Evaluating the behaviour of centrally perforated unreinforced masonry walls: Applications of numerical analysis, machine learning, and stochastic methods

Khaleghi, Mohsen; Salimi, Javid; Farhangi, Visar; Moradi, Mohammad Javad; Karakouzian, Moses

doi:10.1016/j.asej.2021.10.026

Cited by 26 publications

(14 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cryogenic gas can be defined as the use of materials at a low temperature (e.g., À150 °C). Mostly, cryogenic gases are tasteless, odorless, colorless and non-toxic gas [102], which are widely used in different industries like electronics, automobiles, aerospace and medical services [112][113][114][115][116][117][118][119][120][121][122][123][124][125]. Cryogenic gases can be hydrogen, neon, oxygen, helium, and nitrogen with a temperature below À180 °C.…”

Section: Cryogenic Machiningmentioning

confidence: 99%

Application of coolants during tool-based machining – A review

Yang

Pramanik

Basak

et al. 2023

Ain Shams Engineering Journal

View full text Add to dashboard Cite

Section: Cryogenic Machiningmentioning

confidence: 99%

Application of coolants during tool-based machining – A review

Yang

Pramanik

Basak

et al. 2023

Ain Shams Engineering Journal

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

“…Immune cell infiltration and is of great concern in the biomedcine ( Shen et al, 2020 ). Traditional CFD methods are often accompanied by complex and time-dependent modeling and calculations ( Khaleghi et al, 2021 ). Based on this, many leukocyte transmigration CFD simulation data obtained in the previous paper, we tentatively established a time-dependent prediction model of the transmigration.…”

Section: Discussionmentioning

confidence: 99%

A Transendothelial Leukocyte Transmigration Model Based on Computational Fluid Dynamics and BP Neural Network

Chi

Yang²,

Liang³

2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

The mechanism of immune infiltration involving immune cells is closely related to various diseases. A key issue in immune infiltration is the transendothelial transmigration of leukocytes. Previous studies have primarily interpreted the leukocyte infiltration of from biomedical perspective. The physical mechanism of leukocyte infiltration remains to be explored. By integrating the immune cell transmigration computational fluid dynamics (CFD) data, the paper builds a time-dependent leukocyte transmigration prediction model based on the bio-inspired methods, namely back propagation neural networks (BPNN) model. The model can efficiently predict the immune cell transmigration in a special microvascular environment, and obtain good prediction accuracy. The model accurately predicted the cell movement and flow field changes during the transmigration. In the test data set, it has high prediction accuracy for cell deformation, motion velocity and flow lift forces during downstream motion, and maintains a good prediction accuracy for drag force. The two prediction models achieved the prediction of leukocyte transmigration in a specific microvascular environment and maintained a high prediction accuracy, indicating the feasibility and robustness of the BPNN model applied to the prediction of immune cell infiltration. Compared with traditional CFD simulations, BPNN models avoid complex and time-dependent physical modeling and computational processes.

show abstract

“…The configuration of an MLP network, along with the learning algorithm and the activation function applied in each neuron, is defined as a network. Implementation of the neural networks may decrease the number of experiments and save time and cost [ 41 , 42 ].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach

et al. 2022

Self Cite

View full text Add to dashboard Cite

Several advantages of supplementary cementitious materials (SCMs) have led to widespread use in the concrete industry. Many various SCMs with different characteristics are used to produce sustainable concrete. Each of these materials has its specific properties and therefore plays a different role in enhancing the mechanical properties of concrete. Multiple and often conflicting demands of concrete properties can be addressed by using combinations of two or more SCMs. Thus, understanding the effect of each SCM, as well as their combination in concrete, may pave the way for further utilization. This study aims to develop a robust and time-saving method based on Machine Learning (ML) to predict the compressive strength of concrete containing binary SCMs at various ages. To do so, a database containing a mixture of design, physical, and chemical properties of pozzolan and age of specimens have been collected from literature. A total of 21 mix design containing binary mixes of fly ash, metakaolin, and zeolite were prepared and experimentally tests to fill the possible gap in the literature and to increase the efficiency and accuracy of the ML-based model. The accuracy of the proposed model was shown to be accurate and ML-based model is able to predict the compressive strength of concrete containing any arbitrary SCMs at ay ages precisely. By using the model, the optimum replacement level of any combination of SCMs, as well as the behavior of binary cementitious systems containing two different SCMs, can be determined.

show abstract

Evaluating the behaviour of centrally perforated unreinforced masonry walls: Applications of numerical analysis, machine learning, and stochastic methods

Cited by 26 publications

References 58 publications

Application of coolants during tool-based machining – A review

Application of coolants during tool-based machining – A review

A Transendothelial Leukocyte Transmigration Model Based on Computational Fluid Dynamics and BP Neural Network

Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach

Contact Info

Product

Resources

About