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1. Dynamic reliability of infrastructure networks using augmented Monte Carlo simulation

   Zhou, X.; Duenas-Osorio, L. (September 2022, The 13th International Conference on Structural Safety and Reliability (ICOSSAR 2021-2022))

   Li, J.; Spanos, P. D.; Chen, J. B.; Peng, Y. B. (Ed.)

   Infrastructure networks offer critical services to modern society. They dynamically interact with the environment, operators, and users. Infrastructure networks are unique engineered systems, large in scale and high in complexity. One fundamental issue for their reliability assessment is the uncertainty propagation from stochastic disturbances across interconnected components. Monte Carlo simulation (MCS) remains approachable to quantify stochastic dynamics from components to systems. Its application depends on time efficiency along with the capability of delivering reliable approximations. In this paper, we introduce Quasi Monte Carlo (QMC) sampling techniques to improve modeling efficiency. Also, we suggest a principled Monte Carlo (PMC) method that equips the crude MCS with Probably Approximately Correct (PAC) approaches to deliver guaranteed approximations. We compare our proposed schemes with a competitive approach for stochastic dynamic analysis, namely the Probability Density Evolution Method (PDEM). Our computational experiments are on ideal but complex enough source-terminal (S-T) dynamic network reliability problems. We endow network links with oscillators so that they can jump across safe and failed states allowing us to treat the problem from a stochastic process perspective. We find that QMC alone can yield practical accuracy, and PMC with a PAC algorithm can deliver accuracy guarantees. Also, QMC is more versatile and efficient than the PDEM for network reliability assessment. The QMC and PMC methods provide advanced uncertainty propagation techniques to support decision makers with their reliability problems. 

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2. Performance-based design optimization of steel structures subjected to seismic actions

   https://doi.org/10.55592/cilamce.v6i06.10425  

   Rodrigues, Isabela D; Spence, Seymour M; Beck, André T (December 2024, Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE))

   Finding an optimal design for a structural system subject to seismic actions to minimize failure probability, repair costs, and injuries to occupants, significantly contributes to the resilience of buildings in earthquake regions. This research presents a comprehensive framework for the performance-based design optimization of steel structures, incorporating the Performance-Based Earthquake Engineering (PBEE) methodology delineated in FEMA P-58 [1]. A selected set of ground motions, consistent with the seismic hazard intensity of interest, and a nonlinear finite element model, established using OpenSees, enable the assessment of the system's dynamic response. To address the computational complexity related to evaluating the probability of failure of the system during an optimization iteration when using the PBEE methodology for assessing performance, this study introduces metamodeling techniques as a substitute for the original high-fidelity nonlinear finite element model. In particular, Kriging is employed to approximate both the median and standard deviation of the Engineering Demand Parameters (EDPs) in the design domain. The parameters of the Kriging metamodels are derived from nonlinear dynamic analyses performed using the original high-fidelity model and an optimal sampling plan obtained through Latin Hypercube sampling. Under the assumption of a lognormal distribution, the metamodel is then used to generate a large number of simulated demand sets necessary for the Monte Carlo procedure adopted by FEMA P-58 to calculate the distribution of probable losses for any given value of the design variable vector. Additionally, the median and standard deviation of the fragility function modeling collapse are also approximated by a Kriging metamodel, in which the parameters are derived from an Incremental Dynamic Analysis (IDA) for any given value of the design variable vector. The scheme is illustrated in a full-scale case study consisting of the performance-based optimization of the buckling-restrained braces of a steel seismic force-resisting system in terms of expected losses and construction costs. The study demonstrates that the proposed risk-based optimization scheme effectively balances construction costs with expected financial losses from earthquakes, thus enhancing the seismic performance of the system.[1] Applied Technology Council, & National Earthquake Hazards Reduction Program (US). (2012). Seismic performance assessment of buildings. Federal Emergency Management Agency. 

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3. Analytical fragility curves for trees subject to ice loading in a changing climate

   https://doi.org/10.1080/23789689.2023.2202962  

   Campos, R.; Harvey, P. S.; Hou, G. (November 2023, Sustainable and Resilient Infrastructure)

   Campos, Richard & Harvey, P. & Hou, Guangyang. (2023). Analytical fragility curves for trees subject to ice loading in a changing climate. 10.1080/23789689.2023.2202962. Recent severe ice storms across the United States severely damaged trees resulting in extensive electrical power outages. Furthermore, trees and branches can fall on nearby roads, blocking traffic flow and reducing the safety of drivers. In this study, trees subjected to ice loads were analyzed using the finite element method and Monte Carlo simulation to develop analytical fragility curves. Two-dimensional, fractal trees were constructed with randomly generated geometric and mechanical parameters for four deciduous tree species: Acer saccharum, Tilia americana, Fagus grandifolia, and Quercus alba. Two load case scenarios were considered – with and without the effects of leaves – which were then subjected to varying ice accumulation thicknesses. The resulting fragility curves suggest that leaves have a substantial impact on tree branch damage under ice loads, which is significant because of the increase in unseasonably early ice storms due to climate change. 

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4. A real-time structural seismic response prediction framework based on transfer learning and unsupervised learning

   https://doi.org/10.1016/j.engstruct.2024.119227  

   Pak, Hongrak; Paal, Stephanie German (January 2025, Engineering Structures)

   Conventional data-driven methods for predicting the seismic response of structures often require extensive data and computational resources. To address these challenges, a novel deep learning framework that can efficiently and accurately predict the structural seismic responses is proposed. The proposed framework overcomes the limitations of the conventional data-driven methods, by utilizing transfer learning based on the most relevant knowledge determined via the unsupervised learning technique. The framework leverages the seismic information history database to identify the most similar previous earthquake, and subsequently transfers the corresponding knowledge from the Structural Seismic Response network (SSR net) to predict structural responses caused by a new earthquake. This innovative method significantly reduces the need for extensive data collection and provides efficient predictions. Case studies demonstrate the framework’s ability to predict seismic structural responses without extensive training or data collection. The framework can reliably capture the complex nonlinear dynamics of structures under seismic loads and offer significant potential for advancing seismic fragility analyses and reliability assessments. Future research will focus on expanding the framework’s applicability to various structural types and further refining its prediction capabilities. 

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5. An earthquake-source-based metric for seismic fragility analysis

   https://doi.org/10.1007/s10518-018-0341-9  

   Radu, Alin; Grigoriu, Mircea (February 2018, Bulletin of Earthquake Engineering)

   The seismic fragility of a system is the probability that the system enters a damage state under seismic ground motions with specified characteristics. Plots of the seismic fragilities with respect to scalar ground motion intensity measures are called fragility curves. Recent studies show that fragility curves may not be satisfactory measures for structural seismic performance, since scalar intensity measures cannot comprehensively characterize site seismicity. The limitations of traditional seismic intensity measures, e.g., peak ground acceleration or pseudo-spectral acceleration, are shown and discussed in detail. A bivariate vector with coordinates moment magnitude m and source-to-site distance r is proposed as an alternative seismic intensity measure. Implicitly, fragility surfaces in the (m, r)-space could be used as graphical representations of seismic fragility. Unlike fragility curves, which are functions of scalar intensity measures, fragility surfaces are characterized by two earthquake-hazard parameters, (m, r). The calculation of fragility surfaces may be computationally expensive for complex systems. Thus, as solutions to this issue, a bi-variate log-normal parametric model and an efficient calculation method, based on stochastic-reduced-order models, for fragility surfaces are proposed. 

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