•A Bayesian deep learning based method is developed to predict seismic responses.•The method quantifies the uncertainties in outputs caused by input ground motions.•The model is trained with a wide ...class of hysteretic behaviors and ground motions.•The model is not overfitted to hysteretic behaviors or ground motions in train dataset.•The proposed method is applied to input feature selection and fragility evaluation.
Structural failures caused by a strong earthquake may induce a large number of casualties and huge socioeconomic losses. To design a structure that can withstand such earthquake events, it is essential to accurately estimate the nonlinear structural responses caused by strong ground motions. As a replacement of an onerous and complex nonlinear time history analysis, simple regression-based equations have been widely adopted in routine engineering practices. It is, however, noted that the response prediction is deterministic, which cannot quantify the variabilities stemming from the nonlinear behavior of the structural system, i.e. varying seismic demands given the same earthquake intensity value. In addition, it is well known that the accuracy of prediction based on the regression-based equations is limited. In order to quantify such uncertainties and improve the prediction accuracy, this paper proposes a probabilistic deep neural network model based on a Bayesian deep learning method. By introducing a loss function which is proportional to the negative log likelihood of the Gaussian distribution function, the mean and variance of the structural responses can be obtained. This assessment is important especially for earthquake engineering applications because of large randomness in the input ground motion details and their significant impact on the structural responses. Moreover, using the proposed probabilistic deep neural network model, one can estimate seismic fragilities of the structural system efficiently. Thorough numerical investigations are carried out to demonstrate the proposed method. The supporting source code and data are available for download at http://ERD2.snu.ac.kr.
Bouc–Wen class models have been widely used to efficiently describe smooth hysteretic behavior in time history and random vibration analyses. This paper proposes a generalized Bouc–Wen model with ...sufficient flexibility in shape control to describe highly asymmetric hysteresis loops. Also introduced is a mathematical relation between the shape-control parameters and the slopes of the hysteresis loops, so that the model parameters can be identified systematically in conjunction with available parameter identification methods. For use in nonlinear random vibration analysis by the equivalent linearization method, closed-form expressions are derived for the coefficients of the equivalent linear system in terms of the second moments of the response quantities. As an example application, the proposed model is successfully fitted to the highly asymmetric hysteresis loops obtained in laboratory experiments for flexible connectors used in electrical substations. The model is then employed to investigate the effect of dynamic interaction between interconnected electrical substation equipment by nonlinear time-history and random vibration analyses.
Display omitted
•Correlating mechanical and structural properties are crucial in designing structures.•Similar mechanical behavior was observed between atomistic and 3D-printed structures.•3D ...printing with ductile TPU filaments had a high agreement with micro-level simulations.•Classified mechanical response and linked stress changes to various factors.
A wide range of mechanical properties are vital in structures, from macro (e.g., load-bearing) down to atomistic (nanomaterial) level. To design structures with the target mechanical properties, it is crucial to understand the correlation between the mechanical characteristics and structural information. To this end, we explored the similarity in the mechanical behavior between atomistic structures and actual 3D-printed zeolite structures. The zeolite structure was chosen because of its various structural parameters such as pore size, distribution, and geometry. Molecular dynamics (MD) simulations confirmed that similar behavior was observed in the mechanical responses at an atomic scale and with a 3D-printed macro-scale structure. 3D printing with ductile thermoplastic polyurethane (TPU) filaments showed a high degree of agreement with microstructure-level simulations. The mechanical response of zeolite structures is classified depending on their linearity and the characteristics with respect to the applied strain, to anticipate the potential applications of mechanical metamaterials. Further comparative analysis was conducted between the structural characteristics and mechanical properties, linking the changes in the stress to factors, such as density, porosity, angle, and bond length. This study demonstrates that metamaterial design with a mechanical response can be achieved using atomic-level structural design degrees of freedom.
Abstract
Yeast products are extensively used as additives in livestock feed to improve their growth performance and intestinal health. A total of 90 crossbred finishing pigs were allocated to one of ...three treatments according to their BW and sex (2 barrows and 2 gilts) with 6 replicates per treatment and 5 pigs per pen for eight weeks. The dietary treatments were CON -basal diet, CON + 0.05% yeast hydrolysate (YH), CON + 0.1% YH. The yeast hydrolysate supplement used in this study was commercially prepared in the name of CALMORIN. The active ingredients of YH were 40% of crude protein, 3.5% of nucleotides, 23% of β-glucans,4.9% glutamic acid, and 15% of mannan-oligosaccharides. Data were subjected to the statistical analysis as a complete randomized design using the GLM procedures of SAS and the pen was used as the experimental unit. Linear and quadratic polynomial contrasts were performed to determine the effects yeast in the diet with P < 0.05 indicating significance. Dietary inclusion of YH supplement linearly increased (P < 0.05) body weight and average daily gain of pigs at wk 4,8, and overall trail period (respectively). Moreover, apparent total digestibility of dry matter, nitrogen, and energy showed linear (P < 0.05) improvement in pigs fed graded level of YH supplementation. Also, the inclusion of YH supplementation linearly increased (P < 0.05) the fecal microbial lactobacillus population. Furthermore, during the end of the trial, pigs fed YH tended to linearly increase (P < 0.05) backfat thickness and lean meat percentage and linearly reduce drip loss (P > 0.05) from meat sample on day 3 and 5 of storage. In conclusion the growth performance, nutrient digestibility, lactobacillus count, and meat quality of finishing pigs were positively enhanced by yeast hydrolysate supplement in the diet.
Because using social media has become a major part of people's daily lives, many of their personal characteristics are often implicitly or explicitly reflected in the content they share. We present a ...study of two personal characteristics-age and gender-related to user engagement on Instagram that can be determined through the characterization of images and tags. We demonstrate the strong influence of age and gender on Instagram use in terms of topical and content differences. We then build age and gender classification models that yield F1 scores of up to 88% and 74% in the detection of age and gender, respectively, and that better characterize users by images than by tags. We further demonstrate the robustness of our models using a new set of test data, with which the models exhibit greater overall performance than human raters. Our study highlights that future research should look to exploit images to a greater degree because they complement text and there are many unexamined images with no embedded text available.
Artificial intelligence-based algorithms are becoming essential tools in materials science-related fields because of their excellent functionality in reflecting physics in the training database and ...predicting the properties of unexplored materials with outstanding accuracy. Designing novel materials with engineered properties, such as metamaterials, is the key to revolutionizing material discovery, and machine learning (ML) and deep learning (DL) can be powerful and indispensable tools for acceleration. This review focuses on the implementation of ML/DL-based approaches for designing metamaterials. Quantum–mechanical, atomistic, and macroscale simulation methods are also assessed as database construction processes. Forward and inverse design methods are summarized in detail, and breakthroughs in generative models are particularly introduced. Moreover, applications in fundamental property prediction and material structural design are reviewed. Finally, the remaining challenging tasks for future related work are presented.
Prediction of structural deterioration is a challenging task due to various uncertainties and temporal changes in the environmental conditions, measurement noises as well as errors of mathematical ...models used for predicting the deterioration progress. Monitoring of deterioration progress is also challenging even with successive measurements, especially when only indirect measurements such as structural responses are available. Recent developments of Bayesian filters and Bayesian inversion methods make it possible to address these challenges through probabilistic assimilation of successive measurement data and deterioration progress models. To this end, this paper proposes a new framework to monitor and predict the spatiotemporal progress of structural deterioration using successive, indirect and noisy measurements. The framework adopts particle filter for the purpose of real-time monitoring and prediction of corrosion states and probabilistic inference of uncertain and/or time-varying parameters in the corrosion progress model. In order to infer deterioration states from sparse indirect inspection data, for example structural responses at sensor locations, a Bayesian inversion method is integrated with the particle filter. The dimension of a continuous domain is reduced by the use of basis functions of truncated Karhunen-Loève expansion. The proposed framework is demonstrated and successfully tested by numerical experiments of reinforcement bar and steel plates subject to corrosion.
Abstract
Minerals enhance the digestive and bio-synthesis process and growth of animals. Nano-minerals are considered to be more efficient in growth, immunomodulation, bactericidal effects than ...regular products. Also, they are needed in a lower dose. Sulfur is an essential part of many enzymes and antioxidant molecules like glutathione and thioredoxin. Some sulfur containing compounds can efficiently form a line of defense against reactive oxygen and nitrogen species. This study aimed to evaluate the effects of Detoxified nano-Sulfur Dispersion (DSD) on growth performance, fecal score, fecal microbial, gas emissions, blood profile, nutrient digestibility and meat quality in finishing pigs. A total of 160 pigs with an initial body weight of 54.90 ± 5.10 kg were randomly assigned to 2 treatments comprising of basal diet and basal diet with 10ppm DSD. All data were statically analyzed by T-test using the SAS program as a randomized complete block design, with the pen serving as an experimental unit. During the 10-week trial, there were no differences (P > 0.05) in body weight (BW), average daily gain (ADG), average daily feed intake (ADFI) and gain to feed ratio (G:F) between the control and DSD groups. Also, the fecal score, fecal microbiota, gas emission were not affected (P > 0.05) by DSD diet. Dietary inclusion of DSD tended (P < 0.10) to increase water holding capacity and decrease cooking loss and drip loss. At week 5, serum concentrations of glucose, calcium, total cholesterol, high-density level were increased, and triglyceride concentration was significantly (P < 0.05) reduced in pigs fed with DSD than control diets. In summary, the inclusion of dietary DSD in the finishing pig diet improved serum Ca, glucose concentrations and lipid profiles. It also improved some meat quality traits, indicating its importance in improving the health status of animals.
Various types of structural systems are often subjected to the risk of fatigue-induced failures. If a structure does not have an adequate level of structural redundancy, local failures may initiate ...sequential failures and cause exceedingly large damage. For the risk-informed design and maintenance of such structural systems, it is thus essential to quantify the risk of fatigue-induced sequential failure. However, such risk analysis is often computationally intractable because one needs to explore innumerable failure sequences, each of which demands component and system reliability analyses in conjunction with structural analyses to account for various uncertainties and the effect of load redistributions. To overcome this computational challenge, many research efforts have been made to identify critical failure sequences with the highest likelihood and to quantify the overall risk by system reliability analysis based on the identified sequences. One of the most widely used approaches is the so-called “branch-and-bound” method. However, only the lower bound on the system risk is usually obtained because of challenges in system reliability analysis, while the changes of the lower bound by newly identified sequences are not diminishing monotonically. This paper aims to improve the efficiency and accuracy of risk analysis of fatigue-induced sequential failures by developing a new branch-and-bound method employing system reliability bounds (termed the B3 method). On the basis of a recursive formulation of the limit-state functions of fatigue-induced failures, a system failure event is formulated as a disjoint cut-set system event. A new search scheme identifies critical fatigue-induced failure sequences in the decreasing order of their probabilities while it systematically updates both lower and upper bounds on the system failure probability without additional system reliability analyses. As a result, the method can provide reasonable criteria for terminating the branch-and-bound search without missing critical failure sequences and reduce the number of computational simulations required to obtain reliable estimates on the system risk. The B3 method is tested and demonstrated by numerical examples of a multilayer Daniels system and a three-dimensional offshore structure.
PDF
