Artificial Neural Network (ANN) Models for Prediction of Steel Fibre-Reinforced Concrete Strength

2021 ◽  
pp. 223-230
Author(s):  
A. M. Shende ◽  
K. P. Yadav ◽  
A. M. Pande
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Steel Fibre ◽  
Concrete Strength ◽  
Fibre Reinforced Concrete ◽  
Steel Fibre Reinforced Concrete ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Prediction of mechanical strength of polypropylene fibre reinforced concrete using artificial neural network

2020 ◽  
Vol 63 (4) ◽  
pp. 79-86
Author(s):  
P. Sangeetha ◽  
M. Shanmugapriya
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Offshore Structures ◽  
Polypropylene Fibre ◽  
Fibre Reinforced Concrete ◽  
Curing Period ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The usefulness of fibre reinforced concrete (FRC) in various civil engineering applications is indisputable. Fibre reinforced concrete has been successfully used so far in construction of structures like bridges, industrial structures, concrete, architectural panels, precast products, offshore structures and many other applications. This paper presents the study on the mechanical properties of the polypropylene fibre reinforced concrete. The parameters varied in the study include volume of fibre (0%, 0.5%, 1.0%, 1.5% & 2.0%) and the curing period (7 days and 14 days). From the study it is concluded that the further increases in the volume of fibre reduces the water cement ratio. The mechanical properties of the polypropylene fibre reinforced concrete were also predicted by using Artificial Neural Network (ANN) and found to have minimal error when compared to actual experimental results.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

An investigation on the mortars containing blended cement subjected to elevated temperatures using Artificial Neural Network (ANN) models

2012 ◽  
Vol 10 (6) ◽  
pp. 649-662
Author(s):  
A.A. Ramezanianpour ◽  
M.E. Kamel ◽  
A. Kazemian ◽  
E. Ghiasvand ◽  
H. Shokrani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Elevated Temperatures ◽  
Blended Cement ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Pei-Fang (Jennifer) Tsai ◽  
Po-Chia Chen ◽  
Yen-You Chen ◽  
Hao-Yuan Song ◽  
Hsiu-Mei Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coronary Atherosclerosis ◽  
Inpatient Care ◽  
Length Of Hospital Stay ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Christian Hospital

For hospitals’ admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care. This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan. A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed. Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.

scholarly journals A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Agus Saptoro ◽  
Moses O. Tadé ◽  
Hari Vuthaluru
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Data Representation ◽  
Data Splitting ◽  
Neural Network Models ◽  
Data Division ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations.

scholarly journals Artificial Neural Network (ANN) and Finite Element (FEM) Models for GFRP-Reinforced Concrete Columns under Axial Compression

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7172
Author(s):  
Haytham F. Isleem ◽  
Bassam A. Tayeh ◽  
Wesam Salah Alaloul ◽  
Muhammad Ali Musarat ◽  
Ali Raza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Reinforced Concrete ◽  
Concrete Structures ◽  
Fiber Reinforced Polymer ◽  
Concrete Core ◽  
Reinforced Polymer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In reinforced concrete structures, the fiber-reinforced polymer (FRP) as reinforcing rebars have been widely used. The use of GFRP (glass fiber-reinforced polymer) bars to solve the steel reinforcement corrosion problem in various concrete structures is now well documented in many research studies. Hollow concrete-core columns (HCCs) are used to make a lightweight structure and reduce its cost. However, the use of FRP bars in HCCs has not yet gained an adequate level of confidence due to the lack of laboratory tests and standard design guidelines. Therefore, the present paper numerically and empirically explores the axial compressive behavior of GFRP-reinforced hollow concrete-core columns (HCCs). A total of 60 HCCs were simulated in the current version of Finite Element Analysis (FEA) ABAQUS. The reference finite element model (FEM) was built for a wide range of test variables of HCCs based on 17 specimens experimentally tested by the same group of researchers. All columns of 250 mm outer diameter, 0, 40, 45, 65, 90, 120 mm circular inner-hole diameter, and a height of 1000 mm were built and simulated. The effects of other parameters cover unconfined concrete strength from 21.2 to 44 MPa, the internal confinement (center to center spiral spacing = 50, 100, and 150 mm), and the amount of longitudinal GFRP bars (ρv = 1.78–4.02%). The complex column response was defined by the concrete damaged plastic model (CDPM) and the behavior of the GFRP reinforcement was modeled as a linear-elastic behavior up to failure. The proposed FEM showed an excellent agreement with the tested load-strain responses. Based on the database obtained from the ABAQUS and the laboratory test, different empirical formulas and artificial neural network (ANN) models were further proposed for predicting the softening and hardening behavior of GFRP-RC HCCs.

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