A practical ANN model for predicting the PSS of two-way reinforced concrete slabs

Tran, Viet‐Linh; Kim, Seung-Eock

doi:10.1007/s00366-020-00944-w

Cited by 45 publications

(15 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, artificial neural network (ANNs) has been successfully applied by Ghaboussi et al [17]. Many authors have used the artificial intelligence (AI) application to predict the shear strength of RC squat walls under cyclic or monotonic loading [18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Programming for Estimating Shear Strength of RC Squat Wall

et al. 2022

View full text Add to dashboard Cite

The flanged, barbell, and rectangular squat reinforced concrete (RC) walls are broadly used in low-rise commercial and highway under and overpasses. The shear strength of squat walls is the major design consideration because of their smaller aspect ratio. Most of the current design codes or available published literature provide separate sets of shear capacity equations for flanged, barbell, and rectangular walls. Also, a substantial scatter exists in the predicted shear capacity due to a large discrepancy in the test data. Thus, this study aims to develop a single gene expression programming (GEP) expression that can be used for predicting the shear strength of these three cross-sectional shapes based on a dataset of 646 experiments. A total of thirteen influencing parameters are identified to contrive this efficient empirical compared to several shear capacity equations. Owing to the larger database, the proposed model shows better performance based on the database analysis results and compared with 9 available empirical models.

show abstract

Section: Introductionmentioning

confidence: 99%

Gene Expression Programming for Estimating Shear Strength of RC Squat Wall

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The test data are used to measure network performance after the training process. Before training the network, the data are scaled in a range of (À1, 1) as recommended by Tran and Kim (2020b). Finally, the best ANN model is retained with 10 neurons in the hidden layer, 80% training data, 10% test data, and 10% validation data.…”

Section: Proposed Artificial Neural Network Modelmentioning

confidence: 99%

Axial compressive behavior of circular concrete-filled double steel tubular short columns

Ahmed

Tran

et al. 2021

Advances in Structural Engineering

Self Cite

View full text Add to dashboard Cite

This article investigates the axial compressive performance of concrete-filled double steel tubular (CFDST) short columns composed of circular section loaded concentrically. An experimental program comprised of compression tests on short columns is carried out to examine their structural performance. Axial compression tests on conventional concrete-filled steel tubular (CFST) columns and double-skin concrete-filled steel tubular (DCFST) columns are also performed for comparison purposes. The test parameters include the diameter-to-thickness of the outer and inner steel tubes, concrete strength, and diameter ratio. The test results exhibit that CFDST short columns composed of the circular section have improved structural performance compared to its CFST and DCFST counterparts. A theoretical model is also presented to simulate the test ultimate strengths and load-axial strain relationships of CFDST columns. The existing design models proposed including the codified design specifications are evaluated against the collected test data for predicting the axial compressive strengths of circular CFDST columns. It is seen that the existing codified design models cannot yield their ultimate axial compressive strengths accurately. A practical artificial neural network (ANN) model is proposed to estimate the ultimate load of such columns loaded concentrically.

show abstract

“…e advantages of this algorithm are solving nonlinear least-squares problems, robustness, and obtaining rapid convergence [51]. is algorithm was also widely used in previous studies [43,46,47,[52][53][54][55]. To assess the ANN models, two indicators, which are the R 2 value and MSE, were quantified.…”

Section: Regression Model Function Type Y Regression Coefficientmentioning

confidence: 99%

A Machine Learning‐Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel

Tran

Nguyen

Phan

et al. 2021

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

The purpose of this study is to develop a practical artificial neural network (ANN) model for predicting the atmospheric corrosion rate of carbon steel. A set of 240 data samples, which are collected from the experimental results of atmospheric corrosion in tropical climate conditions, are utilized to develop the ANN model. Accordingly, seven meteorological and chemical factors of corrosion, namely, the average temperature, the average relative humidity, the total rainfall, the time of wetness, the hours of sunshine, the average chloride ion concentration, and the average sulfur dioxide deposition rate, are used as input variables for the ANN model. Meanwhile, the atmospheric corrosion rate of carbon steel is considered as the output variable. An optimal ANN model with a high coefficient of determination of 0.999 and a small root mean square error of 0.281 mg/m2.month is retained to predict the corrosion rate. Moreover, the sensitivity analysis shows that the rainfall and hours of sunshine are the most influential parameters on predicting the atmospheric corrosion rate, whereas the average chloride ion concentration, the average temperature, and the time of wetness are less sensitive to the atmospheric corrosion rate. An ANN-based formula, which accommodates all input parameters, is thereafter proposed to estimate the atmospheric corrosion rate of carbon steel. Finally, a graphical user interface is developed for calculating the atmospheric corrosion rate of carbon steel in tropical climate conditions.

show abstract

A practical ANN model for predicting the PSS of two-way reinforced concrete slabs

Cited by 45 publications

References 23 publications

Gene Expression Programming for Estimating Shear Strength of RC Squat Wall

Gene Expression Programming for Estimating Shear Strength of RC Squat Wall

Axial compressive behavior of circular concrete-filled double steel tubular short columns

A Machine Learning‐Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel

Contact Info

Product

Resources

About