Influence Of Various Input Parameters Research Articles

Predictive modeling of aramid fiber reinforced polymer confinement for improved compressive strength in circular concrete columns

Fiber Reinforced Polymers (FRPs) have gained significant attention in the field of structural engineering due to their high strength-to-weight ratio, corrosion resistance, and ease of application. Among various types of FRPs, Aramid Fiber Reinforced Polymers (AFRP) stand out for their exceptional tensile strength and durability, making them ideal for confining concrete columns to enhance their compressive strength. This study aims to leverage these benefits by predicting the compressive strength of circular concrete columns confined with AFRP using Artificial Neural Networks (ANNs). To develop and validate the ANN model, a comprehensive dataset consisting of 190 samples was employed during the training phase, while an additional set of 33 samples was used for validation. The performance and predictive capabilities of the ANN model were thoroughly assessed through extensive testing and direct comparison with experimental results, which demonstrated the model’s high accuracy and reliability. Moreover, a detailed parametric study was conducted to examine the influence of various input parameters on the compressive strength prediction. The findings from this study offered significant insights into the effects of various parameters on the predicted outcomes, including column diameter, unconfined concrete strength and strain, as well as AFRP confinement parameters such as thickness, tensile strength, elastic modulus, and strain. Notably, the sensitivity analysis underscored the profound impact of the tensile strength of AFRP on the ANN model's predictive accuracy. The research offers a robust tool for engineers, enabling them to estimate the compressive strength of AFRP-confined circular concrete columns accurately. Additionally, it provides crucial insights that are instrumental in optimizing the design and application of AFRP wrapping or tube-encased methods within the realm of structural engineering. Overall, this research marks a significant step forward in the field of structural engineering, providing a valuable predictive tool and offering insights that can lead to the development of more resilient and sustainable infrastructure.

Application of Taguchi approach to optimize the waste glass powder in developing eco-friendly ternary blended aluminosilicate matrix

This study investigated the effects of incorporating varying proportions of waste glass powder (WGP) and fly ash (FA) on the strength characteristics of ternary blended ground granulated blast furnace slag (GGBS) based geopolymer matrix. The examination was conducted to explore the impact of different levels of substitution of WGP (ranging from 25 % to 40 %) and FA (ranging from 20 % to 35 %) in GGBS-based geopolymer concrete under conditions of ambient curing, employing Taguchi L16 optimization. An investigation into the influence of various input parameters on the compressive strength and workability results was carried out through the utilization of analysis of variance (ANOVA). The analysis based on signal to noise ratio (SNR) revealed that the most effective mix composition consisted of 30 % WGP, 20 % FA, 50 % GGBS, and an 8 M NaOH solution, yielding the highest compressive strength value of 48.3 MPa. Additionally, the examination of the microstructure and morphology of the geopolymer fragments was explored by utilizing scanning electron microscopy (SEM) in conjunction with energy dispersive spectroscopy (EDS), revealing the creation of a densely packed microstructure for the optimal blend.

STUDY OF HEAT AND MASS TRANSFER IN WOOD-COMPOSITE MATERIALS BY MEANS OF COMPUTATIONAL FLUID DYNAMICS

Wood-composite materials (WCM), undergoes complex heat and mass transfer processes during its conditioning. Understanding these interactions is crucial for optimizing the conditioning process and ensuring high quality of the final product. This study focuses on the problem of modeling the interactions that occur during WCM conditioning, taking into account their characteristics that depend on temperature, moisture content, and density of the material. In addition, various input parameters are taken into account in the modeling process to set the optimal conditioning conditions. Despite the availability of these parameters in the literature based on the results of real experiments, consolidating this data in computer modeling is still a major problem. To resolve this issue, this study focuses on modeling the heat and mass transfer processes during WCM cooling using computational fluid dynamics (CFD) software such as SolidWorks Flow Simulation. By using the capabilities of CFD, it is relatively possible to utilize technical parameters involved in the WCM conditioning process with relative simplicity. The paper provides a detailed description of the created 3D model of the WCM-conditioning chamber and its main components. In addition, the paper describes the various input parameters used in the modeling process, including temperature, moisture content, and density of WCM. In addition, the operation of the fans was set up and the initial values of temperature, moisture content, and air velocity were set. Based on the simulation, the results obtained include the distribution of the temperature field and moisture content on the surface of the WCM at different material densities and air conditioning parameters. Additionally, the trajectory of air masses and their velocity within the 3D model was obtained. In general, the obtained results provide a better understanding of the influence of various input parameters on the process of heat and mass transfer in WCM during their cooling. Given the comprehensive analysis, this research this work can be useful for improvement of WCM production processes and quality control of final products. In particular, it emphasizes the importance of further research in this area and the need for computer modeling of such processes to further develop effective engineering solutions.

