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Importance: Particulate respirators such as N95 masks are an essential component of personal protective equipment (PPE) for front-line workers. This study describes a rapid and effective UVC irradiation system that would facilitate the safe re-use of N95 respirators and provides supporting information for deploying UVC for decontamination of SARS-CoV-2 during the COVID19 pandemic. Objective: To assess the inactivation potential of the proposed UVC germicidal device as a function of time by using 3M 8211 - N95 particulate respirators inoculated with SARS-CoV-2. Design: A germicidal UVC device to deliver tailored UVC dose was developed and snippets (2.5cm2) of the 3M-N95 respirator were inoculated with 106 plaque-forming units (PFU) of SARS-CoV-2 and were UV irradiated. Different exposure times were tested (0-164 seconds) by fixing the distance between the lamp (10 cm) and the mask while providing an exposure of at least 5.43 mWcm-2. Setting: The current work is broadly applicable for healthcare-settings, particularly during a pandemic such as COVID-19. Participants: Not applicable. Main Outcome(s) and Measure(s): Primary measure of outcome was titration of infectious virus recovered from virus-inoculated respirator pieces after UVC exposure. Other measures included the method validation of the irradiation protocol, using lentiviruses (biosafety level-2 agent) and establishment of the germicidal UVC exposure protocol. Results: An average of 4.38x103 PFUml-1(SD 772.68) was recovered from untreated masks while 4.44x102 PFUml-1(SD 203.67), 4.00x102 PFUml-1(SD 115.47), 1.56x102 PFUml-1(SD 76.98) and 4.44x101 PFUml-1(SD 76.98) was recovered in exposures 2s,6s,18s and 54 seconds per side respectively. The germicidal device output and positioning was monitored and a minimum output of 5.43 mWcm-2 was maintained. Infectious SARS-CoV-2 was not detected by plaque assays (minimal level of detection is 67 PFUml-1) on N95 respirator snippets when irradiated for 120s per side or longer suggesting 3.5 log reduction in 240 seconds of irradiation. Conclusions and Relevance: A scalable germicidal UVC device to deliver tailored UVC dose for rapid decontamination of SARS-CoV-2 was developed. UVC germicidal irradiation of N95 snippets inoculated with SARS-CoV-2 for 120s per side resulted in 100% (3.5 log in total) reduction of virus. These data support the reuse of N95 particle-filtrate apparatus upon irradiation with UVC and supports use of UVC-based decontamination of SARS-CoV-2 virus during the COVID19 pandemic.

Alkali Silica Reaction (ASR) is known to be a serious problem for concrete worldwide, especially in high humidity and high temperature regions. ASR is a slow process that develops over years to decades and it is influenced by changes in environmental and loading conditions of the structure. The problem becomes even more complicated if one recognizes that other phenomena like creep and shrinkage are coupled with ASR. This results in synergistic mechanisms that can not be easily understood without a comprehensive computational model. In this paper, coupling between creep, shrinkage and ASR is modeled within the Lattice Discrete Particle Model (LDPM) framework. In order to achieve this, a multi-physics formulation is used to compute the evolution of temperature, humidity, cement hydration, and ASR in both space and time, which is then used within physics-based formulations of cracking, creep and shrinkage. The overall model is calibrated and validated on the basis of experimental data available in the literature. Results show that even during free expansions (zero macroscopic stress), a significant degree of coupling exists because ASR induced expansions are relaxed by meso-scale creep driven by self-equilibriated stresses at the meso-scale. This explains and highlights the importance of considering ASR and other time dependent aging and deterioration phenomena at an appropriate length scale in coupled modeling approaches.

Alkali silica reaction and its effect on concrete and mortar have been studied for many years. Several tests and procedures have been formulated to evaluate this reaction, particularly in terms of aggregate reactivity. However, the data given in the literature concerning the mechanical properties of concrete and mortar are scattered and very little information is available for some properties such as fracture energy. In this study, the mechanical behavior of mortar was evaluated and monitored, under normal and accelerated environmental conditions. Fracture energy, compressive strength and tensile strength were measured for mortar specimens, casted with highly reactive Spratt crushed aggregate, at two different storage temperatures (23℃ and 80℃) and at two different alkali concentrations (immersed in water and in 1 N NaOH solution). Moreover, free expansion tests (according to ASTM C1260) and petrographic observations were performed, in order to relate them to the evolution of the mechanical properties of mortar. Results show a decrease of the mechanical properties associated with specimens at 80℃ in alkali solution and that the deterioration due to alkali silica reaction is counter-balanced by the strengthening of mortar resulting from the hydration process. A multi-physics computational framework, based on the Lattice Discrete Particle Model is then proposed. Numerical simulations based on a complete calibration and validation with the obtained experimental data capture the behavior of mortar subjected to the complex coupled effect of strength build-up and alkali silica reaction at different temperatures and alkali contents.

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In this study, a coarse-graining framework for discrete models is formulated on the basis of multiscale homogenization. The discrete model considered in this paper is the Lattice Discrete Particle Model (LDPM), which simulates concrete at the level of coarse aggregate pieces. In LDPM, the size of the aggregate particles follows the actual particle size distribution that is used in experiment to produce concrete specimens. Consequently, modeling large structural systems entirely with LDPM leads to a tremendous number of degrees of freedom and is not feasible with the currently available computational resources. To overcome this limitation, this paper proposes the formulation of a coarse-grained model obtained by (1) increasing the actual size of the particles in the fine-scale model by a specific coarsening factor and (2) calibrating the parameters of the coarse grained model by best fitting the macroscopic, average response of the coarse grained model to the corresponding fine scale one for different loading conditions. A Representative Volume Element (RVE) of LDPM is employed to obtain the macroscopic response of the fine scale and coarse grained models through a homogenization procedure.Accuracy and efficiency of the developed coarse graining method is verified by comparing the response of fine scale and coarse grained simulations of several reinforced concrete structural systems in terms of both accuracy of the results and computational cost.