| In order to speed up the implementation of the strategy for a country with strong transportation network and build a modern comprehensive transportation system,China has unveiled a plan outlining major targets for transportation network development in the14 th Five-Year Plan period(2021-2025).It puts forward nine key tasks,such as building a high-quality and comprehensive transport network and improving the safety and emergency support capability.As an important part of the transportation network in coastal areas,sea-crossing bridges play a very important role in regional economic development,and their construction need and safety concerns have increase significantly.The sea-crossing bridges will be continuously threatened by strong winds and huge waves in the sea environment,and there is a complex correlation between strong winds and huge waves.It is necessary to establish an accurate joint probability statistical model to describe the complex dependency structure between winds and wave and reasonably estimate its joint probability distribution,so as to provide reference for the design of seacrossing bridges.Moreover,large-scale sea-crossing bridges usually have the characteristics of long main span,tall and flexible tower,low stiffness structure,and small material damping,which makes the structural dynamic effect excited by strong wind and huge waves more obvious.In addition,the southeast coast and Taiwan Strait of China are traditional high-intensity seismic active areas,and the sea-crossing bridges located in such areas are undoubtedly facing the serous threat of strong offshore earthquakes during their life cycle.The combined action of strong wind,huge waves,strong offshore earthquake and other disasters is often the dominate factor leading to the vibration,damage and even destroy of sea-crossing bridge structures.Based on the above background,on the basis of collecting the measured coastal wind-wave data and the seismic records of offshore mainshock-aftershock sequences,aiming at the weaknesses of the existing probabilistic modeling methods for coastal windwave parameters(i.e.,wind speed,wave height,and wave period),the deficiencies of the prediction methods for offshore aftershock seismic intensity parameters,and the limitations of common structural vulnerability analysis methods,a method combining log-transform kernel density estimation and copula function is proposed,The research on the bivariate and trivariate joint probability analysis method of wind-wave parameters and the conditional probability model of offshore mainshock-aftershock intensity parameters were systematically carried out.Taking the sea-crossing cable-stayed bridge as the research object,the research on the extreme response characteristics of the bridge under the combined action of coastal wind-wave and earthquake and the theory and method of disaster-resistance performance evaluation were carried out.The main innovative works and key conclusions are as follows:1.In order to characterize the multimodal and heavy tail characteristics of measured coastal wind-wave data and describe the bivariate complex dependency structure between wind and wave parameters,nonparametric log-transformed kernel density estimation(log-TKDE)is employed for the first time to accurately fit the marginal distribution of wind and wave parameters,and a mixed copula function is constructed to reasonably estimate the bivariate joint probability distribution of wind and wave parameters.Compared with the commonly used tail extreme value distribution model and common single bivariate copula functions,as well as analyzing the return period and conditional probability of wind and wave parameters,it is shown that the bivariate joint probability analysis method integrating log-TKDE and mixed copula function can effectively improve the accuracy of statistical analysis of coastal wind and wave parameters.2.An empirical cumulative distribution function-Pareto(ECDF-Pareto)piecewise extreme value model is proposed to fit the marginal distribution of wind and wave parameters,and a trivariate joint probability distribution of wind and wave parameters estimated by a variety of trivariate mixed Copula Functions is constructed to accurately characterize the extreme value characteristics for coastal wind and wave parameters and reasonably describe their multivariate dependency structure.Compared with the commonly used extreme value distribution models and single trivariate copula functions,the rationality and effectiveness of the proposed ECDF-Pareto piecewise extreme value model and trivariate mixed copula function in describing the statistical characteristics and dependecny structures of wind and wave parameters are verified.Therefore,it can be used for the trivariate statistical analysis of coastal wind and wave parameters.3.To improve the reliability of wind speed,wave height,and wave period parameters required for the analysis and design of offshore structures,a semi-parametric joint probability model which can reasonably estimate the multivariate joint probability distribution of wind and wave parameters is constructed.Combined with the importance sampling technique which can be extended to any dimension,a method for constructing3-dimensional environmental contours of wind and wave parameters based on direct sampling method is proposed,Then,based on the constructed 3-dimensional environmental contours,the combination values of extreme wind and wave parameters related to the most unfavorable sea conditions in a specific return period are obtained,so as to analyze the extreme responses of sea-crossing bridges.4.On the basis of the combination values of relevant extreme wind and wave parameters,the extreme response characteristics of the bridge under the action of relevant extreme wind and wave are studied,and the probability evaluation of extreme response is carried out by using log-TKDE to accurately evaluate the probability characteristics of extreme response for sea-crossing cable-stayed bridge under the action of correlative extreme wind and wave.Furthermore,in order to reveal the influence degree of wind,wave,and earthquake on the extreme response of bridges and the coupling disaster causing mechanism,the research on the extreme response characteristics of bridges under the combined action of wind,wave,and earthquake is carried out,it shows that the bridge response under the action of correlative extreme wind and wave can reach the same level as that of 0.2g earthquake,and the correlative extreme wind and wave as well as earthquake can enhance or weaken the copuling effect of bridge dynamic responses.5.In view of the requirements for disaster-resistance performance evaluation of structural components and systems under multi-hazard scenarios,a structural vulnerability analysis method based on TKC(log-TKDE and Copula function)method is proposed.Taking a sea-crossing cable-stayed bridge as an example,the reliability and accuracy of the proposed TKC method are verified.Furthermore,the R-vine copula function-based system vulnerability analysis method is proposed.The comparison with the system vulnerability curve calculated by the first-order boundary method shows that the proposed system vulnerability analysis method can take the complex correlation between bridge components into account,and the analysis results are more accurate.6.In addition,in order to predict the intensity measure(IM)of aftershocks based on the IMs of offshore mainshocks,taking the seismic records of offshore mainshockaftershock sequences selected from Japan’s K-NNT seismic network as an example,logTKDE and a variety of bivariate Copula functions are combined to realistically describe the statistical characteristics and dependency structures of IMs for offshore mainshockaftershocks,and a copula-based conditional probability model of IMs for offshore mainshock-aftershocks is proposed.By comparing the conditional probability distribution with the marginal distribution of aftershock IM,it shows that given the IMs of the offshore main shock,the range of the aftershock IMs and the corresponding probability can be accurately estimated based on the conditional probability model. |
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