A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling

Naderpour, Hosein; Eidgahee, Danial Rezazadeh; Fakharian, Pouyan; Rafiean, Amir Hossein; Kalantari, Seyed Meisam

doi:10.1016/j.jestch.2019.05.013

Cited by 44 publications

(15 citation statements)

References 38 publications

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“…e ultimate moment capacity of ferrocement prediction by group method of data handling (GMDH) has higher accuracy when compared to other models [5]. Ferrocement with a chicken mesh having a volume fraction of 3.77% and 30% partial replacement of ne aggregate by steel slag has a greater first crack load and ultimate load when related to other specimens [6].…”

Section: Introductionmentioning

confidence: 99%

Response Surface Methodology Approach to Predict the Flexural Moment of Ferrocement Composites with Weld Mesh and Steel Slag as Partial Replacement for Fine Aggregate

Sridhar¹,

Shinde

Vivek

et al. 2022

Advances in Materials Science and Engineering

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Design of Experiment-Response surface methodology approach is adopted to obtain the optimal flexural moment of ferrocement composites comprising galvanised square weld mesh with weight fraction of fine aggregate by steel slag. To get the optimal combination of progression variables on a flexural moment of ferrocement composites, the central composite design of response surface methodology was adopted. Regression models for responses were justified using analysis of variance and the Pareto chart. The test results show that a maximum ultimate load of 3.30 kN and moment capacity of 220 kNm was obtained for ferrocement with a volume fraction of 2.733% and steel slag of 25% replacement. From the analysis of variance, it is evident that the p value is less than 0.005, the predicted R2 and the adjustable R2 are less than 20%, and the predicted values go in hand with the experimental result which indicates that the proposed models are highly suitable. Moreover, the volume fraction of galvanised square weld mesh has a higher significance on a flexural moment of ferrocement composites. Surface plot, Pareto chart, and regression analysis outcomes show that the most substantial and influential factor for a flexural moment is the volume fraction of galvanised square weld mesh.

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Section: Introductionmentioning

confidence: 99%

Response Surface Methodology Approach to Predict the Flexural Moment of Ferrocement Composites with Weld Mesh and Steel Slag as Partial Replacement for Fine Aggregate

Sridhar¹,

Shinde

Vivek

et al. 2022

Advances in Materials Science and Engineering

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“…The value of the single-layer self-organizing algorithms comprises straightforwardness and their ability to categorize model structure [70,71]. It should be noted that in the GMDH-Combi approach, different combinations of input variable with user-defined-power-orders is considered and apparently a time-consuming calculation process would be formed [71][72][73].…”

Section: Gmdh-combinatorial Predictionmentioning

confidence: 99%

Optimal Compressive Strength of RHA Ultra-High-Performance Lightweight Concrete (UHPLC) and Its Environmental Performance Using Life Cycle Assessment

Onyelowe

Ebid²,

Mahdi³

et al. 2022

Civ Eng J

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Frequent laboratory needs during the production of concrete for infrastructure development purposes are a factor of serious concern for sustainable development. In order to overcome this trend, an intelligent forecast of the concrete properties based on multiple data points collected from various concrete mixes produced and cured under different conditions is adopted. It is equally important to consider the impact of the concrete components in this attempt to take care of the environmental risks involved in this production. In this work, 192 mixes of an ultra-high-performance lightweight concrete (UHPLC) were collected from literature representing different mixes cured under different periods and laboratory conditions. These mix proportions constitute measured variables, which are curing age (A), cement content (C), fine aggregate (FAg), plasticizer (PL), and rice husk ash (RHA). The studied concrete property was the unconfined compressive strength (Fc). This exercise was necessary to reduce multiple dependence on laboratory examinations by proposing concrete strength equations. First, the life cycle assessment evaluation was conducted on the rice husk ash-based UHPLC, and the results from the 192 mixes show that the C-783 mix (87 kg/m3 RHA) has the highest score on the environmental performance evaluation, while C-300 (75 kg/m3 RHA) with life cycle indices of 289.85 kg CO2eq. Global warming potential (GWP), 0.66 kg SO2eq. Terrestrial acidification and 5.77 m3 water consumption was selected to be the optimal choice due to its good profile in the LCA and the Fc associated with the mix. Second, intelligent predictions were conducted by using six algorithms (ANN-BP), (ANN-GRG), (ANN-GA), (GP), (EPR), and (GMDH-Combi). The results show that (ANN-BP) with performance indices of R; 0.989, R2; 0.979, mean square error (MSE); 2252.55, root mean squared error (RMSE); 42.46 MPa and mean absolute percentage error (MAPE); 4.95% outclassed the other five techniques and is selected as the decisive model. However, it also compared well and outclassed previous models, which had used gene expression programming (GEP) and random forest regression (RFR) and achieved R2of 0.96 and 0.91, respectively. Doi: 10.28991/CEJ-2022-08-11-03 Full Text: PDF

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“…is an activation function, and θ j is the bias input to the neuron. For the training or learning process, where the weights are selected, the neural network uses the gradient descent learning method to modify the weights and to minimize the error between the actual output values and the expected values [63]. It stops when the errors are minimized or when another stopping criterion is met.…”

Section: Bpnn Model For Learning Predictionmentioning

confidence: 99%

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

Zhang

2020

Complexity

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Data-driven methods are very useful for streamflow forecasting when the underlying physical relationships are not entirely clear. However, obtaining an accurate data-driven model that is sufficiently performant for streamflow forecasting remains often challenging. This study proposes a new data-driven model that combined the variational mode decomposition (VMD) and the prediction models for daily streamflow forecasting. The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. The ensemble forecast was finally reconstructed by summing. Historical daily streamflow series recorded at the Wushan and Weijiabao hydrologic stations from 1 January 2001 to 31 December 2014 in China were investigated using the proposed VMD-based models. Three quantitative evaluation indexes, including the Nash–Sutcliffe efficiency coefficient (NSE), the root mean square error (RMSE), and the mean absolute error (MAE), were used to evaluate and compare the predicted results of the proposed VMD-based models with two other models such as nondecomposition method (BPNN) and BPNN based on ensemble empirical mode decomposition (EEMD-BPNN). Furthermore, a comparative analysis of the performance of the VMD-BPNN model under different forecast periods (1, 3, 5, and 7 days) was performed. The results evidenced that the proposed VMD-based models could always achieve good performance in the testing stage and had relatively good stability and representativeness. Specifically, the VMD-BPNN model considered both the prediction accuracy and computation efficiency. The results show that the reliability of the forecasting decreased as the foresight period increased. The model performed satisfactorily up to 7-d lead time. The VMD-BPNN model could be applied as a promising, reliable, and robust prediction tool for short-term streamflow forecasting modelling.

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A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling

Cited by 44 publications

References 38 publications

Response Surface Methodology Approach to Predict the Flexural Moment of Ferrocement Composites with Weld Mesh and Steel Slag as Partial Replacement for Fine Aggregate

Response Surface Methodology Approach to Predict the Flexural Moment of Ferrocement Composites with Weld Mesh and Steel Slag as Partial Replacement for Fine Aggregate

Optimal Compressive Strength of RHA Ultra-High-Performance Lightweight Concrete (UHPLC) and Its Environmental Performance Using Life Cycle Assessment

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

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