Forecasting the need for palliative and hospice care using the creeping trend method with segment smoothing.

Aim: To determine the limits of refinement of the forecast of the need for palliative and hospice care (PHC) among adults and children, made by the methods of linear, logarithmic and exponential trends, using the improved forecasting method. Materials and Methods: Based on the calculated demand for 2018-2020, a demand forecast was made using the linear trend method for 2021 and 2022, which was verified by comparing it with the calculation based on available statistical data for 2022. To improve the forecasting result, the creeping trend method with a smoothing segment was used. Results: The estimated need for PHC by the linear trend method for 2022 was 87,254 adults and 46,122 children. The predicted need for this year by the linear trend method was 172,303 for adults and 45,517 for children. The prediction using the sliding trend method with segment smoothing was found to be 4.7 times more accurate and reliable for adults and all age groups combined, but was less accurate and not reliable for children. It was found out that in order to achieve a reliable forecast, it is necessary to clarify the data of medical statistics regarding of malignant neoplasms and congenital malformations, as well as demographic statistics. Conclusions: The method of a creeping trend gave more accurate results and made it possible to determine the reliability of the forecast, allowed to take into account the simultaneous influence of various input parameters.

A refined quasi-3D isogeometric model for dynamic instability of graphene nanoplatelets-reinforced porous sandwich plates

This study pays attention to the dynamic instability behavior of advanced sandwich plate structures subjected to periodic in-plane compressive loads. By utilizing a powerful and efficient computational approach based on a four-variable refined quasi-3D plate theory and NURBS-based isogeometric analysis (IGA), the dynamic behavior of structures can be accurately evaluated while accounting adequately for the thickness stretching effect of normal deformation. To investigate the dynamic instability behavior, this study examines sandwich plate models that consist of two graphene nanoplatelets (GNPs)-reinforced skin layers and a porous core layer with two typical distributions of internal porosity: uniform and symmetric dispersion. Bolotin's method is applied in this study to solve the Mathieu–Hill equation and obtain the boundary of dynamic instability zones. Numerical examples are performed to examine the influence of various input parameters on the instability regions, as well as to provide new insights into the dynamic instability behavior of advanced sandwich plates under compressive loading which has not been comprehensively studied in the literature. The findings of this study suggest that the thickness stretching effect should be carefully evaluated for moderate to thick plate structures, such as sandwich plates.

Global sensitivity analysis of a semi-submersible floating wind turbine using a neural network fitting method

The stochastic platform motions of a floating offshore wind turbine (FOWT) significantly affect its load characteristics and power production. It is vital to understand the influence of various input parameters on the global performance of an FOWT to ensure structural safety and power efficiency. This study investigates the influence of key design parameters related to metocean conditions and natural periods of platform motions on wind turbine performance. A global sensitivity analysis (GSA) framework is proposed to map the input-output relation of a 4-MW semi-submersible FOWT. This framework uses fully-coupled numerical analysis and neural network fitting (NNF). Several important parameters are selected, including natural periods of the platform motions, mean wind speed, turbulence intensity, wave spectral peak period and significant wave height. Results demonstrate that natural periods of platform motions and wave conditions have a significant influence on the dynamic responses of the FOWT. Furthermore, statistical analysis reveals that severe sea states and low natural periods of platform motions can increase the operation cycles of the blade pitch system by 20%. The use of NNF method effectively increases the efficiency of the GSA by at least forty times. The proposed framework can be applied to other areas of FOWTs.

