Efficient thermal management is a critical challenge in various engineering configuration where overheating affects performance, such as electronics, industrial cooling, and HVAC applications. Traditional cooling meth...
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Efficient thermal management is a critical challenge in various engineering configuration where overheating affects performance, such as electronics, industrial cooling, and HVAC applications. Traditional cooling methods often struggle with confined enclosures, leading to inefficiencies. Nanofluids and optimized heating mechanisms offer a promising solution, but their complex thermal behavior requires precise predictive modeling. This study addresses this challenge by conducting a numerical analysis of heat transfer in nanofluid-filled enclosures with sinusoidal heating. This study employs multi-expression programming technique to improve thermal performance by analyzing heating design and electromagnetic interactions. In this exploration a square enclosure filled with water-based copper oxide nanofluid is evaluated, featuring a centrally located sinusoidal heated element. The enclosure is also partially heated from below, cooled along the sidewalls, while the upper and remaining lower portions are insulated. The numerical simulation explores flow-controlling variables, including nanoparticles volume fraction, heating element amplitude, magnetic field strength and its orientation, viscous dissipation, and heat generation, to assess their impact on flow dynamics and thermal performance. The findings indicate that the Nusselt number increases by 26.68% when nanoparticle concentration reaches 4%, while a rise in Rayleigh number from 103 to 106 results in an approximate 75.40% increase. Moreover, the average percentage decrease in Nusselt number against Qg from 0 to 30 is 20.71% while for Ha (10 to 100) it is 42.61%.The multi-expression programming model accurately predicts convective heat transfer trends, achieving a high correlation coefficient (CR = 0.99 for training, CR = 0.94 for testing) and low error metrics (RMSE = 0.02, MAE = 0.03, PI = 0.06 for training), ensuring strong agreement with numerical results.
This study emphasizes the expansion of novel relationships to determine maximum compressive stress, corresponding strain, ultimate stress, and strain in order to enhance the precision and practicality of predicting th...
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This study emphasizes the expansion of novel relationships to determine maximum compressive stress, corresponding strain, ultimate stress, and strain in order to enhance the precision and practicality of predicting the behavior of SMA-confined concrete (SMACC) with spirals. It develops predictive equations for the mechanical properties of SMACC cylinders using the multi-expression programming (MEP) method. The MEPX software is employed to derive optimal relationships by collecting experimental data from 42 concrete cylindrical specimens subjected to uniaxial compression and confined with SMA spirals. The findings show that the developed MEP-based relationships not only provide practical equations, but also produce more precise results.
The immense production of plastic waste due to its non-biodegradable nature has become a major issue for the world. Several researchers have recently tried to incorporate plastic waste in building materials, particula...
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The immense production of plastic waste due to its non-biodegradable nature has become a major issue for the world. Several researchers have recently tried to incorporate plastic waste in building materials, particularly as a substitute for cement in concrete, using conventional experimental testing. However, it is difficult to examine the optimal mix design in experimental investigations due to time and resource constraints. Therefore, this study aims to provide robust multi-expression programming (MEP) based predictive equations to evaluate the compressive strength (CS) and tensile strength (TS) of concrete containing plastic waste. Based on the literature, a comprehensive data record of 276 and 235 samples of the CS and TS of plastic concrete was generated for model development. The models ' prediction capabilities were assessed by evaluating the various statistical indicators and comparing the results with a multi -linear regression (MLR) model. Furthermore, a sensitivity analysis was conducted to find out the contribution of each parameter to the CS and TS of plastic concrete. According to the statistical findings, the MEP model demonstrated higher efficacy in prediction, with an R 2 value of 0.87 and 0.89 for the CS and TS models. In addition, MEP generates a simple mathematical formula, which can be employed as a design tool for estimating the CS and TS of plastic concrete. In sensitivity analysis, age demonstrated the highest sensitivity (24.8% and 31%), demonstrating its substantial impact on the model ' s outputs. Cement (17.63% and 17.3%) and plastic (17.4% and 15.3%) have similar contributions to the CS and TS. These results are consistent with previous research, highlighting the agreement between the results of this study and the literature. Therefore, this study can be employed for sustainable construction practices.
An essential factor in the design and in situ performance of asphalt concrete pavements is its elastic-stiffness properties. The objective of the present study, is to develop empirical equations to estimate indirect t...
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An essential factor in the design and in situ performance of asphalt concrete pavements is its elastic-stiffness properties. The objective of the present study, is to develop empirical equations to estimate indirect tensile stiffness modulus (ITSM) of bituminous mixture using the soft computing techniques of multiexpressionprogramming (MEP) and gene expressionprogramming (GEP). The soft computing models were developed using experimental data points containing laboratory and field blended dense-graded hot mix asphalt (HMA) and gap-graded stone matrix asphalt (SMA) mixtures including three asphalt binders. The dominant parameters covered mix physical and volumetric properties, test temperatures, and the mix variables needed during quality control and assurance assessments. The soft computing models could successfully estimate the stiffness modulus with notable values of R values between 0.835 and 0.944. The GEP equations estimated the elastic stiffness modulus with a higher rate of accuracy when compared to the MEP, non-linear and linear regression estimates.
Concrete structures are prone to cracking due to environmental damage and/or tensile strain. Self-healing concrete has garnered scientific interest in recent years as it addresses the cracking problem and extends the ...
