Risk-Based Seismic Design Optimization of Steel Building Systems with Passive Damping Devices

Nonlinear time history analysis software and an optimization algorithm for automating design of steel frame buildings with and without supplemental passive damping systems using the risk- or performance-based seismic design philosophy are developed in this dissertation. The software package developed is suitable for conducting dynamic analysis of 2D steel framed structures modeled as shear buildings with linear/nonlinear viscous and viscoelastic dampers. Both single degree of freedom (SDOF) and multiple degree of freedom (multistory or MDOF) shear-building systems are considered to validate the nonlinear analysis engine developed. The response of both un-damped and damped structures using the 1940 EI Centro (Imperial Valley) ground motion record and sinusoidal ground motion input are used in the validation. Comparison of response simulations is made with the dissertationSEES software system and analytical models based upon established dynamic analysis theory. A risk-based design optimization approach is described and formulation of unconstrained multiple objective design optimization problem statements suitable for this design philosophy are formulated. Solution to these optimization problems using a genetic algorithm are discussed and a prototypical three story, four bay shear-building structure is used to demonstrate applicability of the proposed risk-based design optimization approach for design of moderately sized steel frames with and without supplemental damping components. All programs are developed in MATLAB environment and run on Windows XP operating system. A personal computer cluster with four computational nodes is set up to reduce the computing time and a description of implementation of the automated design algorithm in a cluster computing environment is provided. The prototype building structure is used to demonstrate the impact that the number of design variables has on the resulting designs and to demonstrate the impact that use of supplemental viscous and viscoelastic damping devices have on minimizing initial construction cost and minimizing expected annual loss due to seismic hazard

Bis(2,2′-bipyrid­yl)bromidocopper(II) bromide bromo­acetic acid hemihydrate

In the title compound, [CuBr(C10H8N2)2]Br·BrCH2COOH·0.5H2O, the CuII ion is coordinated by four N atoms [Cu—N = 1.985 (6)–2.125 (7) Å] from two 2,2′-bipyridine ligand mol­ecules and a bromide anion [Cu—Br = 2.471 (2) Å] in a distorted trigonal-bipyramidal geometry. Short centroid–centroid distances [3.762 (5) and 3.867 (5) Å] between the aromatic rings of neighbouring cations suggest the existence of π–π inter­actions. Inter­molecular O—H⋯Br hydrogen bonds and weak C—H⋯O and C—H⋯Br inter­actions consolidate the crystal packing

Bis(3,4-dimethoxy­benzoato-κ2 O,O′)(1,10-phenanthroline-κ2 N,N′)copper(II)

The asymmetric unit of the title compound, [Cu(C9H9O4)2(C12H8N2)], contains one half-mol­ecule, the complete mol­ecule being generated by a twofold rotation axis. The CuII atom exhibits a six-coordinated distorted octa­hedral geometry with two N atoms from the phenanthroline ligand [Cu—N 2.007 (2) Å] and four O atoms from two 3,4-dimethoxy­benzoate ligands [Cu—O 1.950 (1) and 2.524 (1) Å]. The difference in Cu—O bond distances indicates a strong Jahn–Teller effect. In the crystal, C—H⋯π inter­actions result in chains of mol­ecules along the c axis

Triaqua­chlorido(1,10-phenanthroline-κ2 N,N′)cobalt(II) chloride monohydrate

In the title compound, [CoCl(C12H8N2)(H2O)3]Cl·H2O, the CoII ion is coordinated by two N atoms from the 1,10-phenanthroline ligand [Co—N = 2.125 (6) and 2.146 (6) Å], one chloride ligand [Co—Cl = 2.459 (2)Å] and three water mol­ecules [Co—O = 2.070 (5)–2.105 (5)Å] in a distorted octa­hedral geometry. Inter­molecular O—H⋯Cl and O—H⋯O hydrogen bonds form an extensive three-dimensional hydrogen-bonding network, which consolidates the crystal packing

Bayesian Analysis for Random Effects Models

Random effects models have been widely used to analyze correlated data sets, and Bayesian techniques have emerged as a powerful tool to fit the models. However, there has been scarce literature that systematically reviews and summarizes the recent advances of Bayesian analyses of random effects models. This chapter reviews the use of the Dirichlet process mixture (DPM) prior to approximate the distribution of random errors within the general semiparametric random effects models with parametric random effects for longitudinal data setting and failure time setting separately. In a survival setting with clusters, we propose a new class of nonparametric random effects models which is motivated from the accelerated failure models. We employ a beta process prior to tact clustering and estimation simultaneously. We analyze a new data set integrated from Alzheimer’s disease (AD) study to illustrate the presented model and methods

A Tensor-Based Framework for Studying Eigenvector Multicentrality in Multilayer Networks

Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.Comment: 57 pages, 10 figure

Bis(2-bromo­acetato-κ2 O,O′)(1,10-phenanthroline-κ2 N,N′)copper(II)

The two halves of the title compound, [Cu(C2H2BrO2)2(C12H8N2)], are related by twofold symmetry along the b axis through the central CuII ion. The CuII ion is coordinated by two symmetry-related N atoms from the 1,10-phenanthroline ligand and four O atoms from two 2-bromo­acetate ligands, showing a distorted octahedral geometry. Weak inter­molecular C—H⋯O inter­actions link neighbouring mol­ecules

Aqua­bis(2-iodo­acetato-κO)(1,10-phenanthroline-κ2 N,N′)copper(II)

In the title compound, [Cu(C2H2IO2)2(C12H8N2)(H2O)], the CuII ion is coordinated by two N atoms [Cu—N = 2.013 (4) and 2.024 (4) Å] from a 1,10-phenanthroline ligand and three O atoms [Cu—O = 1.940 (4)–2.261 (4) Å] from two carboxyl ligands and a water mol­ecule in a distorted square-pyramidal geometry. One iodo­acetate O atom [Cu—O = 2.775 (4) Å] completes the coordination to form a distorted octa­hedron. Inter­molecular O—H⋯O hydrogen bonds link the mol­ecules into centrosymmetric dimers, which are further packed by π–π inter­actions between the 1,10-phenanthroline ligands into layers parallel to the ab plane. The crystal packing also exhibits short inter­molecular I⋯I contacts of 3.6772 (9) Å and weak C—H⋯O hydrogen bonds

Aqua­bis(dichloro­acetato-κO)(1,10-phenanthroline-κ2 N,N′)copper(II)

In the title complex, [Cu(C2HCl2O2)2(C12H8N2)(H2O)], the CuII ion has a distorted square-pyramidal coordination geometry. The equatorial positions are occupied by two N atoms from a 1,10-phenanthroline ligand [Cu—N = 1.994 (3) and 2.027 (3) Å] and two O atoms from dichloro­acetate ligands and a water mol­ecule [Cu—O = 1.971 (2) and 1.939 (2) Å]. One O atom from another dichloro­acetate ligand occupies the apical positon [Cu—O = 2.152 (3) Å]. Inter­molecular O—H⋯O hydrogen bonds link the mol­ecules into centrosymmetric dimers. The crystal packing also exhibits weak inter­molecular C—H⋯O hydrogen bonds, π–π inter­actions [centroid–centroid distance = 3.734 (2) Å] and short inter­molecular Cl⋯Cl contacts [3.306 (2) and 3.278 (2) Å]

Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services

Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users' QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme