scholarly journals An ANN Model for Predicting the Compressive Strength of Concrete

2021 ◽  
Vol 11 (9) ◽  
pp. 3798
Author(s):  
Chia-Ju Lin ◽  
Nan-Jing Wu
Keyword(s):  
Compressive Strength ◽  
Back Propagation ◽  
Ann Model ◽  
Bp Network ◽  
Mix Proportioning ◽  
Proper Number ◽  
Concrete Mix ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

An artificial neural network (ANN) model for predicting the compressive strength of concrete is established in this study. The Back Propagation (BP) network with one hidden layer is chosen as the structure of the ANN. The database of real concrete mix proportioning listed in earlier research by another author is used for training and testing the ANN. The proper number of neurons in the hidden layer is determined by checking the features of over-fitting while the synaptic weights and the thresholds are finalized by checking the features of over-training. After that, we use experimental data from other papers to verify and validate our ANN model. The final result of the synaptic weights and the thresholds in the ANN are all listed. Therefore, with them, and using the formulae expressed in this article, anyone can predict the compressive strength of concrete according to the mix proportioning on his/her own.

scholarly journals Predicting the Compressive Strength of Concrete Using an RBF-ANN Model

2021 ◽  
Vol 11 (14) ◽  
pp. 6382
Author(s):  
Nan-Jing Wu
Keyword(s):  
Compressive Strength ◽  
Global Minimum ◽  
Shape Parameters ◽  
Ann Model ◽  
Mix Proportioning ◽  
Concrete Mix ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural Network Ann ◽  
The One ◽  
Gradient Approach

In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic gradient approach presented in the textbook is employed for determining the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs have converged to the solutions of global minimum error. So, the only consideration of whether the ANN can work in practical uses is just the issue of over-fitting. The ANN with only three RBFs is finally chosen. The results of verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author’s previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using the formulae expressed in this article, anyone can predict the 28-day compressive strength of concrete according to the concrete mix proportioning on his/her own.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

scholarly journals Prediction of Drape Coefficient by Artificial Neural Network

2015 ◽  
Vol 15 (4) ◽  
pp. 266-274 ◽  
Author(s):  
Adel Ghith ◽  
Thouraya Hamdi ◽  
Faten Fayala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

scholarly journals Model Development for Prediction of Surface Roughness by using of AI Technique

2020 ◽  
pp. 286-292
Author(s):  
Mohd. Tauseef ◽  
Dheeraj Kumar Verma
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Model Development ◽  
Back Propagation ◽  
Ann Model ◽  
Feed Forward Neural Network ◽  
Roughness Prediction ◽  
Overall Performance ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The surface roughness of manufactured product is final results of the turning technique parameters, and an critical characteristics that outline product first-rate, aesthetics etc. It imposes one of the most essential constraints for the choice of machines and slicing parameters in manner planning. In this paper, Artificial Neural Network (ANN) method has been used to develop surface roughness prediction model the use of experimental statistics, wherein Feed Forward Neural Network (FFNN) the usage of Back Propagation set of rules and Levenberg-Marquardt education function has been used. The work has been done using Neural etwork Toolbox in MATLAB. The overall performance of the version has been assessed based totally on Regression analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). A three-2-1 model with two neurons in the hidden layer turned into discovered to be the excellent developed model, having universal regression ( R) cost of zero.9923 and pleasant validation overall performance MSE value of 0.00913. The ANN model confirmed incredible consequences for forecasting

scholarly journals Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Mehdi Nikoo ◽  
Farshid Torabian Moghadam ◽  
Łukasz Sadowski
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
Ann Models ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Different Characteristics ◽  
Mlr Model

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

scholarly journals River Bedup Catchment Water Level Prediction Using Pre-developed ANN Model of Siniawan Catchment

2011 ◽  
Vol 2 (1) ◽  
pp. 36-41
Author(s):  
Bustami R. ◽  
Hong C. ◽  
Lim D.
Keyword(s):  
Water Level ◽  
Back Propagation ◽  
Mathematical Structure ◽  
Gradient Algorithm ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Hourly Data ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Water Level Prediction

This study proposes the application of Artificial Neural Network (ANN) in the prediction of hourly water level under tidal influence for Sadong Basin. An ANN is undoubtedly a robust tool for forecasting various non-linear hydrologic processes, including the water level prediction. It is a flexible mathematical structure which is capable to generalize patterns in imprecise or noisy and ambiguous input and output data sets. In this study, ANN models were developed specifically to forecast the hourly water level for River Bedup Station. Distinctive networks were trained, validated and simulated using hourly data obtained from Department of Irrigation and Drainage, Sarawak in Kuching. The performances of ANN were evaluated based on the coefficient of efficiency, E2 and the coefficient of correlation, R. The back propagation algorithm was adopted for this study. Models used in this study is trained, validated and simulated with scaled conjugate gradient algorithm (trainscg) with two hours of antecedent data, learning rate and the number of neurons in the hidden layer of 0.8 and 40 respectively. In this study, the models generated an accuracy of 100% for all training, validating and simulating stages. It has been found that the ANN has the potential to solve the problems of water level prediction.

scholarly journals Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hai-Van Thi Mai ◽  
Thuy-Anh Nguyen ◽  
Hai-Bang Ly ◽  
Van Quan Tran
Keyword(s):  
Compressive Strength ◽  
Blast Furnace ◽  
Fly Ash ◽  
Blast Furnace Slag ◽  
Machine Learning Algorithms ◽  
Ann Model ◽  
Neuron Network ◽  
Compressive Strength Of Concrete ◽  
Hidden Layer ◽  
Furnace Slag

The prediction accuracy of concrete compressive strength is important and considered a challenging task, aiming at reducing costly and time-consuming experiments. Moreover, compressive strength prediction of concrete using blast-furnace slag (BFS) and fly ash (FA) is more difficult due to the complex mix design of a composition. In this investigation, an approach using the artificial neuron network (ANN), one of the most powerful machine learning algorithms, is applied to predict the compressive strength of concrete containing BFS and FA. The ANN models with one hidden layer containing 13 neuron number cases are proposed to determine the best ANN structure. Under the effect of random sampling strategies and the network structures selected, Monte Carlo simulations (MCS) are introduced to statistically investigate the convergence of results. Next, the evaluation of the model is concluded over 100 simulations for the convergence analysis. The results show that ANN is a highly efficient predictor of the compressive strength using BFS and FA, with maximum values of the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) of 0.9437, 3.9474, and 2.9074, respectively, on the training part and 0.9285, 4.4266, and 3.2971, respectively, for the testing part. The best-defined structure of ANN is [8-24-1], with 24 neurons in the hidden layer. Partial Dependence Plots (PDP) are also performed to investigate the dependence of the prediction results of input variables used in the ANN model. The age of sample and cement content are found to be the two most crucial factors that affect the compressive strength of concrete using BFS and FA. The ANN algorithm is practical for engineers to reduce costly experiments.

scholarly journals Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 485
Author(s):  
Amirreza Kandiri ◽  
Farid Sartipi ◽  
Mahdi Kioumarsi
Keyword(s):  
Compressive Strength ◽  
Optimization Algorithms ◽  
Recycled Aggregate ◽  
Recycled Aggregates ◽  
Tree Model ◽  
Grasshopper Optimization Algorithm ◽  
Compressive Strength Of Concrete ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann ◽  
Industry Needs

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

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.

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