Predicting the Bond Resistance of Glass Fiber Bars in Hinged Beams Employing Enhanced Soft Computing Techniques

One of the principal parameters which affect the behavior of reinforced concrete (RC) elements is the bond of the bar to concrete. Considering the corrosion of steel bars and the widespread use of glass fiber reinforced polymer (GFRP) bars, it is necessary to study the bond between concrete and GFRP rebars. The hinged beam is one of the tests which simulates the relatively actual stress of RC members. In this study, a database containing 116 GFRP reinforced hinged beams with pull-out failure mode has been collected. To predict the bond resistance, Kriging, multivariate adaptive regression splines (MARS), and nonlinear regression techniques are utilized. K-fold cross-validation, sensitivity analysis of input variables, and optimization of tuning parameters for soft computing methods have been used to increase the precision, efficiency, and generality of the proposed models. Also, using the nonlinear regression method, a practical and straightforward relationship is suggested to forecast the bond resistance. The proposed Kriging, MARS, and nonlinear regression models outperformed the other models with determination coefficients of 0.98, 0.96, and 0.78, respectively. Parametric analysis of the variables has also been performed to investigate the influence of various input parameters on the bond resistance of GFRP bars in hinged beams.

Stability Analysis of the Foundation Pit and the Twin Shield Tunnels during Adjacent Construction

Due to the rapid development of urban rail transit and the development and use of underground space, foundation pit construction near existing subway tunnels is becoming increasingly common. When adjacent foundation pit projects are being constructed, it is difficult to avoid the influence on existing subway tunnels, which threatens the safe operation of subway tunnels. Thus, this study focused on the stability of the adjacent construction of foundation pits and twin shield tunnels. The finite element limit analysis (FELA) method was used to analyze the influences of construction scheme and relative position of the twin shield tunnels and adjacent foundation pit on the global stability of adjacent construction. Based on the research results, the guidance and design for the construction scheme and relative position are provided. The stability analysis on the foundation pit during the construction process is performed. The criteria of adjacent influential partition are proposed, and the influence of various input parameters (c/γD, φ, L/D and C/D) on the global stability of adjacent construction is discussed, respectively. A new design equation of safety factor of global stability of adjacent construction is obtained by curve fitting.

Machine learning based quantification of VOC contribution in surface ozone prediction

The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship. This work applies machine learning (ML) algorithms for temporal forecasting of surface ozone over a metropolitan city in India. The availability of continuous VOCs measurement data along with meteorology and other pollutants during 2014–2016 makes it possible to deduce the influence of various input parameters on surface ozone prediction. After evaluating the best ML model for ozone prediction, simulations were carried out using varied input combinations. The combination with isoprene, meteorology, NOx, and CO (Isop + MNC) was the best with RMSE 4.41 ppbv and MAPE 6.77%. A season-wise comparison of simulations having all data, only meteorological data and Isop + MNC as input showed that Isop + MNC simulation gives the best results during the summer season (RMSE: 5.86 ppbv, MAPE: 7.05%). This shows the increased ability of the model to capture ozone peaks (high ozone during summer) relatively better when isoprene data is used. The overall results highlight that using all available data doesn't necessarily give best prediction results; also critical thinking is essential when evaluating the model results.

Effects of a New Type of Grinding Wheel with Multi-Granular Abrasive Grains on Surface Topography Properties after Grinding of Inconel 625.

Finishing operations are one of the most challenging tasks during a manufacturing process, and are responsible for achieving dimensional accuracy of the manufactured parts and the desired surface topography properties. One of the most advanced finishing technologies is grinding. However, typical grinding processes have limitations in the acquired surface topography properties, especially in finishing difficult to cut materials such as Inconel 625. To overcome this limitation, a new type of grinding wheel is proposed. The tool is made up of grains of different sizes, which results in less damage to the work surface and an enhancement in the manufacturing process. In this article, the results of an experimental study of the surface grinding process of Inconel 625 with single-granular and multi-granular wheels are presented. The influence of various input parameters on the roughness parameter (Sa) and surface topography was investigated. Statistical models of the grinding process were developed based on our research. Studies showed that with an increase in the cutting speed, the surface roughness values of the machined samples decreased (Sa = 0.9 μm for a Vc of 33 m/s for a multigranular wheel). Observation of the grinding process showed an unfavorable effect of a low grinding wheel speed on the machined surface. For both conventional and multigranular wheels, the highest value for the Sa parameter was obtained for Vc = 13 m/s. Regarding the surface topography, the observed surfaces did not show defects over large areas in the cases of both wheels. However, a smaller portion of single traces of active abrasive grains was observed in the case of the multi-granular wheel, indicating that this tool performs better finishing operations.

Predicting early‐age stress evolution in restrained concrete by thermo‐chemo‐mechanical model and active ensemble learning

AbstractEarly‐age stress (EAS) is an important index for evaluating the early‐age cracking risk of concrete. This paper encompasses a thermo‐chemo‐mechanical (TCM) model and active ensemble learning (AEL) for predicting the EAS evolution. The TCM model provides the data for the AEL model. First, based on Fourier's law, Arrhenius’ equation, and rate‐type creep law, a TCM model is built to simulate the heat transfer, cement hydration, and viscoelasticity, which together determine the EAS evolution. Then, a material model composed of an eXtreme Gradient Boosting model and adjusted Model Code 2010 is built to allow for parametric study and database construction. Finally, an AEL framework is built, which incorporates principal component analysis (PCA), Gaussian process, and light gradient boosting machine (LGBM). This study resulted in the following findings: (1) The dimensionality of the 672‐by‐1 EAS vector can be effectively reduced by PCA, and the first principal component (PC) is a global index representing the magnitude of the EAS; (2) the mechanical field of the TCM model is validated by testing data. Correlation analysis on the first PC quantifies the influence of various input parameters of the TCM model, which is in accordance with common understandings of the EAS evolution process. (3) The AEL and one‐shot ensemble learning (OSEL) both achieve high prediction performance in the testing set, whose R2 reaches 0.961 and 0.948, respectively. Thanks to the uncertainty‐based query procedure, comparing with OSEL, AEL shows advantages in prediction performance over the whole training history. (4) AEL can significantly reduce the number of samples required for training, which can be a major improvement in efficiency considering the computational cost of the TCM model.

Nonlinear vibration analysis of FGM sandwich structure under thermal loadings

The geometrically nonlinear thermal frequencies of the functionally graded (FG) sandwich structures are predicted numerically in the current work considering the variable temperature distributions (linear and nonlinear). For numerical analysis of the FG sandwich structure, an in-house finite element code has been developed in MATLAB using the higher-order shear deformation theory (HSDT) and Green–Lagrange nonlinear strain kinematics. The governing equation of motion for the graded sandwich structure is obtained using Hamilton’s principles, and the direct iterative method is used to predict the nonlinear vibration response of the sandwich structure. The temperature distributions along the thickness of the sandwich structure are considered. Temperature dependent material properties are considered in the present work for computation of frequency responses under thermal environment. The material properties are described in accordance with the power-law distribution. The current models are initially validated with the published results. The influence of various input parameters, i.e. the curvature ratio (CRO), thickness ratio (TRO), aspect ratio (ARO), boundary conditions, and power-law indices on the nonlinear vibration behaviours of FG sandwich structure have been studied.

Structural reliability of hollow core slabs considering compressive membrane action

Compressive membrane action can considerably improve the load bearing capacity of concrete slabs and beams in case of excessive loaded due to an accidental event. Currently, only limited research has been focusing on compressive membrane action in prestressed concrete elements, or on concrete elements with large cavities, such as precast concrete hollow core slabs. Therefore, a novel real-scale test setup has been developed in order to assess this effect in precast hollow core slabs, and how it can enhance the load-carrying capacity in accidental events. In parallel with these tests, a numerical finite element model has been developed in order to perform a more detailed structural analysis of this phenomenon, and to study the influence of various input parameters. The details of this test setup are briefly explained, and some relevant experimental test results are provided. Considering the experimental findings and validated numerical model, this contribution aims to quantify the influence of compressive membrane action on the structural reliability of precast concrete hollow core slabs. To this end, probabilistic models for the most important material and geometric variables are gathered, and the structural reliability is assessed using Latin Hypercube sampling. Overall, the results indicate that considering the formation of compressive membrane action strongly influences the variability of the ultimate load-carrying capacity of precast concrete hollow core slabs.

Excitation of helical shape argon atmospheric pressure plasma jet using RF pulse modulation

The article reports the excitation of a helical argon atmospheric pressure plasma jet using a pulse-modulated 13.56 MHz radio frequency (RF) power source. This helical structure is observed in open ambient air, which is far different from the conventional conical shape. This helical structure originates due to the periodic pressure variation in the discharge region caused by pulse-modulated RF (2 kHz modulation frequency) and propagates downstream into the ambient air. The geometrical characteristics of the observed structure are explored using optical imaging. Moreover, the influence of various input parameters, viz., duty cycle, gas flow rate, and RF power, of the modulated pulse on the formation of a helical structure are studied. These helical structures have an implication on the plasma jet chemical features (enhancement of reactive oxygen and nitrogen species) as these are involved in an increase in air entrainment into the ionization region desired for various plasma applications.

Simulation of Dynamic Effects in Progressive Die Operation and Control

The demand from the automotive industry for increasingly complex sheet metal components and higher throughput in progressive die operations has led to the increased integration of sensors and control-systems in the sheet metal forming process. However, current control-systems used in sheet metal forming are often limited to measuring the state of the tooling during the forming process, neglecting the dynamic effects of the strip during its transfer between tooling operations. Developing a control strategy that accounts for the strip dynamics requires knowledge of how various process parameters influence the strip behaviour during both the transfer and forming stages. FE element models can accurately model the behaviour of sheet metal, but by themselves cannot identify a robust control strategy. Machine learning can solve this issue by constructing a probabilistic representation for the data generated from FE simulations to be used to identify a control strategy for sheet metal forming. The goal of this work is to conduct a parametric study on a progressive die FE model and evaluate the influence of various input parameters. The data collected from this FEM study will be used to construct a neural network model that will inform a control strategy for a progressive die.

A fuzzy-based expert system to analyse purchase behaviour under uncertain environment

This study develops a Mamdani based Fuzzy inference model to explore the behaviour of customers during purchase of an E-commerce product under an uncertain environment. For the purpose of illustration, product laptop has been considered. The data for this study is primarily collected through questionnaire that involved around 464 participants who are habituated to such online purchase, thus, improving the authenticity of the study. Six such independent input variables like Brand name, Processor speed, RAM capacity, internal storage, Screen size and Graphics are considered in the study. The study proposes Mamdani based Fuzzy inference model that has six inputs and one output. Each input variable is measured on a scale expressed in linguistic terms. For the model, set of all possible rules are generated in the form of antecedent and consequences principle. The proposed model establishes a basis for understanding the influence of various input parameters on the purchase behaviour.

Performance analysis of multi-gap V-roughness with staggered elements of solar air heater based on artificial neural network and experimental investigations.

