Edge-based graph neural network for ranking critical road segments in a network.

Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network's efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.

Effects of leaflet curvature and thickness on the crimping stresses in transcatheter heart valve.

With the current advances and expertise in biomedical device technologies, transcatheter heart valves (THVs) have been drawing significant attention. Various studies have been carried out on their durability and damage by dynamic loading in operational conditions. However, very few numerical investigations have been conducted to understand the effects of leaflet curvature and thickness on the crimping stresses which arise during the surgical preparation processes. In order to contribute to the current state of the art, a full heart valve model was presented, the leaflet curvature and thickness of which were then parameterized so as to understand the stress generation as a result of the crimping procedure during the surgical preparations. The results show that the existence of stresses is inevitable during the crimping procedure, which is a reduction factor for valve durability. Especially, stresses on the leaflets at the suture sites connected with the skirt were deduced to be critical and may result in leaflet ruptures after THV implantation.

Identification of Nonlinear Soil Properties from Downhole Array Data Using a Bayesian Model Updating Approach.

An accurate seismic response simulation of civil structures requires accounting for the nonlinear soil response behavior. This, in turn, requires understanding the nonlinear material behavior of in situ soils under earthquake excitations. System identification methods applied to data recorded during earthquakes provide an opportunity to identify the nonlinear material properties of in situ soils. In this study, we use a Bayesian inference framework for nonlinear model updating to estimate the nonlinear soil properties from recorded downhole array data. For this purpose, a one-dimensional finite element model of the geotechnical site with nonlinear soil material constitutive model is updated to estimate the parameters of the soil model as well as the input excitations, including incident, bedrock, or within motions. The seismic inversion method is first verified by using several synthetic case studies. It is then validated by using measurements from a centrifuge test and with data recorded at the Lotung experimental site in Taiwan. The site inversion method is then applied to the Benicia-Martinez geotechnical array in California, using the seismic data recorded during the 2014 South Napa earthquake. The results show the promising application of the proposed seismic inversion approach using Bayesian model updating to identify the nonlinear material parameters of in situ soil by using recorded downhole array data.

Microstructural evaluation and recommendations for face masks in community use to reduce the transmission of respiratory infectious diseases.

BACKGROUND AND OBJECTIVE: Recommendations for the use of face masks to prevent and protect against the aerosols (≤5µm) and respiratory droplet particles (≥5µm), which can carry and transmit respiratory infections including severe acute respiratory syndrome coronavirus (SARS-CoV-2), have been in effect since the early stages of the coronavirus disease 2019 (COVID-19). The particle filtration efficiency (PFE) and air permeability are the most crucial factors affecting the level of pathogen transmission and breathability, i.e. wearer comfort, which should be investigated in detail. METHODS: In this context, this article presents a novel assessment framework for face masks combining X-ray microtomography and computational fluid dynamics simulations. In consideration to their widespread public use, two types of face masks were assessed: (I) two layer non-woven face masks and (II) the surgical masks (made out of a melt-blown fabric layer covered with two non-woven fabric layers). RESULTS: The results demonstrate that the surgical masks provide PFEs over 75% for particles with diameter over 0.1µm while two layer face masks are found out to have insufficient PFEs, even for the particles with diameter over 2µm (corresponding PFE is computed as 47.2%). Thus, existence of both the non-woven fabric layers for mechanical filtration and insertion of melt-blown fabric layer(s) for electrostatic filtration in the face masks were found to be highly critical to prevent the airborne pathogen transmission. CONCLUSIONS: The present framework would assist in computational assessment of commonly used face mask types based on their microstructural characteristics including fiber diameter, orientation distributions and fiber network density. Therefore, it would be also possible to provide new yet feasible design routes for face masks to ensure reliable personal protection and optimal breathability.

Sustainable Earthquake Resilience with the Versatile Shape Memory Alloy (SMA)-Based Superelasticity-Assisted Slider.

Earthquakes threaten humanity globally in complex ways that mainly include various socioeconomic consequences of life and property losses. Resilience against seismic risks is of high importance in the modern world and needs to be sustainable. Sustainable earthquake resilience (SER) from the perspective of structural engineering means equipping the built environment with appropriate aseismic systems. Shape memory alloys (SMAs) are a class of advanced materials well suited for fulfilling the SER demand of the built environment. This article explores how this capability can be realized by the innovative SMA-based superelasticity-assisted slider (SSS), recently proposed for next-generation seismic protection of structures. The versatility of SSS is first discussed as a critical advantage for an effective SER. Alternative configurations and implementation styles of the system are presented, and other advantageous features of this high-tech isolation system (IS) are studied. Results of shaking table experiments, focused on investigating the expected usefulness of SSS for seismic protection in hospitals and conducted at the structural earthquake engineering laboratory of the University of Bonab, are then reported. SSS is compared with currently used ISs, and it is shown that SSS provides the required SER for the built environments and outperforms other ISs by benefitting from the pioneered utilization of SMAs in a novel approach.

Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements.

Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism.

Airborne pathogen projection during ophthalmic examination.

PURPOSE: Microscale droplets act as coronaviruses (CoV) carriers in the air when released from an infected person and may infect others during close contact such as ophthalmic examination. The main objective of the present work is to demonstrate how CoV deposited droplets are projected during biomicroscopy and to discuss what kind of precautions should be taken in ophthalmic practice. METHODS: A coupled fluid-structure system comprising smoothed particle hydrodynamics and the finite element method has been built to assess the projection of droplets spreading from an infected person. Different conditions based on the maximum exit flow velocity from the infector's mouth during the ophthalmic examination were modeled. RESULTS: During exhalation, for which the exit flow is ~ 1000 mm/s, the average horizontal distance of the flow front was ~ 200 mm while individual particles can reach up to ~ 500 mm. In case of coughing or sneezing (corresponding to an exit flow of ~ 12,000 mm/s), the average horizontal distance of the flow front was ~ 1300 mm. CONCLUSION: During the ophthalmic examination, the proximity to the patient's nose and mouth was observed to be less than the horizontal distance of flow front particles. Even though mounted breath shields are used, particles flew beyond the shield and contaminate the ophthalmologist. Compared with the current protective breath shields, the use of a larger shield with a minimum radius of 18 cm is needed to decrease viral transmission.