Archives of Civil and Mechanical Engineering最新文献

The hydrothermal dynamical effectiveness and usefulness of highly responsive spinning mechanisms under slanted Hall currents is a significant issue in several manufacturing and experimental functions. Hybridized nanoparticles have novel properties that are advantageous for a range of technical uses. Compared to trihybrid, bihybrid, or mono-nanofluid, tetrahybrid nanofluid (Tetra HNF) is a new idea in research that enables a faster cooling process. These motivate us to research the effects of oblique Hall currents on a non-Newtonian couple stress tetrahybrid nanofluid flow in an oblique channel with oscillatory heating under strong external magnetic attraction with Hall currents in a magneto-gyrating environment. To create tetrahybrid nanofluids (Cu–TiO(_2)–Ag–Al(_2)O(_3)/WEG), copper, titania, silver, and alumina nanopowder forms are dispersed in a colloidal solution of water and ethylene glycol (vol. 60–40(%)). We discuss four kinds of nanoparticles: spheres, bricks, cylinders, and platelets. Mechanical circumstances and presumptions are used to build the partial differential equations (PDEs) that describe the mechanical problems. The dimensionless energy and momentum with related wall constraints are resolved using an analytical approach. Multiple kinds of graphic representations and tabulated data are presented to fully accomplish and demonstrate the mechanical aspects of important developing parameters on the hydrothermal trends and their practical significance. Our results demonstrate that the resultant velocity rapidly rises over growing changes in inclined Hall currents. The velocity profile gets an elevation for the inclination of the channel in the range (pi /4<alpha <pi /2), but reversal flow occurs for a slight angle of inclination ((0<alpha <pi /4)). Platelet-shaped NPs transport higher heat than other shapes (spherical, brick shaped, or cylindrical). Tetrahybrid nanofluid achieves higher heat transport than other base fluid types (pure WEG or mono/bi/trihybrid nanofluids). An artificial neural network (ANN) model is also developed based on testing datasets generated via the analytical evaluation. This ANN architecture achieves an astounding (99.98%) accuracy in predicting critical flow amounts. Our simulations can be applied to the development of reliable oblique Hall sensors and to several manufacturing procedures, including the interaction of nano-polymers and the use of composite nano-lubricants in regulating temperature.

The present study proposes a new fuzzy finite element method for dynamic multibody interaction with consideration for structural damage. Here, fuzzy parameters are equivalently transformed into stochastic parameters using information entropy, and the fuzzy response of the structure is obtained by fuzzy calculation combined with the new point estimation method. Numerical examples are used to illustrate the accuracy and efficiency of the presented methods, and scanning method simulations are implemented to validate the computational results. Considering that the damage degree of the pier is uncertain, namely fuzzy uncertainty, stiffness reduction is used to simulate the damage of the pier. The fuzzy dynamic response of the train–bridge system is investigated when the pier structure and the mass of the train are fuzzy parameters. The response of the train–bridge interaction considering damage far exceeds that obtained from conventional deterministic parameter calculations. To ensure running safety, studying the response of the vehicle-system coupled vibration with fuzzy parameters is of great significance.

The paper presents mathematical models and their implementation in C # language describing the phase transformations occurring in the process of austempered ductile iron (ADI) manufacture. The research includes two main stages: austenitization and isothermal holding at the bainitic range. The influence of free energy of austenite and ferrite on transformations was taken into account. Parameters of models were identified based on inverse analysis and experimental research. As part of the research, verification and validation of the developed models was carried out based on the results of experimental research. The tool developed and implemented enables the analysis of phase transformations occurring during heat treatment with isothermal holding of ductile iron with Ni addition.

