Analytical Fragility Curves for Seismic Design of Glass Systems Based on Cloud Analysis

Given the growing spread of glass as a construction material, the knowledge of structural response must be ensured, especially under dynamic accidental loads. In this regard, an increasingly popular method to probabilistically characterize the seismic response of a given structure is based on the use of “fragility” or “seismic vulnerability” curves. Most existing applications, however, typically refer to construction and structural members composed of traditional building materials. The present study extends and adapts such a calculation method to innovative structural glass systems, which are characterized by specific material properties and expected damage mechanisms, restraint details, and dynamic features. Suitable Engineering Demand Parameters (EDPs) for seismic design are thus required. In this paper, a major advantage is represented by the use of Cloud Analysis in the Cornell’s reliability method, for the seismic assessment of two different case-study glass systems. Cloud Analysis is known to represent a simple and immediate tool to analytically investigate a given (glass) structure by taking into account variations in seismic motions and uncertainties of structural parameters. Such a method is exploited by means of detailed three-dimensional (3D) Finite Element (FE) numerical models and non-linear dynamic analyses (ABAQUS/Standard). Critical issues and typical failure mechanisms for in-plane seismically loaded glass systems are discussed. The validity of reference EDPs are addressed for the examined solutions. Based on a broad seismic investigation (60 records in total), fragility curves are developed from parametric results, so as to support a multi-hazard performance-based design (PBD) procedure.

On the Use of Cloud Analysis for Structural Glass Members under Seismic Events

Current standards for seismic-resistant buildings provide recommendations for various structural systems, but no specific provisions are given for structural glass. As such, the seismic design of joints and members could result in improper sizing and non-efficient solutions, or even non-efficient calculation procedures. An open issue is represented by the lack of reliable and generalized performance limit indicators (or “engineering demand parameters”, EDPs) for glass structures, which represent the basic input for seismic analyses or q-factor estimates. In this paper, special care is given to the q-factor assessment for glass frames under in-plane seismic loads. Major advantage is taken from efficient finite element (FE) numerical simulations to support the local/global analysis of mechanical behaviors. From extensive non-linear dynamic parametric calculations, numerical outcomes are discussed based on three different approaches that are deeply consolidated for ordinary structural systems. Among others, the cloud analysis is characterized by high computational efficiency, but requires the definition of specific EDPs, as well as the choice of reliable input seismic signals. In this regard, a comparative parametric study is carried out with the support of the incremental dynamic analysis (IDA) approach for the herein called “dynamic” (M1) and “mixed” (M2) procedures, towards the linear regression of cloud analysis data (M3). Potential and limits of selected calculation methods are hence discussed, with a focus on sample size, computational cost, estimated mechanical phenomena, and predicted q-factor estimates for a case study glass frame.

Enhanced Cloud Method (E-Cloud) for Efficient Seismic Fragility Assessment of Structures

Cloud analysis is based on linear regression in the logarithmic space by using least squares, in which a large number of nonlinear dynamic analyses are usually suggested to ensure the accuracy of this method. So, it needs significant computational effort to establish fragility curves especially for the complicated structures. The present paper proposed the Enhanced Cloud Method (E-Cloud) to enhance the efficiency but maintain the accuracy of the Cloud method. The basic concept of the proposed “E-Cloud” aims to utilize both maximum and additional seismic responses with corresponding intensity measures (IMs) from ground motions for the logarithmic linear regression of the Cloud method. Since the nonlinear time-history responses can be transferred to the Engineering Demand Parameter (EDP)-IM curve at the duration when the ground motion is intensifying, the additional seismic responses at different IM levels (i.e., potential Cloud points) can be selected from this EDP-IM curve. These potential Cloud points are combined with maximum seismic responses for the regression so as to reduce the required number of dynamic analyses in Cloud analysis. The proposed “E-Cloud” method is applied for the case study of a typical RC frame structure. By comparison of the obtained probabilistic seismic demand models and fragility curves from “E-Cloud” method to Cloud analysis, it is demonstrated that the E-Cloud method can significantly improve the computational efficiency of the Cloud analysis, which also leads to accurate and stable results for the seismic fragility assessment of structures.

Learning Polynomial-Based Separable Convolution for 3D Point Cloud Analysis

Shape classification and segmentation of point cloud data are two of the most demanding tasks in photogrammetry and remote sensing applications, which aim to recognize object categories or point labels. Point convolution is an essential operation when designing a network on point clouds for these tasks, which helps to explore 3D local points for feature learning. In this paper, we propose a novel point convolution (PSConv) using separable weights learned with polynomials for 3D point cloud analysis. Specifically, we generalize the traditional convolution defined on the regular data to a 3D point cloud by learning the point convolution kernels based on the polynomials of transformed local point coordinates. We further propose a separable assumption on the convolution kernels to reduce the parameter size and computational cost for our point convolution. Using this novel point convolution, a hierarchical network (PSNet) defined on the point cloud is proposed for 3D shape analysis tasks such as 3D shape classification and segmentation. Experiments are conducted on standard datasets, including synthetic and real scanned ones, and our PSNet achieves state-of-the-art accuracies for shape classification, as well as competitive results for shape segmentation compared with previous methods.