Application of artificial neural networks to predict the deflections of reinforced concrete beams

Kaczmarek, Mirela; Szymańska, Agnieszka

doi:10.1515/sgem-2016-0017

Cited by 20 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…n , which are averages of the measured (Y i ) and predicted (X i ) outputs, while n is the number of the sample data in the database. To obtain an optimised ANN model, the value of "R" should be highest (approaching 1), while the values of "MSE" and "MAE" should be lowest [30]. To overcome the problem of overfitting, the gradient descent methodology is used to converge values of weights and biases, while, at the same time, early stopping criteria, as defined in the functionality of ANN, is employed to avoid overfitting [31][32][33].…”

Section: The Functionality Of the Ann Modelmentioning

confidence: 99%

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Ahmad,

Elahi,

Barbhuiya

2023

Materials

View full text Add to dashboard Cite

This research aims to conduct a comparative analysis of the first crack load, flexural strength, and shear strength in reinforced concrete beams without stirrups. The comparison is made between the conventional model developed according to the current design code (ACI building code) and an unconventional approach using Artificial Neural Networks (ANNs). To accomplish this, a dataset comprising 110 samples of reinforced concrete beams without stirrup reinforcement was collected and utilised to train a Multilayer Backpropagation Neural Network in MATLAB. The primary objective of this work is to establish a knowledge-based structural analysis model capable of accurately predicting the responses of reinforced concrete structures. The coefficient of determination obtained from this comparison yields values of 0.9404 for the first cracking load, 0.9756 for flexural strength, and 0.9787 for shear strength. Through an assessment of the coefficient of determination and linear regression coefficients, it becomes evident that the ANN model produces results that closely align with those obtained from the conventional model. This demonstrates the ANN’s potential for precise prediction of the structural behaviour of reinforced concrete beams.

show abstract

Section: The Functionality Of the Ann Modelmentioning

confidence: 99%

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Ahmad,

Elahi,

Barbhuiya

2023

Materials

View full text Add to dashboard Cite

show abstract

“…They measured crack width and vertical displacements (deflections) of beams by using IP cameras (sensors). Another method involves using artificial neural networks to predict the deflections of RC beams 14 . The inputs to the model include the surface area of tensile reinforcements, the Young's modulus of the reinforcing steel, the Young's modulus of concrete, and the bending moment of the cross section.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying deflections of reinforced concrete beams under seismic loads by bio‐inspired optimization of deep residual learning

Chou

Karundeng

Truong

et al. 2022

Structural Contr & Hlth

View full text Add to dashboard Cite

Summary The seismic performance of a building must be evaluated after it has been affected by an earthquake load. In the evaluation process, building codes and standards require that the drift of the structure is determined to assess structural performance. This study provides an innovative method that helps engineers in measuring the deflection of reinforced concrete (RC) beams. An imagery deep learning model, called residual networks (ResNet), is used to classify the deflection based on observation by computer vision. However, determining the optimal values of the hyperparameters of this model is a challenge. Therefore, a hybrid model that integrates the bio‐inspired optimization (i.e., jellyfish search [JS] algorithm) and ResNet is developed. The input data that are used to train the model are images that are collected in RC structural experiments. This experiment involved 29 cantilever beams with various RC designs. These specimen RC beams were tested under simulated seismic loads with lateral displacement control. After each load had been applied to the beam, four single‐lens digital cameras captured images from the east, west, north, and south. Then, the performance of computer vision‐based JS–ResNet was evaluated by comparing its accuracy with that of the original ResNet using default hyperparameters. The results of the analysis show that the proposed JS–ResNet model achieves higher accuracy than conventional ResNet. Therefore, the hybrid model can provide insights in similar visual surveillance tasks.

show abstract

“…Several researchers have used the ANN method for many structural engineering studies, such as predicting the compressive strength of concrete [34], axial strength of composite columns [35], and determination of RC building displacement [36]. Kaczmarek and Szyma ńska (2016) [37] concluded that the results of calculating displacement in reinforced concrete using ANN proved to be very effective. Abd et al (2015) [38] concluded that the ANN method is also very good for predicting displacement in concrete beams with a very strong correlation level of 97.27% to the test data.…”

Section: Validation Of Artificial Neural Network (Ann)mentioning

confidence: 99%

The Prediction of Stiffness Reduction Non-Linear Phase in Bamboo Reinforced Concrete Beam Using the Finite Element Method (FEM) and Artificial Neural Networks (ANNs)

Muhtar

2020

Forests

View full text Add to dashboard Cite

This paper discusses the reduction of the stiffness of bamboo reinforced concrete (BRC) beams to support the use of bamboo as an environmentally friendly building material. Calculation of cross-section stiffness in numerical analysis is very important, especially in the non-linear phase. After the initial crack occurs, the stiffness of the cross-section will decrease with increasing load and crack propagation. The calculation of the stiffness in the cross-section of the concrete beam in the non-linear phase is usually approximated by giving a reduction in stiffness. ACI 318-14 provides an alternative, reducing the stiffness of the plastic post-linear beam section through the moment of inertia (I) of the beam section for elastic analysis between 0.50Ig–0.25Ig. This study aims to predict the value of the reduction in the stiffness of the BRC beam section in the non-linear phase through the load-displacement relationship of experimental results validated by the Finite Element Method (FEM) and the Artificial Neural Networks (ANN) method. The experiment used 8 BRC beams and one steel-reinforced concrete (SRC) beam of singly reinforced with a size of 75 mm × 150 mm × 1100 mm. The beams were tested using a four-point loading method. The analysis results showed that the value of the stiffness reduction in the beam cross-sectional in the non-linear phase ranged from 0.5Ig–0.05Ig for BRC beams, and 0.75Ig–0.40Ig for SRC beams.

show abstract

Application of artificial neural networks to predict the deflections of reinforced concrete beams

Cited by 20 publications

References 8 publications

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions

Identifying deflections of reinforced concrete beams under seismic loads by bio‐inspired optimization of deep residual learning

The Prediction of Stiffness Reduction Non-Linear Phase in Bamboo Reinforced Concrete Beam Using the Finite Element Method (FEM) and Artificial Neural Networks (ANNs)

Contact Info

Product

Resources

About