To investigate the mechanical properties of glass fiber-reinforced backfills under different proportion conditions, uniaxial compression tests were conducted on glass fiber-reinforced backfills with different slurry concentrations (65%, 68%, and 72%) and different cement–tailings ratios (1:6, 1:8, and 1:10). The effects of slurry concentration and cement–tailings ratio on the mechanical performance parameters, failure modes, and energy evolution of the glass fiber-reinforced backfills were discussed, and the effect mechanism of glass fiber on the overall mechanical properties of the backfills was revealed from a microscopic perspective. The results show that the slurry concentration and cement–tailings ratio have significant effects on the elastic modulus and uniaxial compressive strength of the glass fiber-reinforced backfill. The strength of the backfill reaches a maximum value of 2.831 MPa at a slurry concentration of 72% and a cement–tailings ratio of 1:6. The damage of the glass fiber-reinforced backfill under different proportion conditions first appeared in the central low-strength zone, and then gradually extended to the two ends, eventually leading to the overall failure. As the axial strain increases, the total and dissipated energies of glass fiber-reinforced backfill specimens increase as an exponential function, and the elastic energy increases and then decreases with the peak strain as the node. The bond between the glass fiber and the mortar matrix interface allows the fibers across both sides of the crack to form an “anchoring” effect, thus improving the overall properties of the backfill. The results of the study can promote the application and exploration of glass fiber-reinforced backfills in mine filling and provide some reference for improving the backfill performance.

The rapid increase in the number of electronic products worldwide, in terms of both variety and advanced technology, together with the decrease in costs, has led to the generation of a large amount of electronic waste (e-waste), which has significantly increased environmental pollution. This study was conducted to investigate the hypothesis that the adhesion of polymer binders and plastic origin e-waste will be more effective and stronger, and therefore have a positive effect on the permeability properties of polymer concrete and its behavior against aggressive solutions. For this purpose, quartz aggregates and gravel used as an aggregate in polymer concrete were replaced with 0%, 3%, 6%, 9%, 12% and 15% e-waste. In the study where unsaturated polyester resin was used as a binder, the changes in the permeability properties (capillary water absorption, rapid chloride permeability) of the e-waste polymer concrete and its behavior against aggressive solutions (acid and sulfate attacks) were evaluated after 7, 28 and 90 days. In addition, mechanical experiments were conducted and comparisons were made. After the control concrete, the highest compressive strengths were obtained from the polymer concrete specimens using 3% e-waste, measured as 59.05 MPa, 64.5 MPa and 73.05 MPa after 7, 28 and 90 days, respectively. The research showed that polymer concretes with capillary water absorption coefficient values close to zero after 90 days can be produced with using up to 9% e-waste. The use of e-waste as an aggregate in polymer concrete at 3%, 6% and 9% e-waste, in particular, produced concrete with a high resistance to acid and sulfate attacks. The hypothesis of the study was confirmed after extensive experiments.

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Engineered geopolymer composite (EGC) is considered an excellent innovation in imparting the high ductility to the concrete composites with 100% eco-friendly and sustainable materials. This paper critically reviews the most significant state-of-the-art literature from the early 2010s to till date particularly focused on the individual and combined influence of different kinds of materials. The analysis of different materials performed in this paper includes the different binders, liquids, fine aggregate and fibers involved in the production of EGC by various researchers concerning workability, mechanical and microstructural parameters in which the peculiar influence of various materials towards the production of EGC is critically reviewed. The frequent material combination was identified to be fly ash, sodium activators, polyvinyl alcohol fiber and silica sand in the EGC productions, beyond which several researchers made remarkable accomplishments in EGC such as ultra-high ductility and high compressive strength, and it also summarizes the future challenges of research. Conscientiousness towards the special EGCs such as high strength EGCs and EGC at elevated temperatures are still under research. Inescapable artificial intelligence techniques were also successfully applied by few researchers which is also reviewed in-depth in this paper. Studies revealed that from various material combinations, the tensile strain capacity and compressive strength of 17% and 100 MPa was obtained to be the best. EGC fibers can sustain upto the temperature of 250 °C. Higher fineness of precursors and lower particle size of fine aggregate enhance the effectiveness of EGC. The detailed reviews help the researchers to adopt the optimum materials in the production of EGC based on their requisites and availability which would certainly adhere to the structural applications. It is notable that high-prior research is still required in testing the EGC as a retrofit material by layering and developments of special EGC like self-compacting EGCs.

