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1. Seismic fragility analysis using nonlinear autoregressive neural networks with exogenous input

   https://doi.org/10.1080/15732479.2021.1894184  

   Sheikh, Imran A. ; Khandel, Omid ; Soliman, Mohamed ; Haase, Jennifer S. ; Jaiswal, Priyank ( January 2021 , Structure and Infrastructure Engineering)

   null (Ed.)

   Rapidly growing societal needs in urban areas are increasing the demand for tall buildings with complex structural systems. Many of these buildings are located in areas characterized by high seismicity. Quantifying the seismic resilience of these buildings requires comprehensive fragility assessment that integrates iterative nonlinear dynamic analysis (NDA). Under these circumstances, traditional finite element (FE) analysis may become impractical due to its high computational cost. Soft-computing methods can be applied in the domain of NDA to reduce the computational cost of seismic fragility analysis. This study presents a framework that employs nonlinear autoregressive neural networks with exogenous input (NARX) in fragility analysis of multi-story buildings. The framework uses structural health monitoring data to calibrate a nonlinear FE model. The model is employed to generate the training dataset for NARX neural networks with ground acceleration and displacement time histories as the input and output of the network, respectively. The trained NARX networks are then used to perform incremental dynamic analysis (IDA) for a suite of ground motions. Fragility analysis is next conducted based on the results of the IDA obtained from the trained NARX network. The framework is illustrated on a twelve-story reinforced concrete building located at Oklahoma State University, Stillwater campus. 

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2. Quantifying the impacts of modeling uncertainties on the seismic drift demands and collapse risk of buildings with implications on seismic design checks

   https://doi.org/10.1002/eqe.2740  

   Gokkaya, Beliz U. ; Baker, Jack W. ; Deierlein, Greg G. ( April 2016 , Earthquake Engineering & Structural Dynamics)

   Robust estimation of collapse risk should consider the uncertainty in modeling of structures as well as variability in earthquake ground motions. In this paper, we illustrate incorporation of the uncertainty in structural model parameters in nonlinear dynamic analyses to probabilistically assess story drifts and collapse risk of buildings. Monte Carlo simulations with Latin hypercube sampling are performed on ductile and non‐ductile reinforced concrete building archetypes to quantify the influence of modeling uncertainties and how it is affected by the ductility and collapse modes of the structures. Inclusion of modeling uncertainty is shown to increase the mean annual frequency of collapse by approximately 1.8 times, as compared with analyses neglecting modeling uncertainty, for a high‐seismic site. Modeling uncertainty has a smaller effect on drift demands at levels usually considered in building codes; for the same buildings, modeling uncertainty increases the mean annual frequency of exceeding story drift ratios of 0.03 by 1.2 times. A novel method is introduced to relate drift demands at maximum considered earthquake intensities to collapse safety through a joint distribution of deformation demand and capacity. This framework enables linking seismic performance goals specified in building codes to drift limits and other acceptance criteria. The distributions of drift demand at maximum considered earthquake and capacity of selected archetype structures enable comparisons with the proposed seismic criteria for the next edition (2016) of ASCE 7. Subject to the scope of our study, the proposed drift limits are found to be unconservative, relative to the target collapse safety in ASCE 7. Copyright © 2016 John Wiley & Sons, Ltd.

    

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3. An Intelligently Designed AI for Structural Health Monitoring of a Reinforced Concrete Bridge

   Locke, William R ; Mokalled, Stefani C ; Abuodeh, Omar R ; Redmond, Laura M ; McMahan, Christopher S ( January 2021 , The Concrete Industry in the Era of AI)

   e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques. 

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4. Seismic behavior of a partially grouted reinforced masonry structure: Shake‐table testing and numerical analyses

   https://doi.org/10.1002/eqe.3281  

   Koutras, Andreas A. ; Shing, P. Benson ( May 2020 , Earthquake Engineering & Structural Dynamics)

   In regions of low to moderate seismicity in North America, reinforced masonry structures are mostly partially grouted. The behavior of such structures under lateral seismic loads is complicated because of the interaction of the grouted and ungrouted masonry. As revealed in past experimental studies, the performance of partially grouted masonry (PGM) walls under in‐plane cyclic lateral loading is inferior to that of fully grouted walls. However, the dynamic behavior of a PGM wall system under severe seismic loads is not well understood. In this study, a full‐scale, one‐story, PGM building designed for a moderate seismic zone according to current code provisions was tested on a shake table. It was shown that the structure was able to develop an adequate base shear capacity and withstand two earthquake motions that had an effective intensity of two times the maximum considered earthquake with only moderate cracking in mortar joints. However, the structure eventually failed in a brittle manner in a subsequent motion that had a slightly lower effective intensity. A detailed finite element model of the test structure has been developed and validated. The model has been used to understand the distribution of the lateral force resistance among the wall components and to evaluate the shear‐strength equation given in the design code. The code equation has been found to be adequate for this structure. Furthermore, a parametric study conducted with the finite element model has shown that the introduction of a continuous bond beam right below a window opening is highly beneficial.

    

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5. Seismic loss and damage in light‐frame wood buildings from sequences of induced earthquakes

   https://doi.org/10.1002/eqe.3189  

   Chase, Robert E. ; Liel, Abbie B. ; Luco, Nicolas ; Baird, Bridger W. ( July 2019 , Earthquake Engineering & Structural Dynamics)

   Activities related to oil and gas production, especially deep disposal of wastewater, have led to sequences of induced earthquakes in the central United States. This study aims to quantify damage to and seismic losses for light‐frame wood buildings when subjected to sequences of induced, small to moderate magnitude, events. To conduct this investigation, one‐ and two‐story multifamily wood frame buildings are designed, and their seismic response dynamically simulated using three‐dimensional nonlinear models, subjected to ground motion sequences recorded in induced events. Damage is quantified through seismic losses, which are estimated using the FEMA P‐58 methodology. Results show that at levels of shaking experienced in recent earthquakes, minor damage, consisting of cracking of interior finishes and nonstructural damage to plumbing and heating, ventilation, and air conditioning systems, is expected, which is consistent with observed damage in these events. The study also examines how expected losses and building fragility will accumulate and/or change over a sequence of earthquakes. Results indicate that damage quantified in terms of absorbed hysteretic energy tended to accumulate over the sequences; this damage corresponds to elongation or widening of cracks. However, fragility is not significantly altered by damage in a preceding event, meaning structures are not becoming more vulnerable due to existing damage. In addition, sequences of events do not change losses if the building is only repaired once at the end of the sequence, as the worsening of damage does not alter repair actions. If repairs are conducted after each event, though, total seismic losses can increase greatly from the sequence.

    

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