scholarly journals Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

2020 ◽  
Author(s):  
Abambres M ◽  
He J
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Analytical Model ◽  
Analytical Models ◽  
Steel Girders ◽  
Concentrated Load ◽  
Corrugated Web ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Relative Errors

<p>Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%.</p>

scholarly journals Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

2021 ◽  
Author(s):  
Miguel Abambres ◽  
He J
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Analytical Model ◽  
Analytical Models ◽  
Steel Girders ◽  
Concentrated Load ◽  
Corrugated Web ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Relative Errors

<p>Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%.</p>

scholarly journals Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

2018 ◽  
Author(s):  
Miguel Abambres ◽  
Jun He
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Analytical Model ◽  
Analytical Models ◽  
Steel Girders ◽  
Concentrated Load ◽  
Corrugated Web ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Relative Errors

Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%.

scholarly journals Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

2020 ◽  
Author(s):  
Abambres M ◽  
He J
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Analytical Model ◽  
Analytical Models ◽  
Steel Girders ◽  
Concentrated Load ◽  
Corrugated Web ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Relative Errors

<p>Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%.</p>

scholarly journals Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Jun He ◽  
Miguel Abambres
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Analytical Model ◽  
Analytical Models ◽  
Steel Girders ◽  
Concentrated Load ◽  
Corrugated Web ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Relative Errors

Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%.

scholarly journals Neural network-based formula for shear capacity prediction of one-way slabs under concentrated loads

2018 ◽  
Author(s):  
Miguel Abambres ◽  
Eva Olivia Leontien Lantsoght
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Concrete Slab ◽  
Shear Capacity ◽  
Reinforced Concrete Slab ◽  
Concentrated Load ◽  
Reinforced Concrete Slabs ◽  
To Come ◽  
Artificial Neural Network Ann ◽  
Relative Errors

According to the current codes and guidelines, shear assessment of existing reinforced concrete slab bridges sometimes leads to the conclusion that the bridge under consideration has insufficient shear capacity. The calculated shear capacity, however, does not consider the transverse redistribution capacity of slabs, thus leading to overconservative values. This paper proposes an artificial neural network (ANN)-based formula to come up with estimates of the shear capacity of one-way reinforced concrete slabs under a concentrated load, based on 287 test results gathered from the literature. The proposed model yields maximum and mean relative errors of 0.0% for the 287 data points. Moreover, it was illustrated to clearly outperform (mean Vtest / VANN =1.00) the Eurocode 2 provisions (mean VE,EC / VR,c =1.59) for that dataset. A step-by-step assessment scheme for reinforced concrete slab bridges by means of the ANN-based model is also proposed, which results in an improvement of the current assessment procedures.

scholarly journals Neural network-based formula for shear capacity prediction of one-way slabs under concentrated loads

2021 ◽  
Author(s):  
Miguel Abambres ◽  
Lantsoght E
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Concrete Slab ◽  
Shear Capacity ◽  
Reinforced Concrete Slab ◽  
Concentrated Load ◽  
Reinforced Concrete Slabs ◽  
To Come ◽  
Artificial Neural Network Ann ◽  
Relative Errors

<p>According to the current codes and guidelines, shear assessment of existing reinforced concrete slab bridges sometimes leads to the conclusion that the bridge under consideration has insufficient shear capacity. The calculated shear capacity, however, does not consider the transverse redistribution capacity of slabs, thus leading to overconservative values. This paper proposes an artificial neural network (ANN)-based formula to come up with estimates of the shear capacity of one-way reinforced concrete slabs under a concentrated load, based on 287 test results gathered from the literature. The proposed model yields maximum and mean relative errors of 0.0% for the 287 data points. Moreover, it was illustrated to clearly outperform (mean <i>V<sub>test</sub> / V<sub>ANN</sub></i> =1.00) the Eurocode 2 provisions (mean <i>V<sub>E,EC </sub>/ V<sub>R,c</sub></i><sub> </sub>=1.59) for that dataset. A step-by-step assessment scheme for reinforced concrete slab bridges by means of the ANN-based model is also proposed, which results in an improvement of the current assessment procedures.<br></p>

scholarly journals Neural network-based formula for shear capacity prediction of one-way slabs under concentrated loads

2020 ◽  
Author(s):  
Abambres M ◽  
Lantsoght E
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Concrete Slab ◽  
Shear Capacity ◽  
Reinforced Concrete Slab ◽  
Concentrated Load ◽  
Reinforced Concrete Slabs ◽  
To Come ◽  
Artificial Neural Network Ann ◽  
Relative Errors

<p>According to the current codes and guidelines, shear assessment of existing reinforced concrete slab bridges sometimes leads to the conclusion that the bridge under consideration has insufficient shear capacity. The calculated shear capacity, however, does not consider the transverse redistribution capacity of slabs, thus leading to overconservative values. This paper proposes an artificial neural network (ANN)-based formula to come up with estimates of the shear capacity of one-way reinforced concrete slabs under a concentrated load, based on 287 test results gathered from the literature. The proposed model yields maximum and mean relative errors of 0.0% for the 287 data points. Moreover, it was illustrated to clearly outperform (mean <i>V<sub>test</sub> / V<sub>ANN</sub></i> =1.00) the Eurocode 2 provisions (mean <i>V<sub>E,EC </sub>/ V<sub>R,c</sub></i><sub> </sub>=1.59) for that dataset. A step-by-step assessment scheme for reinforced concrete slab bridges by means of the ANN-based model is also proposed, which results in an improvement of the current assessment procedures.<br></p>

scholarly journals Neural network-based formula for shear capacity prediction of one-way slabs under concentrated loads

2020 ◽  
Author(s):  
Abambres M ◽  
Lantsoght E
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Concrete Slab ◽  
Shear Capacity ◽  
Reinforced Concrete Slab ◽  
Concentrated Load ◽  
Reinforced Concrete Slabs ◽  
To Come ◽  
Artificial Neural Network Ann ◽  
Relative Errors

<p>According to the current codes and guidelines, shear assessment of existing reinforced concrete slab bridges sometimes leads to the conclusion that the bridge under consideration has insufficient shear capacity. The calculated shear capacity, however, does not consider the transverse redistribution capacity of slabs, thus leading to overconservative values. This paper proposes an artificial neural network (ANN)-based formula to come up with estimates of the shear capacity of one-way reinforced concrete slabs under a concentrated load, based on 287 test results gathered from the literature. The proposed model yields maximum and mean relative errors of 0.0% for the 287 data points. Moreover, it was illustrated to clearly outperform (mean <i>V<sub>test</sub> / V<sub>ANN</sub></i> =1.00) the Eurocode 2 provisions (mean <i>V<sub>E,EC </sub>/ V<sub>R,c</sub></i><sub> </sub>=1.59) for that dataset. A step-by-step assessment scheme for reinforced concrete slab bridges by means of the ANN-based model is also proposed, which results in an improvement of the current assessment procedures.<br></p>

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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