Among all renewable energy sources, solar power is one of the major sources which contributes for pollution control and protection of environment. For a number of decades, technologies for utilizing the solar power have been the area of research and development. In the current research, thermal performance parameters of multi-gap V-roughness with staggered elements of a solar air heater (SAH) are experimentally investigated. The artificial neural network (ANN) is also utilized for predicting the thermal performance parameters of SAH. Experiments were executed in a rectangular channel with one roughened side at the top exposed to a uniform heat flux. A significant rise in thermal efficiency performance was reported under a predefined range of Reynolds number (Re) from 3000 to 14000 with an optimized value of relative roughness pitch ratio (P/e) and relative staggered rib length (w/g) as 12 and 1, respectively. The maximum thermal efficiency was attained in the range from 42.15 to 87.02% under considered Reynolds numbers for optimum value of P/e as 12 and w/g as 1. A multilayered perceptron (MLP) feed-forward ANN trained by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was utilized to predict the thermal efficiency (ηth), friction (f), and Nusselt number (Nu). The thermal performance parameters such as P/e, w/g, Re, and temperature at the inlet, outlet, and plate were the critical input parameters/signals used in the ANN method. The optimum ANN arrangement/structure to predict the Nu, f, and ηth demonstrate higher accurateness in assessing the performance characteristics of SAH by attaining the root mean squared error (RMSE) in prediction and the Pearson coefficient of association (R2) of 1.591 and 0.994; 0.0012 and 0.851; and 0.025 and 0.981, respectively. The prediction profile plots of the ANN demonstrate the influence of various input parameters on the thermal performance parameters.

Deformation analysis of a construction pit in high-plasticity clay

In Slovakia, construction process already begins in less suitable locations. Such a location is also situated in Podhorie, small village near the city of Žilina. The subsoil is formed by high-plasticity clay with claystone in lower position. This article deals with the analysis of the influence of various input parameters as well as Rinter mesh discretization on the determination of safety factor – M SF. The Mohr-Coulomb constitutive model was used in numerical model with respect to availability of input parameters. The ability to identify the parameter with the biggest impact on the resulting value of safety factor is an indisputable benefit. Various studies have been published on this topic. In our case, cohesion c had the biggest influence on the M SF parameter when reducing shear parameters. In the future, the authors will compare the achieved results of numerical modelling with the actual deformations of the structure.

Influence of near-dry ambiance on WEDM of Monel superalloy

ABSTRACT Progress in the manufacturing sector resulted in the need for products with enhanced mechanical properties, corrosion, and heat resistance. Thus, superalloy was produced with desired mechanical properties. Monel Alloy is one of those nickel-based superalloys, commonly used in industry for its high strength and resistance to corrosion. Monel is also acid- and saltwater resistant. Some properties such as gummy behavior and job hardening make machining superalloys by traditional methods difficult. For this purpose, in this analysis, near-dry wire electric discharge machining (WEDM) is chosen for Monel machining. The present research explores the feasibility of nearly dry Monel alloy WEDM using air and deionized water in nebulous or mist form to achieve the optimal surface finish and resolve environmental issues associated with wet WEDM. This work reflects the study of the influence of various input parameters on the output calculated in terms of the rate of material removal (MRR) and surface roughness (Ra). On an inference, near-dry WEDM formed a smooth surface on Monel alloy over traditional WEDM; however, ranges of MRR still elevate their support to wet WEDM.

Effect of Early Closing of the Inlet Valve on Fuel Consumption and Temperature in a Medium Speed Marine Diesel Engine Cylinder

The energy efficiency and environmental friendliness of medium-speed marine diesel engines are to be improved through the application of various measures and technologies. Special attention will be paid to the reduction in NOx in order to comply with the conditions of the MARPOL Convention, Annex VI. The reduction in NOx emissions will be achieved by the application of primary and secondary measures. The primary measures relate to the process in the engine, while the secondary measures are based on the reduction in NOx emissions through the after-treatment of exhaust gases. Some primary measures such as exhaust gas recirculation, adding water to the fuel or injecting water into the cylinder give good results in reducing NOx emissions, but generally lead to an increase in fuel consumption. In contrast to the aforementioned methods, the use of an earlier inlet valve closure, referred to in the literature as the Miller process, not only reduces NOx emissions, but also increases the efficiency of the engine in conjunction with appropriate turbochargers. A previously developed numerical model to simulate diesel engine operation is used to analyse the effects of the Miller process on engine performance. Although the numerical model cannot completely replace experimental research, it is an effective tool for verifying the influence of various input parameters on engine performance. In this paper, the effect of an earlier closing of the intake valve and an increase in inlet manifold pressure on fuel consumption, pressure and temperature in the engine cylinder under steady-state conditions is analysed. The results obtained with the numerical model show the justification for using the Miller processes to reduce NOx emissions and fuel consumption.