# Seismic Ground Response Prediction Based on Multilayer Perceptron
## 1 Introduction  
Earthquake disasters can cause enormous social and economic damage [1, 2], and therefore the sustainability of infrastructure requires the mitigation of earthquake consequences. Disaster mitigation should consider the seismic performance of structures in response to earthquakes. The bedrock motion may be remarkably amplified on the ground surface [3]. Geotechnical engineers have developed quantitative methods for analyzing the seismic ground response. Seismic ground response analysis refers to the evaluation of acceleration response on the surface based on the characteristics of the soil deposits and the bedrock motions.  
Deep learning based on artificial neural networks allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction [4]. Developments in deep learning have opened up new possibilities in the domain of civil engineering [5, 6, 7]. [PERSON] and [PERSON] [8] suggested a multilayer perceptron (MLP)-based damage classification scheme for a beam-type structure. [PERSON] and [PERSON] [9] used MLP to present a monitoring method for dam inspection, estimating the long-term deformation of the dam. The MLP model is given the information extracted from the water level and temperature distribution of the dam body. [PERSON] et al. [10] carried out active control of a base-isolated building with MLP. They approximated the nonlinear control law of the active controller and applied appropriate force from the controller to the building to reduce the vibration response using MLP. [PERSON] and [PERSON] [11] achieved damage detection, localization, and quantification for bridge structures by incorporatingBayesian inference with MLP. [PERSON] et al. [12] fit the load-deflection curve of a carbon fiber-reinforced polymer reinforced concrete slab using MLP. [PERSON] et al. [13] predicted the relative displacement and velocity time history by incorporating MLP and ordinary differential equation (ODE) solver, in which MLP was used to detect damage by modeling the soil-structure interaction. [PERSON] and [PERSON] [14] developed a convolution neural networks (CNN)-based model to estimate the structural dynamic response of a linear single-degree-of-freedom (SDOF) system, a nonlinear SDOF system, and a full-scale 3-story multidegree-of-freedom (MDOF) steel frame.  
This study proposes a multilayer perceptron (MLP) model to evaluate the seismic response of the surface based on the seismic motion at the bedrock (or 100 m level) and compares its performance with that of a conventional physical model. The acceleration histories at the surface and bedrock stored in the Japanese database Kiban Kyoshin Network (KiK-net) were used as datasets. A total of 6 sites were selected, and 100 earthquake events at each site were fabricated and used for the MLP model. The spectral accelerations were calculated from the predicted and measured (baseline) acceleration histories and compared. The seismic motion at the surface was evaluated based on the software for conventional seismic response analysis, SHAKE2000 [15], and its results were also compared with the results of the MLP model.