[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120661-en":3,"doc-seo-120661-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120661,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","SITE AMPLIFICATION PREDICTION MODEL OF SHALLOW BEDROCK SITES - BASED ON MACHINE LEARNING MODELS","Prediction of site amplification is essential for accurate site-specific seismic hazard assessment. Traditional empirical and simulation-conditioned models rely on simplified proxies such as time-averaged shear-wave velocity (VS30) and site period (TG), yielding only approximate estimates. This study builds site amplification prediction models using random forest (RF) and deep neural networks (DNN) trained on simulation outputs from shallow bedrock profiles. Using response-spectrum matrix inputs and shear-wave velocity profiles, the ML models achieve higher accuracy for both linear and nonlinear amplification, producing reliable binned means and standard deviations, with DNN performing best.","1 SITE AMPLIFICATION PREDICTION MODEL OF SHALLOW BEDROCK SITES  \n2 BASED ON MACHINE LEARNING MODELS  \n3 Yong-Gook Leea, Sang-Jin Kimb, ZeinepAchmetc, Oh-Sung Kwond, Duhee Parka *, and Luigi  \n4 Di Sarnoe  \n5  \n6 ABSTRACT  \n7 Prediction of the site amplification is of primary importance for a site-specific seismic hazard  \n8 assessment. A large suite of both empirical and simulation-based site amplification models has  \n9 been proposed. Because they are conditioned on a few simplified site proxies including time- 10 averaged shear wave velocity up to a depth of 30 m ( VS30) and site period ( TG), they only  \n11 provide approximate estimates of the site amplification. In this study, site amplification  \n12 prediction models are developed using two machine learning algorithms, which are random  \n13 forest (RF) and deep neural network (DNN) . A comprehensive database of site response  \n14 analysis outputs obtained from simulations performed on shallow bedrock profiles is used.  \n15 Instead of simplified site proxies and ground motion intensity measures, matrix data which  \n16 include the response spectrum of the input ground motion and shear wave velocity profile. Both  \n17 machine learning based models provide exceptional prediction accuracies of both the linear  \n18 and nonlinear amplifications compared with the regression-based model, producing accurate  \n19 predictions of both binned mean and standard deviation of the site amplification. Among two  \n20 machine learning techniques, DNN-based model is revealed to produce better predictions.  \n21 Keywords: Machine learning, random forest, deep neural network, site amplification, site  \n22 response analysis.  \na  \n*  \nb  \nc  \nd  \ne  \nDepartment of Civil and Environmental Engineering, Hanyang University, Seoul, Korea Corresponding author. Email: [dpark@hanyang.ac.kr](dpark@hanyang.ac.kr) (D. Park).  \nInfrastructure Division, Infrastructure Engineering Group, Infrastructure Geotechnical Engineering Team, Hyundai Engineering and Construction Group, Seoul, Korea.  \nDepartment of Civil Engineering, National Technical University of Athens, Zografou, Greece. Department of Civil & Mineral Engineering, University of Toronto, Toronto, Canada.  \nDepartment of Civil Engineering and Industrial Design, University of Liverpool, Liverpool, UK  \n23 1. INTRODUCTION  \n24 The vertically propagating shear waves are generally amplified as they radiate upwards from  \n25 the bedrock through soil layers, which have relatively lower stiffness and density. This  \n26 phenomenon is referred to as the seismic site amplification. The prediction of site-specific  \n27 seismic amplification is critical for estimation of the design ground motion and seismic design  \n28 of various types of structures and facilities.  \n29 Regression-based site amplification models that are linked to ground motion models (GMMs)  \n30 have been developed from both recorded ground motions and numerical simulation outputs.  \n31 The models are based on site proxies, which include the time-averaged shear wave velocity of  \n32 top 30 m ( VS30), depth at which shear wave velocity ( VS) reaches 1 km/s or greater (Z 1), and  \n33 natural site period ( TG) . They are also conditioned on motion proxies including peak ground  \n34 acceleration (PGA) and spectral acceleration (SA) at selected periods. Although widely used  \n35 because of their ease of use, the regression-based site amplification models inevitably contain  \n36 large levels of uncertainty.  \n37 A number of studies proposed to use machine learning (ML) algorithms instead of regression  \n38 equations to develop site amplification models [1-7] . Kamatchi et al. [3] developed an artificial  \n39 neural network (ANN)-based methodology to predict site-specific acceleration response using  \n40 the outputs from one-dimensional (1D) equivalent linear (EQL) site response analyses  \n41 performed for a selected site in Delhi, India. The input parameters of the ANN model were the  \n42 moment magnitu","cbCaipIGbXDeFf8V","https://ap.wps.com/l/cbCaipIGbXDeFf8V","pdf",2065607,1,39,"English","en",105,"# Abstract\n# 1. Introduction\n## Seismic site amplification concept\n## Limitations of regression-based models\n## Prior machine learning studies\n## Related neural network and feature choices","[{\"question\":\"Why is site amplification prediction important in seismic hazard assessment?\",\"answer\":\"Site-specific seismic amplification controls the design ground motion and supports seismic design of structures and facilities, making prediction critical for hazard evaluation.\"},{\"question\":\"What is the main limitation of regression-based site amplification models?\",\"answer\":\"They depend on simplified site proxies such as VS30 and TG and motion proxies like PGA and spectral acceleration, which introduce substantial uncertainty and only approximate amplification estimates.\"},{\"question\":\"How do the proposed machine learning models improve prediction accuracy?\",\"answer\":\"They use simulation-derived databases and richer input representations, including the response spectrum of the input ground motion and the shear-wave velocity profile. RF and DNN outperform regression-based approaches for both linear and nonlinear amplification, with DNN showing better results.\"}]","SITE AMPLIFICATION PREDICTION MODEL OF SHALLOW BEDROCK SITES - BASED ON MACHINE LEARNING MODELS | PDF",1785731216,98,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"site-amplification-prediction-model-of-shallow-bedrock-sites-based-on-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/site-amplification-prediction-model-of-shallow-bedrock-sites-based-on-machine-learning-models/120661/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is site amplification prediction important in seismic hazard assessment?","Question",{"text":75,"@type":76},"Site-specific seismic amplification controls the design ground motion and supports seismic design of structures and facilities, making prediction critical for hazard evaluation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main limitation of regression-based site amplification models?",{"text":80,"@type":76},"They depend on simplified site proxies such as VS30 and TG and motion proxies like PGA and spectral acceleration, which introduce substantial uncertainty and only approximate amplification estimates.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the proposed machine learning models improve prediction accuracy?",{"text":84,"@type":76},"They use simulation-derived databases and richer input representations, including the response spectrum of the input ground motion and the shear-wave velocity profile. 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