Rotational demand and capacity of conventional single-plate shear connections subjected to gravity loading

Iranpour, Amin; Hedayat, Amir Ahmad; Afzadi, Ehsan Ahmadi

doi:10.1016/j.engstruct.2019.01.100

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…A natural nervous system is composed of several natural neurons (or nodes) where they receive signals through synapses (or connections) and process them to obtain an output. In recent years, researchers have successfully employed ANN for developing the mathematical models to predict the behavior of systems in different applications 28–31 . In this study, using the neural network toolbox in MATLAB, an ANN‐based model was developed for predicting the CVP value.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, in order to find out the importance of each input parameter in the ANN-based model, a sensitivity analysis was conducted on the basis of the Garson 37 method. This method was also used by other researchers [28][29][30][31] in different applications. In this method, the weights corresponding to the input-hidden and hidden-output of the trained ANN-based model are separated, and the absolute values of the weights are taken to compute the relative importance values.…”

Section: Development Of Ann-based Equation For Predicting the Cvp Valuementioning

confidence: 99%

“…In recent years, researchers have successfully employed ANN for developing the mathematical models to predict the behavior of systems in different applications. [28][29][30][31] In this study, using the neural network toolbox in MATLAB, an ANN-based model was developed for predicting the CVP value. The model was made up of the following three layers: one input layer, one hidden layer and one output layer.…”

Section: Development Of Ann-based Equation For Predicting the Cvp Valuementioning

confidence: 99%

See 2 more Smart Citations

Prediction of the central venous pressure in trauma patients on the basis of non-invasive parameters using artificial neural network

Moinadini

Tajoddini

Hedayat

2019

Hong Kong Journal of Emergency Medicine

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Background: Generally, in traumatic patients, uncontrolled bleeding leads to shock and ultimately death. So, any early detection of shock can reduce the likelihood of a patient’s death. At present, the precise method for estimating the body’s need for fluids is to measure central venous pressure (CVP). However, this method is invasive and time consuming. Objective: This study aimed to predict the central venous pressure value and range of trauma patients through non-invasive parameters such as caval index, lactate clearance, base excess and shock index. Methods: A prospective observational study was performed in 100 trauma patients. Written informed consent was obtained from patient(s) or relatives for their anonymized information to be published in any article. Results: It indicated that parameters such as caval index (at a proposed cutoff point of 35%), lactate clearance (at a cutoff point of 6%) and base excess (at a proposed cutoff point of 6 mmol/L) are approximately of the same level of accuracy for estimating the central venous pressure range, while parameter shock index (at a cutoff point of 1.25) is of the least level of accuracy to predict the central venous pressure range. Results also showed that among all proposed predictive models for estimating the central venous pressure value, which were on the basis of either non-linear regression or artificial neural network, the most accurate model was the one on the basis of the artificial neural network. Among parameters lactate clearance, base excess and shock index used to form the artificial neural network-based model, parameters base excess and lactate clearance were of the highest and lowest level of importance, respectively. Conclusion: Among all proposed models and non-invasive parameters to predict the central venous pressure range, CVPLC model (at a cutoff point of 9), which is a non-linear regression model and is in terms of parameter lactate clearance, was the most accurate model.

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Section: Discussionmentioning

confidence: 99%

Section: Development Of Ann-based Equation For Predicting the Cvp Valuementioning

confidence: 99%

Section: Development Of Ann-based Equation For Predicting the Cvp Valuementioning

confidence: 99%

See 1 more Smart Citation

Prediction of the central venous pressure in trauma patients on the basis of non-invasive parameters using artificial neural network

Moinadini

Tajoddini

Hedayat

2019

Hong Kong Journal of Emergency Medicine

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Machine learning models on the rigidity of welded shear tab connections

Farivar,

Ghassemi,

Yousefian

et al. 2024

Steel Construction

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This article presents an analytical study on the rigidity of shear tab connections, building on a previous study that developed 281 finite element models of varying geometry and size. The initial study proposed an equation to predict shear tab connection behavior based on geometric characteristics. This article further refines those predictions by assessing uncertainties using supervised machine learning (ML) models and proposing improved equations and methods. First, a simplified polynomial regression curve with enhanced statistical metrics is introduced. Additionally, support vector machine (SVM) with feature dimension reduction is used to predict the rigidity of shear tab connections. To identify the best regression model, three algorithms were tested: random forest (RF), XGBoost (XGB), and artificial neural network (ANN), with XGB achieving the lowest error. For classification, four algorithms – SVM, ANN, RF, and CatBoost (CB) – were employed to categorize shear tab behavior as either simple or semirigid. Among these, CB demonstrated the highest accuracy in classifying new test data based on numerical modeling results.

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Elastic Lateral Torsional Buckling Resistance for Continuous Beams Using Artificial Neural Networks

Iranpour

Mohareb

2023

Lecture Notes in Civil Engineering

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Rotational demand and capacity of conventional single-plate shear connections subjected to gravity loading

Cited by 9 publications

References 41 publications

Prediction of the central venous pressure in trauma patients on the basis of non-invasive parameters using artificial neural network

Prediction of the central venous pressure in trauma patients on the basis of non-invasive parameters using artificial neural network

Machine learning models on the rigidity of welded shear tab connections

Elastic Lateral Torsional Buckling Resistance for Continuous Beams Using Artificial Neural Networks

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