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Concrete structures are prone to cracking due to environmental damage and/or tensile strain. Self-healing concrete has garnered scientific interest in recent years as it addresses the cracking problem and extends the service life of concrete structures. Engineered cementitious composite (ECC), a high-performance fiber-reinforced cement composite, is known to have some self-healing characteristics. However, the self-healing capability of ECC is characterized by the type and content of input parameters and is difficult to predict. This investigation utilised a multi-expression programming algorithm for the prediction of self-healing characteristics of ECC containing various admixtures. The database, containing 619 points, was extracted from literature containing crack width before self-healing (CB), limestone powder (LP), silica fume (SF), and fly ash (FA) as input parameters and crack width after self-healing as an output parameter. The performance of the developed model was evaluated based on various statistical indices and parametric analysis. The results showed that the developed model is robust and efficient in predicting the selfhealing behavior of ECC containing various admixtures. Parametric and sensitivity analysis revealed that among the admixtures used, fly ash had the least impact and limestone powder had the highest impact on the self-healing capacity of ECC.(C) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC
This study is attempted to develop the prediction model for the soaked California bearing ratio (CBR) value of fine-grained soil through the multi-expression programming (MEP) approach. The modeling phase was performe...
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This study is attempted to develop the prediction model for the soaked California bearing ratio (CBR) value of fine-grained soil through the multi-expression programming (MEP) approach. The modeling phase was performed on 1011 in situ collected soil samples from an ongoing highway construction project work site. Based on the literature recommendations and present study database analysis, six relevant input parameters were extracted from numerous geotechnical parameters achieved through laboratory experiments. Using those parameters, several tentative combinations were prepared to develop the most efficient predictive model. The comparative results of all the models demonstrate the higher accuracy with PL, PI, S, FC, MDD and OMC as input parameters. The developed model with the adopted input parameters is able to explain 63% variability in the soaked CBR value of fine-grained soil. Almost 97% of total observations were found to be predicted within +/- 20% variations. Additionally, the sensitivity analysis reveals that the soaked CBR value is prominently influenced by the MDD followed by FC, PL, S, PI and OMC. The validation results of the model exhibit that the developed model is also worthy in predicting the soaked CBR value of the unseen dataset. Eventually, a graphical user interface was generated for the future convenience of the researchers and site engineers.
In this paper, a new series of applicable relations, including maximum confined stress and strain as well as the stress-strain relationship, for AFRP confined concrete are proposed based on the design-oriented approac...
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In this paper, a new series of applicable relations, including maximum confined stress and strain as well as the stress-strain relationship, for AFRP confined concrete are proposed based on the design-oriented approach. The main advantages of these new relations are their higher accuracy and unified nature for both circular and square cross sections as well as their ability to predict stress and strain of partially confined columns. For this purpose, a complete database of the available experimental results from the previous studies is collected. In order to achieve higher accuracy and reliability, only part of the collected experimental results that passes a series of deliberately considered criteria are used for derivation of the relations. In addition to the mentioned relations, a threshold for confinement pressure is defined which can be used as a beneficial tool by designers to specify sufficiency of their designed AFRP confinement. Furthermore, a simple relation to predict lateral hoop rupture strain of AFRP wraps is also provided. In order to make the suggested models utilizable for the cases that fibers are not placed perpendicular to the column axis, simple modification factors are also derived. The proposed models are formulated using an evolutionary algorithm named multi-expression programming (MEP), which is an approach for predicting models in the cases of unknown mathematical structures. Accuracy of the proposed relations is compared with the other available models based on the collected experimental database. The obtained results showed that the suggested relations and especially the stress-strain model are capable to predict behavior of AFRP confined concretes with remarkable accuracy.
Waste Foundry sand (WFS), a major solid waste from metal casting industry, is posing a significant environmental threat owing to its disposal to landfills. In this research, an innovative artificial intelligence techn...
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Waste Foundry sand (WFS), a major solid waste from metal casting industry, is posing a significant environmental threat owing to its disposal to landfills. In this research, an innovative artificial intelligence technique i.e. multi-expression programming (MEP) is applied to model the split tensile strength (ST) and modulus of elasticity (E) of concrete containing waste foundry sand (CWFS). The presented formulations correlate mechanical properties with four input variables i.e. w/c, foundry sand content, superplasticizer content and compressive strength. The results of statistical analysis validate the model accuracy as evident by the low values of objective function (0.033 for E and 0.052 for ST). Moreover, the average error in the predicted values is significantly low i.e. 0.287 MPa and 1.75 GPa for ST and E model, respectively. Parametric study depicts that the models are well trained to accurately predict the trends of mechanical properties with variation in mix parameters. The prediction models can promote the usage of WFS in green concrete thereby preventing waste disposal and contributing towards and sustainable construction. (c) 2021 Elsevier B.V. All rights reserved.
Supplier selection problem is a multi-objective problem in which different criteria should be taken into consideration. This article presents a new approach to supplier pre-qualification, supplier selection and evalua...
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Supplier selection problem is a multi-objective problem in which different criteria should be taken into consideration. This article presents a new approach to supplier pre-qualification, supplier selection and evaluation. In the first stage of the model, multi-expression programming (MEP) techniques are used for a supplier pre-qualification. Techniques implemented in MEP allow construction of experiential models using the knowledge contained in the experimental information. Evaluation of the qualified suppliers is done in the second stage using fuzzy logic and Fuzzy Inference System (FIS). In this way, it is possible to retain expert knowledge of the subject phenomenon in a model with the possibility of selecting different operators which lead to the possibility of the faster selection of parameters and making more reliable decisions. Numerical examples are presented to demonstrate the proposed approach.
In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of c...
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In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). Considering the complicated nature of consolidation, an evolutionary computation technique called multi-expression programming was applied to generate several non-piecewise models which are accurate and straightforward enough for different purposes for calculating the degree of consolidation for each depth and its average as well for the whole soil layer. The parametric study was also performed to investigate the impact of each input parameter on the predicted consolidation degree of developed models for each depth. Moreover, the results of the consolidation test carried out on four different clays attained from the literature showed the proper performance of the proposed models. (C) 2019 Elsevier B.V. All rights reserved.
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