Search

This article presents the research on the mechanical characteristics of concrete in the construction of three concrete bridges. A system of recording the internal temperature of concrete and automatic control of laboratory ovens was used for specimen curing. This allowed the specimens to be cured under conditions similar to those occurring in the structure. Before the construction, reference blocks were used to define similar curing conditions. Maximum setting temperatures ranged from 47.6 °C to 62.0 °C and had a favorable effect on the properties of the concrete at an early age. For concretes with the use of CEM I cement, after 3 days of curing, the strength obtained was up to 8.2 MPa (23%) higher than that for specimens cured under standard conditions. The modulus of elasticity was higher up to 4.9 GPa (21%). For concrete with the use of CEM III cement, these differences were 26.9 MPa (174%) and 10.3 GPa (64%), respectively. After 7 days of curing, the results were close to each other and after 14, 28, and 56 days, higher values were obtained for specimens cured under standard conditions. The value of the modulus of elasticity of concrete was determined using the direct method according to Eurocode and the standard A method. A test load of the bridge was carried out to verify the modulus values obtained from laboratory tests. The highest consistency (99%) between the theoretical deflections and those measured in the test load was achieved when using the stabilized modulus values obtained on specimens cured under structure conditions in the FEM model. The research confirms the necessity of determining the mechanical characteristics of concrete with taking into account the curing conditions of concrete in the structure. A procedure for determining the mechanical properties of concrete for the correct construction of a bridge is proposed. These results can also be used in the development of a digital twin for bridge management. [ABSTRACT FROM AUTHOR]

The study is aimed to virtually miniaturize medical implants produced of the biocompatible Ti with improved mechanical performance. The results on the simulation-driven design of medical implants fabricated of nanostructured commercially pure Ti with significantly enhanced mechanical properties are presented. The microstructure of initially coarse-grained Ti has been refined to ultrafine grain size by severe plastic deformation. The ultrafine-grained (UFG) Ti exhibits remarkably high static and cyclic strength, allowing to design new dental and surgical implants with miniaturized geometry. The possibilities to reduce the implant dimensions via virtual fatigue tests for the digital twins of two particular medical devices (a dental implant and a maxillofacial surgery plate) are explored with the help of finite element modeling. Additionally, the effect of variation in loading direction and the fixation methods for the tested implants are studied in order to investigate the sensitivity of the fatigue test results to the testing conditions. It is shown that the UFG materials are promising for the design of a new generation of medical products. [ABSTRACT FROM AUTHOR]

Digital twin ushers in growth as one cutting-edge technology that enables high-precision, informative and real-time interaction of advanced composites, influencing the paradigm of composite analysis and design in aerospace, automotive, intelligent electronics and other fields. The online and fine evolution of digital twins at scale face challenges due to creating scalable predictions, updates, and controls with coupled information in workflows. In this work, an interactive digital twin methodology for complex composite structures (CCSDT) based on machine learning is introduced. To demonstrate the framework's feasibility and applicability, a hybrid architecture comprising hierarchical deep neural networks (H-DNNs), statistical inference and cross-scale physical constraints is proposed considering the fabric rubber composites (FRC) in aerospace field. The framework predicts 3D displacement and stress fields directly from sensing features and can incorporate dynamic updating and evaluation of computational models into data assimilation. Real-time prediction and uncertainty quantification are verified through synthetic and experimental data, and the capability of CCSDT in perception and decision is demonstrated by a case of cruise state monitoring. The comparison between the measured strain field and the post-processing predicted strain field shows the extensibility of the direct prediction results. These results provide guidance for the development of composite digital twins, stimulating the potential for cost-effective and efficient digital twin services. [Display omitted] [ABSTRACT FROM AUTHOR]

Features of the development of digital twins of thermomechanical steel treatment processes and examples of production tasks solved with their use are considered. The technique of creating digital twins of hot rolling, as well as hot or cold rolling with subsequent heat treatment is described. Adistinctive feature of the digital twin being developed is that in addition to the finite-element model of the process, it must also contain an integral model of structure formation that occurs in the treated steel throughout the process. [ABSTRACT FROM AUTHOR]

Digital twins are undergoing growth that enables highly informative and scaleable interaction between physical objects and virtual twins, which is of great significance to the life cycle analysis of composites. Real-time fine evolution is challenging due to vast combinations of input features and high-resolution calculated variables. Here, we systematically demonstrate an AI-based methodology for digital twinning of complex composite structures. First, three types of deep neural networks are created with optionally used autoencoders as surrogate models, with architectures and data processing inspired by the rule-of-mixture of composites. Second, the prediction accuracy and efficiency are evaluated quantitatively and qualitatively, demonstrating the feasibility of predicting 3D displacement and stress fields directly from sensing data of temperature, pressure and loading displacement, and the optimal architecture is selected to be the evolving digital twin. Finally, the real-time interactive experiments are relayed to the digital twin and demonstrated that it can interact with physical objects and evolve online with high accuracy. These results indicate that the computational time can be reduced by 3∼6 orders of magnitude with high information intensity and scalability compared with conventional numerical and experimental methods, which opens up the avenues for the cost-effective and efficient development of digital twin services for composites. [Display omitted] [ABSTRACT FROM AUTHOR]

The design of a nanotube grouping in a composite at the local scale to form representative volume elements (RVEs) and the resultant stiffness under displacement is investigated due to the challenges of experimentally isolating factors that influence performance in nanotube-reinforced composites (NRCs). Criteria for known nanotube features are considered in this work, including waviness, diameter, and alignment. The influence of the nanotube waviness on the effective modulus is analyzed for CNTs. Waviness has the most significant effect on the NRC, changing the final performance by 20.9%. We evaluate the effect of these parameters on the model's local scale using virtual testing and analysis of variance (ANOVA), which reveal that the waviness factor has the most significant effect on the composite's modulus and give possible explanations for the modulus reduction caused by the presence of highly wavy nanotubes. This work will impact the model-informed manufacturing of future nanocomposite material structures. [Display omitted]. [ABSTRACT FROM AUTHOR]