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1. Smoothed particle hydrodynamics modeling of elevated structures impacted by tsunami-like waves

   https://doi.org/10.1016/j.engstruct.2022.114851  

   Reis, Cláudia; Barbosa, André R; Figueiredo, Jorge; Clain, Stéphane; Lopes, Mário; Baptista, Maria Ana (November 2022, Engineering Structures)

   Accurate characterization of the response of coastal structures when subjected to tsunami-like waves is important for structural engineering assessment and design. The weakly-compressive Smoothed Particle Hydrodynamic (SPH) model can theoretically investigate such phenomena in both horizontal and vertical directions. Yet, the convergence of the solutions is sensitive to physical and numerical parameters used in the modeling. In this paper, multiple three- and two-dimensional SPH models are used to study the numerical convergence of free-surface elevation solutions for various initial inter-particle distances, domain locations along the flume and vicinity of the structure, and unbroken and broken wave flow conditions. The results are used to infer on the trade-offs between the accuracy of the SPH solutions and computational costs of the simulations, including computing time and data storage requirements. Two-dimensional models and an approximate ratio of ten particles per wave height can reasonably predict the nonturbulent unbroken wave case. The broken wave case requires three-dimensional models and four times the ratio of particles per wave height. A correlation between experimental and numerical results is then performed, showing adequacy of the free-surface elevation converged SPH models to capture global force responses. The distribution of horizontal and vertical pressures exerted on the elevated structure are characterized and compared with an analytical equation derived from the experimental dataset, highlighting the symbiotic relationship between experimental data, for calibration of the models, and numerical insights, for physical setup design. For example, additional instruments should be placed at strategic locations in future experimental programs to further validate numerical local responses, such as pressures near the edges and corners of structures. Such insights are important to support future work and development of updated US and European guidelines for the design of overland built infrastructures. 

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2. 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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3. Surface Roughness Effects on Self-Interacting and Mutually Interacting Rayleigh Waves

   https://doi.org/10.3390/s21165495  

   Bakre, Chaitanya; Lissenden, Cliff J. (August 2021, Sensors)

   Rayleigh waves are very useful for ultrasonic nondestructive evaluation of structural and mechanical components. Nonlinear Rayleigh waves have unique sensitivity to the early stages of material degradation because material nonlinearity causes distortion of the waveforms. The self-interaction of a sinusoidal waveform causes second harmonic generation, while the mutual interaction of waves creates disturbances at the sum and difference frequencies that can potentially be detected with minimal interaction with the nonlinearities in the sensing system. While the effect of surface roughness on attenuation and dispersion is well documented, its effects on the nonlinear aspects of Rayleigh wave propagation have not been investigated. Therefore, Rayleigh waves are sent along aluminum surfaces having small, but different, surface roughness values. The relative nonlinearity parameter increased significantly with surface roughness (average asperity heights 0.027–3.992 μm and Rayleigh wavelengths 0.29–1.9 mm). The relative nonlinearity parameter should be decreased by the presence of attenuation, but here it actually increased with roughness (which increases the attenuation). Thus, an attenuation-based correction was unsuccessful. Since the distortion from material nonlinearity and surface roughness occur over the same surface, it is necessary to make material nonlinearity measurements over surfaces having the same roughness or in the future develop a quantitative understanding of the roughness effect on wave distortion. 

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4. Research Experiences for Undergraduates (REU), NHERI 2023: Mayfield Clothing Mill Deviation Analysis and Conversion of Point Cloud Model to FEM

   https://doi.org/10.17603/ds2-f3pq-dn68  

   Whitesides, Elanor; Kaushal, Saanchi Singh; Napolitano, Rebecca; Wartman, Joseph (January 2023, Designsafe-CI)

   Point cloud models of the Mayfield Clothing Mill, a tornado-damaged historic masonry building, are analyzed to understand any possible structural deviations. A point cloud alignment and deviation analysis workflow are described. Cloud2FEM, a point cloud to finite element model (FEM) conversion software, is utilized to generate a FEM for the Clothing Mill. The resulting FEM can be used for structural analysis purposes and be simulated under load conditions to study the structure’s response. 

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5. 0–5 Hz deterministic 3-D ground motion simulations for the 2014 La Habra, California, Earthquake

   https://doi.org/10.1093/gji/ggac174  

   Hu, Zhifeng; Olsen, Kim B.; Day, Steven M. (May 2022, Geophysical Journal International)

   SUMMARY We have simulated 0–5 Hz deterministic wave propagation for a suite of 17 models of the 2014 Mw 5.1 La Habra, CA, earthquake with the Southern California Earthquake Center Community Velocity Model Version S4.26-M01 using a finite-fault source. Strong motion data at 259 sites within a 148 km × 140 km area are used to validate our simulations. Our simulations quantify the effects of statistical distributions of small-scale crustal heterogeneities (SSHs), frequency-dependent attenuation Q(f), surface topography and near-surface low-velocity material (via a 1-D approximation) on the resulting ground motion synthetics. The shear wave quality factor QS(f) is parametrized as QS, 0 and QS, 0fγ for frequencies less than and higher than 1 Hz, respectively. We find the most favourable fit to data for models using ratios of QS, 0 to shear wave velocity VS of 0.075–1.0 and γ values less than 0.6, with the best-fitting amplitude drop-off for the higher frequencies obtained for γ values of 0.2–0.4. Models including topography and a realistic near-surface weathering layer tend to increase peak velocities at mountain peaks and ridges, with a corresponding decrease behind the peaks and ridges in the direction of wave propagation. We find a clear negative correlation between the effects on peak ground velocity amplification and duration lengthening, suggesting that topography redistributes seismic energy from the large-amplitude first arrivals to the adjacent coda waves. A weathering layer with realistic near-surface low velocities is found to enhance the amplification at mountain peaks and ridges, and may partly explain the underprediction of the effects of topography on ground motions found in models. Our models including topography tend to improve the fit to data, as compared to models with a flat free surface, while our distributions of SSHs with constraints from borehole data fail to significantly improve the fit. Accuracy of the velocity model, particularly the near-surface low velocities, as well as the source description, controls the resolution with which the anelastic attenuation can be determined. Our results demonstrate that it is feasible to use fully deterministic physics-based simulations to estimate ground motions for seismic hazard analysis up to 5 Hz. Here, the effects of, and trade-offs with, near-surface low-velocity material, topography, SSHs and Q(f) become increasingly important as frequencies increase towards 5 Hz, and should be included in the calculations. Future improvement in community velocity models, wider access to computational resources, more efficient numerical codes and guidance from this study are bound to further constrain the ground motion models, leading to more accurate seismic hazard analysis. 

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