Neural Networks for the Approximate Analysis and Design of Double Layer Grids

Kaveh, A.; Servati, H.

doi:10.1260/026635102760123079

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…Artificial neural networks (ANNs) are information-processing systems based on how biological nervous systems, like the brain, process information [9]. An artificial neural network is a network of interconnected nodes, referred to as artificial neurons, designed to emulate the functioning of the human brain [10,11]. These neurons are linked to each other, facilitating the transmission of signals from one to another.…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction

Sah,

Hong

2024

Materials

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This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree—are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.

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Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction

Sah,

Hong

2024

Materials

View full text Add to dashboard Cite

show abstract

“…Artificial Neural Networks (ANNs) are information processing systems based on how biological nervous systems, like the brain, process information (Mr. Dongare Sadanand, Dr. Sachin Bhosale, 2023). An artificial neural network is a network of interconnected nodes, referred to as artificial neurons, designed to emulate the functioning of the human brain (Chou & Tsai, 2012;Kaveh, 2002). These neurons are linked to each other, facilitating the transmission of signals from one to another.…”

Section: Artificial Neural Network(ann) Modelmentioning

confidence: 99%

Estimation of Concrete Compressive Strength Using Machine Learning

Hong,

Sah

2024

Preprint

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This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—Artificial Neural Network, Multiple Linear Regression, Support Vector Machine, and Regression Tree—are employed and compared for performance. Evaluation metrics such as Mean Absolute Deviation, Root Mean Square Error, Coefficient of Correlation, and Mean Absolute Percentage Error are used. After preprocessing 1030 samples, the dataset is split into 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.

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Size and Geometry Optimization of Double-Layer Grids Using the CBO and ECBO Algorithms

Kaveh¹

2016

Applications of Metaheuristic Optimization Algorithms in Civil Engineering

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Neural Networks for the Approximate Analysis and Design of Double Layer Grids

Cited by 9 publications

References 13 publications

Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction

Performance Comparison of Machine Learning Models for Concrete Compressive Strength Prediction

Estimation of Concrete Compressive Strength Using Machine Learning

Size and Geometry Optimization of Double-Layer Grids Using the CBO and ECBO Algorithms

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