| Earthquakes in subduction zones,where huge amounts of energy are accumulated as a result of plate compression,tend to be of high magnitude and devastating.With the continuous development of China’s marine areas and the increasing number of offshore transportation construction projects and marine projects,seismic research in subduction zone areas has gradually become a research focus.Current studies on the seismic characteristics of subduction zone regions are mostly based on the results of 5% damping ratios.However,the damping ratio of steel buildings,which are widely used in marine engineering,is lower than 5%;the damping ratio of space grid structures,long cantilever structures and large-span composite floor slabs is likewise lower than 5%;the damping ratio of buildings fitted with seismic energy-consuming elements is higher than 5%.However,the damping ratio of steel structures widely used in marine engineering is less than 5%;the damping ratio of space grid structures,long cantilever structures and large span composite floor slabs is also less than 5%;the damping ratio of buildings with seismic energy dissipation elements is higher than 5%,which shows that the design spectrum of 5% damping ratio cannot meet the complex and diverse design requirements.The use of the damping modification factor(DMF)method to overcome the limitations of the 5% damping ratio design spectrum is therefore a common method currently used in seismic design.In the past,studies on the relationship between ground vibration attenuation and damping correction coefficients were mainly in the horizontal direction,and the response spectrum ratio was adopted for vertical ground vibration,which was set at 2/3 of the horizontal ground vibration.The DMF model is needed to ensure that structures with weak vertical seismic capacity are adequately resilient to vertical seismic effects.In this study,a DMF model of the vertical acceleration spectrum was developed for the subduction interface earthquakes classified in the subduction zone based on the location of the source and the attenuation relationship.The seismic data used in the study were obtained from the website of the Japan Institute of Disaster Prevention Science and Technology,and a total of 3552 records from 76 interface seismic events were used to build the DMF model.In this study,the modeling steps are divided into three steps.In the first step,the acquired seismic data are filtered and filtered to construct a seismic data set for this study,and the site period is used as the basis for site classification,and the seismic data are divided into four groups according to four types of sites;in the second step,the DMF geometric mean of the four actual data groups is calculated,and the damping ratio and spectral period are used as parameters to fit the four geometric mean groups separately by regression In the third step,based on the conclusions of the analysis of the standard deviation of the model residuals in the second step,the final DMF model with seismic parameters is constructed by introducing the magnitude term,the source depth term,the source distance term and other parameters into the simple parameter model.These models can be used to adjust the design response spectrum for specified seismic conditions.From the above studies,this thesis draws the following conclusions:(1)Site type and earthquake type are factors that must be considered in the process of constructing a DMF model.(2)A good fit to the actual DMF values can be achieved by using a cubic function form of the model,which can avoid the excessive model error caused by the quadratic function form and the problem of complex parameters that are difficult to fit by using a quadratic function form.(3)The same type of horizontal DMF model and vertical DMF model is significantly different from each other,cannot be achieved by a fixed parameter to convert between the two,the horizontal and vertical direction is necessary to separate the study.(4)The inclusion of magnitude,depth of source and distance from the source in the DMF model can significantly improve the accuracy of the model,and the use of iteration by iteration can avoid cumulative errors in the regression of these coefficients. |