•CNN models for predicting creep and shrinkage of concrete are researched.•The constitutive law of concrete based on the proposed CNN models is presented.•An ABAQUS user subroutine of concrete creep ...and shrinkage is developed.•The performance of CNN models is better than the B4 model.•Developed user subroutine predicts well the long-term deformation of a RC beam.
The problem of long-term deformation caused by creep and shrinkage (C&S) needs to be concerned in the design and service of concrete structures. Although various models have been developed to predict the C&S of concrete, more accurate and reliable prediction methods are still needed. The models of C&S based on convolutional neural networks (CNNs) are proposed in the study. The performance of the CNN models is verified by using 906 sets of creep experiment data and 1114 sets of shrinkage experiment data in the Northwestern University (NU) database. Besides, the K-means clustering algorithm is introduced to divide the data set into the training set, validation set, and test set, and the problem of uneven distribution of the data set on the time scale is overcome. Finally, the incremental viscoelastic constitutive law of concrete based on the developed CNN models is proposed, and the ABAQUS user subroutine for simulating C&S of concrete is developed. The availability of the user subroutine is validated by the creep and shrinkage test of a reinforced concrete beam. The research can provide reliable methods for the rapid prediction of C&S of concrete and the simulation analysis of long-term deformation of concrete structures.
Abstract
Ferroelectric vortex in multiferroic materials has been considered as a promising alternative to current memory cells for the merit of high storage density. However, the formation of regular ...natural ferroelectric vortex is difficult, restricting the achievement of vortex memory device. Here, we demonstrated the creation of ferroelectric vortex-antivortex pairs in BiFeO
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thin films by using local electric field. The evolution of the polar vortex structure is studied by piezoresponse force microscopy at nanoscale. The results reveal that the patterns and stability of vortex structures are sensitive to the poling position. Consecutive writing and erasing processes cause no influence on the original domain configuration. The
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4 proper coloring vortex-antivortex network is then analyzed by graph theory, which verifies the rationality of artificial vortex-antivortex pairs. This study paves a foundation for artificial regulation of vortex, which provides a possible pathway for the design and realization of non-volatile vortex memory devices and logical devices.
For rhombohedral multiferroelectrics, non-180° ferroelectric domain switching may induce ferroelastic and/or (anti-)ferromagnetic effect. So the determination and control of ferroelectric domain ...switching angles is crucial for nonvolatile information storage and exchange-coupled magnetoelectric devices. We try to study the intrinsic characters of polarization switching in BiFeO3 by introducing a special data processing method to determine the switching angle from 2D PFM (Piezoresponse Force Microscopy) images of randomly oriented samples. The response surface of BiFeO3 is first plotted using the piezoelectric tensor got from first principles calculations. Then from the normalized 2D PFM signals before and after switching, the switching angles of randomly oriented BiFeO3 grains can be determined through numerical calculations. In the polycrystalline BiFeO3 films, up to 34% of all switched area is that with original out-of-plane (OP) polarization parallel to the poling field. 71° polarization switching is more favorable, with the area percentages of 71°, 109° and 180° domain switching being about 42%, 29% and 29%, respectively. Our analysis further reveals that IP stress and charge migration have comparable effect on switching, and they are sensitive to the geometric arrangements. This work helps exploring a route to control polarization switching in BiFeO3, so as to realize desirable magnetoelectric coupling.
Considerable attention has recently been focused on classification and regression-based convolutional neural network (CNN) and long short-term memory (LSTM) due to their excellent performance in ...capturing complex spatial and temporal information characteristics for structural damage identification. However, few studies have considered structural damage identification as a classification and regression problem. In addition, bridges in practical engineering are vulnerable to various environmental and vehicle loading conditions. Hence, this study proposed a new two-stage CNN–LSTM configuration for bridge damage identification using vibration data considering the influence of temperatures. First, a classification-based CNN–LSTM is designed to perform multiclass damage detection tasks, and then a regression-based CNN–LSTM is developed for damage localization and severity prediction tasks. The performance of the proposed damage identification method was evaluated through a simulation dataset of a concrete highway bridge model and a field experiment dataset of Z24-bridge (Switzerland). In addition, a set of statistical evaluation metrics such as sparse categorical cross-entropy loss, accuracy, confusion matrix, mean squared loss, mean absolute error, mean absolute percentage error, and coefficient of determination were used to compare the damage identification performance of the proposed CNN–LSTM configuration with a regular CNN model and conventional machine learning (ML) algorithms. Prediction results indicate that the proposed CNN–LSTM model outperforms the regular CNN model and conventional ML algorithms for bridge damage identification.
We report a one-step, mild method to modify antifouling oligo(ethylene glycol)-terminated self-assembled monolayers. We demonstrate for the first time that self-polymerized dopamine, previously ...reported as an underwater adhesive, can be patterned on typical antifouling surfaces by microfluidic patterning or microcontact printing. The patterns can be applied in spatiotemporal cell patterning.
To achieve high-power density in power supplies, it is desirable to minimize the physical size of the energy storage capacitor. The capacitance is determined by the energy storage requirement for ...line outage ride-through and also the ripple current handling capability of the capacitor. Interleaving is well known as an effective method to reduce the capacitor ripple current and in cases where ripple current considerations dominate, it could reduce capacitor size. This paper presents a methodology to calculate the ripple current, both for single phase and for m interleaved phases of power factor correction converters operating with constant load or a DC-DC converter load. Experimental results from a commercial power supply yielded a small error when compared to the calculations, showing that the proposed methodology has enough accuracy to be used as a design tool.
Giant-permittivity CaCu3Ti4O12 has been modeled by nature at an atomic or electronic scale. Notably, the relation between the imaginary part of the complex dielectric value and the frequency of the ...applied A.C. electric field is found to deviate from the universal dielectric response in the frame of classic mechanics. Only the deviation is elucidated based on the model, and thus, a clue to quantum dielectric physics appears.
Malaria is caused by Plasmodium parasites, which are transmitted via the bites of infected Anopheline mosquitoes. Midgut invasion is a major bottleneck for Plasmodium development inside the mosquito ...vectors. Malaria parasites in the midgut are surrounded by a hostile environment rich in digestive enzymes, while a rapidly responding immune system recognizes Plasmodium ookinetes and recruits killing factors from the midgut and surrounding tissues, dramatically reducing the population of invading ookinetes before they can successfully traverse the midgut epithelium. Understanding molecular details of the parasite-vector interactions requires precise measurement of nascent protein synthesis in the mosquito during Plasmodium infection. Current expression profiling primarily monitors alterations in steady-state levels of mRNA, but does not address the equally critical issue of whether the proteins encoded by the mRNAs are actually synthesized.
In this study, we used sucrose density gradient centrifugation to isolate actively translating Anopheles gambiae mRNAs based upon their association with polyribosomes (polysomes). The proportion of individual gene transcripts associated with polysomes, which is determined by RNA deep sequencing, reflects mRNA translational status. This approach led to identification of 1017 mosquito transcripts that were primarily regulated at the translational level after ingestion of Plasmodium falciparum-infected blood. Caspar, a negative regulator of the NF-kappaB transcription factor Rel2, appears to be substantially activated at the translational levels during Plasmodium infection. In addition, transcripts of Dcr1, Dcr2 and Drosha, which are involved in small RNA biosynthesis, exhibited enhanced associations with polysomes after P. falciparum challenge. This observation suggests that mosquito microRNAs may play an important role in reactions against Plasmodium invasion.
We analyzed both total cellular mRNAs and mRNAs that are associated with polysomes to simultaneously monitor transcriptomes and nascent protein synthesis in the mosquito. This approach provides more accurate information regarding the rate of protein synthesis, and identifies some mosquito factors that might have gone unrecognized because expression of these proteins is regulated mainly at the translational level rather than at the transcriptional level after mosquitoes ingest a Plasmodium-infected blood meal.
Influence lines (ILs) and vehicle loads identification are critical in the design, health monitoring, and damage detection of bridges. Traditionally, the approach used in most existing literature has ...been to solve the system of equations directly. However, these approaches require complex calculations such as matrix decomposition and regularization coefficient optimization, making them difficult to implement. In addition, there are difficulties in obtaining accurate axle information and effectively separating the bridge response due to each vehicle. Thus, the improvement of identification algorithms for ILs and multi-vehicle loads remains of significant importance. To address these issues, this paper presents a novel approach that integrates prior physical equations and neural networks. This is achieved by integrating the equation that reflects the relationship between axle loads and bridge response into the neural network, utilizing existing methods for acquiring axle information of vehicles. To validate the effectiveness of the proposed method, it was first applied to theoretical and simulation data. The study then investigated the impact of noise and dynamic effects on the accuracy of the results, as well as the range of the neural network layers and sampling intervals. Finally, the method was implemented for identifying multiple-vehicle loads. The findings of the study confirm the feasibility and numerical stability of the proposed approach. The proposed method eliminates the need for complex computational processes, including matrix decomposition, diagonalization, regularization coefficient optimization, and solution vector smoothing fitting. As a result, the implementation of the algorithm is significantly less challenging, and identification accuracy is improved. It is important to note, however, that the proposed method is relatively more time-consuming due to the iterative learning and training required by the neural network.
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