This study presents the development and validation of a mix design determination procedure for geopolymer concrete to achieve the desired compressive strength. The procedure integrates artificial neural network (ANN) model developed based on a comprehensive data base from literature, data clustering, and parameter optimization techniques to enhance accuracy and reliability. Experimental validation is undertaken to demonstrate the mix design determination procedure’s capability to accurately predict mix designs for geopolymer concrete based on the target compressive strength, validating its efficacy for mix proportion determination. The integration of chemical oxide content in fly ash, curing time, curing temperature, and activator properties results in a 15.9% improvement in prediction accuracy for the training dataset and a 68.3% enhancement for the testing dataset, compared to the base ANN model that includes only the weight of fly ash and activator properties. Employing data clustering techniques enables the identification of prior estimates for the mix design parameters related to specific fly ash types and target compressive strength, streamlining the mix design process by analyzing pertinent data subsets. Parameter optimization ensures refined mix proportions, achieving the desired target strength economically while minimizing material waste and cost. The development of a user interface facilitates easy manipulation of mix designs, catering to users of varying expertise levels. Additional options for deeper insights into geopolymer concrete characteristics can be integrated into the mix design determination procedure. To assess the mix design determination procedure's ability to generalize effectively, a variety of fly ash samples with distinct chemical compositions were utilized, differing from those already present in the database. This approach allows for a thorough evaluation of the mix design determination procedure's performance when presented with fly ash compositions it has not encountered before. By doing so, this provides insights into the adaptability of the mix design determination procedure beyond the limitations of the training and testing datasets.

Products made of clad sheets are a cost-effective alternative to products made entirely of cladding material. The cladding process aims to enhance functional properties, such as corrosion resistance and tribological properties, or modify mechanical properties and conductivity. This publication analyzes the influence of the rolling method on the cold forming ability of explosive welded Ti/steel sheets. Special attention was paid to the quality of the connection between the sheets, as it significantly impacts clad sheet formability. The drawability of these clad sheets was assessed based on the mechanical and technological properties, as well as through microstructural analyses. Experimental analyses revealed that hot rolling of the clad leads to the disappearance of the wave character of the interface and formation in its area of the Frenkel plane and interface layer, which significantly affect the mechanical and technological properties of the analyzed clad. Better cold forming ability, especially in reverse bend test, were obtained for asymmetrically rolled clad, which exhibits greater uniformity of structure.

Drive-by bridge monitoring utilizes measured responses from passing vehicles to perform damage detection of bridge, a methodology challenged by multiple factors and operational conditions. Recently, data-driven methods have been used to improve the accuracy of drive-by monitoring. This thriving research field requires (but lacks) publicly available datasets to improve and validate its monitoring and damage detection capabilities. To foster data-driven drive-by bridge damage assessment methods, this document presents an openly available dataset consisting of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. The dataset includes results for different monitoring scenarios, road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field. In addition, four recently published data-driven drive-by methods have been tested using the same dataset.

A network computational model for a 3D ceramic structure is developed. The model is applied to study the impact of geometric and material parameters of structure on the liquid metal flow through random porous ceramic medium in pressure infiltration processes. The characteristic geometric features of the ceramic structure favorable for liquid metal flow during the infiltration process are determined. The method of structural approximation and constructive homogenization are applied, and the discrete stationary Stokes equations on random graphs are considered. This approach gives a robust algorithm to determine the macroscopic permeability K of interpenetrating phases. The dependencies of K on the distribution of connections (windows) between the cells (inclusions) are derived. The numerical simulations demonstrate that the permeability K does not depend on the scaled distribution sizes of windows. This implies that K is proportional to the mean value of the window areas. The considered model takes into account a random complex structure of 3D ceramic. Hence, it complements the previous study on the local transport properties of tubes (windows) connecting the cells.