpi.com/journal/applsci
