Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study

Althoey, Fadi; Akhter, Muhammad Naveed; Nagra, Zohaib Sattar; Awan, Hamad Hassan; Alanazi, Fayez; Khan, Mohsin Ali; Javed, Muhammad Faisal; Eldin, Sayed M.; Özkılıç, Yasin Onuralp

doi:10.1016/j.cscm.2022.e01774

Cited by 16 publications

(9 citation statements)

References 87 publications

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“…For further details of the method Frontiers in Materials frontiersin.org considered that have been documented by various researchers, see refs. (Shahzad et al, 2022;AlBaidani et al, 2023;Althoey et al, 2023;Assiri et al, 2023;Sajid et al, 2023b;Rafique et al, 2023). Moreover, the authors have utilized the mentioned numerical method for the following reasons:…”

Section: (24)mentioning

confidence: 99%

The significance of ternary hybrid cross bio-nanofluid model in expanding/contracting cylinder with inclined magnetic field

Alraddadi,

Ayub,

Hussain

et al. 2023

Front. Mater.

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Significance: Bio-nanofluids have achieved rapid attention due to their potential and vital role in various fields like biotechnology and energy, as well as in medicine such as in drug delivery, imaging, providing scaffolds for tissue engineering, and providing suitable environments for cell growth, as well as being used as coolants in various energy systems, wastewater treatment, and delivery of nutrients to plants.Objective: The present study proposes a novel mathematical model for the ternary hybrid cross bio-nanofluid model to analyse the behaviour of blood that passes through a stenosed artery under the influence of an inclined magnetic field. The model considers the effect of expanding/contracting cylinder, infinite shear rate viscosity, and bio-nanofluids.Methodology: The considered model of the problem is bounded in the form of governing equations such as PDEs. These PDEs are transformed into ODEs with the help of similarity transformations and then solved numerically with the help of the bvp4c method.Findings: The results show that the flow rate and velocity decrease as the inclination angle of the magnetic field increases. Additionally, research has found that the presence of nanoparticles in the bio-nanofluid has a significant impact on the velocity and flow rate. Therefore, the flow rate decreases, in general, as the stenosis becomes more severe.Advantages of the study: The results obtained from this study may provide insights into the behaviour of blood flow in stenosed arteries and may be useful in the design of medical devices and therapies for the treatment of cardiovascular diseases.

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Section: (24)mentioning

confidence: 99%

The significance of ternary hybrid cross bio-nanofluid model in expanding/contracting cylinder with inclined magnetic field

Alraddadi,

Ayub,

Hussain

et al. 2023

Front. Mater.

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“…MEP was used by Alavi et al 71 to forecast soil classification established on the liquid limit (LL), plastic limit (PL), and soil color. Similarly, MEP is used for Marshall mix design, flow, and stability 72 – 74 .…”

Section: Introductionmentioning

confidence: 99%

Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming

Khan,

Ali,

Najeh

et al. 2024

Sci Rep

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Bentonite plastic concrete (BPC) demonstrated promising potential for remedial cut-off wall construction to mitigate dam seepage, as it fulfills essential criteria for strength, stiffness, and permeability. High workability and consistency are essential attributes for BPC because it is poured into trenches using a tremie pipe, emphasizing the importance of accurately predicting the slump of BPC. In addition, prediction models offer valuable tools to estimate various strength parameters, enabling adjustments to BPC mixing designs to optimize project construction, leading to cost and time savings. Therefore, this study explores the multi-expression programming (MEP) technique to predict the key characteristics of BPC, such as slump, compressive strength (fc), and elastic modulus (Ec). In the present study, 158, 169, and 111 data points were collected from the experimental studies for the slump, fc, and Ec, respectively. The dataset was divided into three sets: 70% for training, 15% for testing, and another 15% for model validation. The MEP models exhibited excellent accuracy with a correlation coefficient (R) of 0.9999 for slump, 0.9831 for fc, and 0.9300 for Ec. Furthermore, the comparative analysis between MEP models and conventional linear and non-linear regression models revealed remarkable precision in the predictions of the proposed MEP models, surpassing the accuracy of traditional regression methods. SHapley Additive exPlanation analysis indicated that water, cement, and bentonite exert significant influence on slump, with water having the greatest impact on compressive strength, while curing time and cement exhibit a higher influence on elastic modulus. In summary, the application of machine learning algorithms offers the capability to deliver prompt and precise early estimates of BPC properties, thus optimizing the efficiency of construction and design processes.

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“…Most experimental tests use resources, which necessitated the use of an artificial intelligence algorithm to predict parameters. However, caution is required, such as data partitioning, training, and testing to avoid the overfitting/underfitting of results (Matlab, 2004;Althoey et al, 2022). A comparison of experimental with analytical studies to ensure the quality of analytical results has been reported by (Khan et al, 2021) in 2021.…”

Section: Introductionmentioning

confidence: 99%

Finite element, analytical, artificial neural network models for carbon fibre reinforced polymer confined concrete filled steel columns with elliptical cross sections

et al. 2023

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In the present era of architecture, different cross-sectional shapes of structural concrete elements have been utilized. However, this change in shape has a significant effect on load-carrying capacity. To restore this, the use of column confinements with elliptical sections has gained attention. This paper aim to investigate the effect of elliptical shape sections of confined concrete reinforced with Carbon Fiber Reinforced Polymer (CFRP) and steel tube on axial load-carrying capacity. This study is achieved using following tools Finite Element (FE) in Abaqus and Artificial Neural Networks (ANN) modeling. The study involved a 500-mm-high column with three sets of aspect ratios: 1.0, 1.5, and 2.0. In each aspect ratio, three different layers of CFRP were used, i.e., .167, .334, and .501-mm. Analytical results showed that with the increase in aspect ratio from 1 to 2, there is a decrease in ultimate axial load of about 23.2% on average. In addition, the combined confining pressure of steel tube and CFRP increases with a decrease in dilation angle as the number of CFRP layers increases. The failure mode for the column with a large aspect ratio is local buckling at its mid-height along the minor axis. The result showed a good correlation between FE and experimental results of ultimate stress and strains, with a mean squared error of 2.27 and .001, respectively. Moreover, ANN and analytical models showed a delightful correlation of R2 of .97 for stress models and .88 for strain models, respectively. The elliptical concrete section of the column confined with steel tubes can be adopted for a new architectural type of construction; however, with more than three aspect ratios, the wrapping of the section with CFRP jackets is highly recommended.

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Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study

Cited by 16 publications

References 87 publications

The significance of ternary hybrid cross bio-nanofluid model in expanding/contracting cylinder with inclined magnetic field

The significance of ternary hybrid cross bio-nanofluid model in expanding/contracting cylinder with inclined magnetic field

Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming

Finite element, analytical, artificial neural network models for carbon fibre reinforced polymer confined concrete filled steel columns with elliptical cross sections

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