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Precast segmental bridge columns (PSBC) have been gradually used in various bridge engineering for rapid construction. But current applications are mainly limited to low seismic areas due to the lack of energy dissipation capacity. In this study, the external self-centering energy dissipation device (SCEDD) was applied to improve the seismic capacity of PSBC. The refined solid finite element model was established and tested based on ABAQUS platform. The effects of four parameters of SCEDD, i.e., connection mode, nominal yield force, stiffness of disc springs and whether to install SCEDD at the bottom of the pier, were systematically discussed by quasi-static analysis. The results show that, the ductility of the PSBC can be improved by using hinged SCEDD in comparison with the rigid connection. However, two connection modes have no effects on the maximum strength and initial stiffness of the pier. Moreover, increasing the nominal yield force of SCEDD and the stiffness of disc springs can obviously reduce the ductility of the PSBC. Meanwhile, the potential plastic hinge region moves up. Furthermore, the installation of SCEDD can enhance the energy dissipation capacity of the PSBC, but is still insufficient compared with the cast-in-place (CIP) pier.

A realistic multivariate model for extreme values of ocean parameters such as wind speed, wave height, and wave period is needed to obtain accurate statistical descriptions of metocean conditions. Here, using seasonally normalized monthly coastal winds and waves (CWW) data, we constructed an empirical cumulative distribution function-Pareto (ECDF-Pareto) method to model the marginal cumulative distributions (MCDs) of ocean parameters and compared them with the log-transformed KDE-Pareto (LTKDE-Pareto) distributions, generalized Pareto distributions (GPD), and generalized extreme value (GEV) distributions. Furthermore, six types of trivariate copula models for joint probability distributions (JPDs) of pre-processed CWW data, i.e., symmetric Archimedean copulas (SACs), asymmetric Archimedean copulas (AACs), mixed SACs, mixed AACs, mixed symmetric-asymmetric Archimedean copulas (SAACs), and C-Vine copulas, were described and compared. The results indicated that the proposed ECDF-Pareto outperformed the LTKDE-Pareto, GPD, and GEV distributions in fitting both the interior portions and upper tails of the MCDs. The constructed mixed SAACs gave the best overall fit for the pre-processed 3-dimensional CWW data. The proposed approach for fitting distributions, which applied the ECDF-Pareto method to fit the MCDs and the mixed SAACs to model the JPDs, was able to reasonably model the statistical characteristics and dependence structures of pre-processed CWW data, and therefore, was suitable for the statistical analysis of trivariate extremes for CWW data.

Environmental contours are often utilized in response analysis and design of offshore structures. Properly modeling the multivariate joint probability distributions (JPD) of ocean environmental variables (e.g., wind speed, wave height, and wave period) is fundamental to constructing environmental contours. In this paper, we present an environmental contour construction procedure based on the direct sampling method. The procedure is a semi-parametric joint probability model designed to build the multivariate JPD of ocean environmental variables using a log-transformed kernel density estimation (KDE)-Paretotails approach to calculate statistical characteristics and estimate the marginal cumulative distributions (MCDs) and the vine copula using bivariate copulas as building blocks to fully define the complex dependence structures between MCDs. Furthermore, the importance sampling technique, which can easily be generalized to any dimension, was used to generate kept samples. The proposed model can accommodate three or higher dimensional direct sampling-based environmental contours and was shown by GOF tests to be well suited to building the multivariate JPDs of ocean environmental variables.