Concrete strength prediction using artificial neural network and genetic programming

2018 ◽  
Vol 9 (3) ◽  
pp. 75
Author(s):  
Preeti Kulkarni ◽  
Shreenivas N. Londhe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Programming ◽  
Construction Material ◽  
Concrete Strength ◽  
Maximum Size ◽  
Design Parameters ◽  
Ann Model ◽  
Influencing Parameters ◽  
Artificial Neural

Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations.

Numerical Modeling and Artificial Neural Network for Predicting J-Integral of Top-Down Cracking in Asphalt Pavement

2017 ◽  
Vol 2631 (1) ◽  
pp. 83-95 ◽  
Author(s):  
Meng Ling ◽  
Xue Luo ◽  
Sheng Hu ◽  
Fan Gu ◽  
Robert L. Lytton
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Goodness Of Fit ◽  
Design Parameters ◽  
Shear Stresses ◽  
Fracture Parameter ◽  
J Integral ◽  
Ann Model ◽  
Top Down ◽  
Artificial Neural

Top-down cracking (TDC) is recognized as one of the major distress modes in asphalt pavements. This study aimed to determine the fracture parameter J-integral of TDC, which is a critical input to predict the crack growth rate and fatigue life of pavements for this type of distress. Previous research studies demonstrated that TDC is affected by various factors, including the complex state of high tensile or shear stresses induced by the loading at the edge of or within the tire and material properties such as the modulus gradient in the asphalt layer, moduli of the base and subgrade layers, and pavement structures. In this study, the finite element model (FEM) was adopted to simulate the propagation of TDC by considering combinations of these essential factors and to calculate the J-integral for 194,400 cases. It was shown that the modulus gradient plays an important role in determining the J-integral, and the J-integral is not uniformly distributed within the pavement depth. On the basis of the database generated from the FEM, six backpropagation artificial neural network (ANN) models—including one input layer, two hidden layers, and one output layer—were developed by using the same input variables and output variable as those for the FEM. The R2 value for each ANN model was greater than .99, which indicates the goodness of fit. After the parameters of each ANN model have been determined, the J-integral can be predicted for any combination of the design parameters without reconstruction of the FEM.

Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment

2013 ◽  
Vol 465-466 ◽  
pp. 1149-1154
Author(s):  
Ibrahim Masood ◽  
Nadia Zulikha Zainal Abidin ◽  
Nur Rashida Roshidi ◽  
Noor Azlina Rejab ◽  
Mohd Faizal Johari
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Recognition Performance ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Multivariate Quality Control ◽  
Artificial Neural ◽  
Full Factorial

Automated recognition of process variation patterns using an artificial neural network (ANN) model classifier is a useful technique for multivariate quality control. Proper design of the classifier is critical for achieving effective recognition performance (RP). The existing classifiers were mainly designed empirically. In this research, full factorial design of experiment was utilized for investigating the effect of four design parameters, i.e., recognition window size, training data amount, training data quality and hidden neuron amount. The pattern recognition study focuses on bivariate correlated process mean shifts for cross correlation function, ρ = 0.1 ~ 0.9 and mean shifts, μ = ± 0.75 ~ 3.00 standard deviations. Raw data was used as input representation for a generalized model ANN classifier. The findings suggested that: (i) the best performance for each pattern could be achieved by setting different design parameters through specific classifiers, which (ii) gave superior result (average RP = 98.85%) compared to an empirical design (average RP = 96.5%). This research has provided a new perspective in designing ANN pattern recognition scheme in the field of statistical process control.

Geometry Optimization of Cylindrical Flaps of Oscillating Wave Surge Converters Using Artificial Neural Network Models

2019 ◽  
Author(s):  
Chen-Chou Lin ◽  
Yi-Chih Chow ◽  
Yu-Yu Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Geometry Optimization ◽  
Network Models ◽  
Upper And Lower Bounds ◽  
Design Parameters ◽  
Ann Model ◽  
Neural Network Models ◽  
Wave Condition ◽  
Artificial Neural

Abstract This paper presents an optimization algorithm based on the Artificial Neural Network (ANN) to determine the optimal shape, size, and density for the cylindrical flap of the Bottom-Hinged Oscillating Wave Surge Converter (BH-OWSC) that can extract maximal wave power under a given wave condition. Eight parameters are selected, and their upper and lower bounds are set at the initial stage, and then 64 cases with different combinatorial parametric settings are generated by the Design of Experiment process. The 64 cases are then fed into FLOW-3D to simulate the operations of the BH-OWSC under the given wave condition for calculating the capture factor, establishing a database for subsequent ANN data training purpose. To search the maximal capture factor in the specific range of the flap models, we fed 107 random models with various levels of design parameters into the ANN model, which adopts the backpropagation architecture and one hidden layer with ten neuron cells. After three complete random searches, and by simulating the ANN-derived flap’s geometry using FLOW-3D, the result shows that a maximal capture factor of 1.824 can be obtained. The major geometric features of the flap with maximal capture factor are (1) the cylinder axis of the flap inclines to the opposite direction of incident wave propagation, (2) the cylinder’s sectional diameters are about the same size, and (3) the smaller flap density the better power capturing performance.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1448
Author(s):  
Nam-Gyu Lim ◽  
Jae-Yeol Kim ◽  
Seongjun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Battery ◽  
Battery Pack ◽  
Battery Management ◽  
Ann Model ◽  
Battery System ◽  
Load Current ◽  
Circulating Current ◽  
Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

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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