scholarly journals Artificial Neural Network Modelling for Asphalt Concrete Samples with Boron Waste Modification

2021 ◽  
Author(s):  
Mustafa Keskin ◽  
◽  
Murat Karacasu ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Asphalt Concrete ◽  
Building Materials ◽  
Mechanical Performance ◽  
Design Method ◽  
Ann Model ◽  
Network Modelling ◽  
Specification Limits ◽  
Artificial Neural

Civil engineering science has evolved into the 21st century with concepts of recycling and sustainability. It is one of the most important goals of this century to create sustainable habitats by evaluating waste materials in building materials. This study aims to eliminate the boron waste dunes that have occurred and continue to occur in our country which has the world's largest boron reserves by using in road materials. Solid boron wastes obtained from the field were crushed and added to asphalt samples in certain ratios and the effect of Crushed Boron Waste (CBW) on asphalt samples were investigated. As a result of Marshall Design Method, it has been proved that boron wastes can be used in asphalt concrete within the specification limits. Besides, an artificial neural network (ANN) model was created for the evaluation of obtained data. As a result of Marshall Design Method, it has been proved that boron wastes can be used in asphalt concrete within the specification limits. Furthermore, examination of modelling and statistical analysis, mechanical performance of asphalt concrete samples with and without CBW addition has been predicted in noticeable manner. As a result of regression analysis, training and test sets r2 values are reached 0.95-0.91 for stability and 0.91-0.87 for flow values. Finally, a simulation was prepared with the created model and the effect of boron wastes on asphalt samples were examined in more detail.

Predictive Artificial Neural Network Laboratory Fatigue Endurance Limit Model for Asphalt Concrete Pavements Based on the Volumetric Properties and Loading Conditions

2021 ◽  
pp. 036119812199965
Author(s):  
Mayzan M. Isied ◽  
Mena I. Souliman ◽  
Waleed A. Zeiada ◽  
Nitish R. Bastola
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Asphalt Concrete ◽  
Endurance Limit ◽  
Volumetric Properties ◽  
Void Content ◽  
Loading Conditions ◽  
Ann Model ◽  
Artificial Neural ◽  
Fatigue Endurance

Asphalt concrete healing is one of the important concepts related to flexible pavement structures. Fatigue endurance limit (FEL) is defined as the strain limit under which no damage will be accumulated in the pavement and is directly related to asphalt healing. Pavement section designed to handle a strain value equivalent to the endurance limit (EL) strain will be considered as a perpetual pavement. All four-point bending beam fatigue testing results from the NCHRP 944-A project were extracted and utilized in the development of artificial neural network (ANN) EL strain predictive model based on mixture volumetric properties and loading conditions. ANN model architecture, as well as the prediction process of the EL strain utilizing the generated model, were presented and explained. Furthermore, a stand-alone equation that predicts the EL strain value was extracted from the developed ANN model utilizing the eclectic approach. Moreover, the EL strain value was predicted utilizing the new equation and compared with the EL strain value predicted by other prediction models available in literature. A total of 705 beam fatigue lab test data points were utilized in model training and evaluation at ratios of 70%, 15%, and 15% for training, testing, and validation, respectively. The developed model is capable of predicting the EL strain value as a function of binder grade, temperature, air void content, asphalt content, SR, failure cycles number, and rest period. The reliability of the developed stand-alone equation and the ANN model was presented by reasonable coefficient of determination (R2) value and significance value (F).

scholarly journals Design and Implementation of ANN Based SCC with GGBS using Auromix 400

2020 ◽  
Vol 9 (2) ◽  
pp. 776-781
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Building Materials ◽  
Mineral Admixture ◽  
Hardened Concrete ◽  
Neural Function ◽  
Ann Model ◽  
Single Output ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Concrete is the main building material. When concrete becomes hard, it gives strength to the structure. Many times it is a difficult task to pour the concrete into the formwork and compact it perfectly. This has been overcome by using Self-Compacting Concrete (SCC). Such a concrete is one of the advanced building materials in the field of construction industry. Unlike the other type of concrete, this kind of concrete compact’s effectively under its own weight. There is no need of any external vibration or compaction procedure to minimal the concrete in formwork. It can easily flow in every corners of the formwork without blocking. This project deals with SCC in which, the binary material used is Ground Granulated Blast Furnace Slag (GGBS) as mineral admixture at various percentage of replacement. To reduce the measure of water used in concrete, Auromix-400 is used as Super Plasticizer at a constant dosage. Several tests were carried out to study the behavior of fresh and hardened concrete. Test for fresh concrete includes slump flow, V Funnel test. Similarly, the properties of concrete were also determined by conducting compression and Spit tensile test. At the same time the simulation model was also developed to test the proposed system using the artificial neural network (ANN) protocol. The ANN model is built on six objects with multiple output-multiple. Single Output Type - In the second method, the artificial neural network model is a single input neural network that is built on top of multiple inputs - where multiple inputs has been predicted separately based on various types of neural function - Secondly, the ANN model is built on multiple inputs. The results indicate the superiority of the neural network method in terms of the accuracy of publication prediction results.

scholarly journals Prediction of the Bicarbonate Amount in Drinking Water in the Region of Médéa Using Artificial Neural Network Modelling

2020 ◽  
Vol 69 (11-12) ◽  
pp. 595-602
Author(s):  
Hichem Tahraoui ◽  
Abd Elmouneïm Belhadj ◽  
Adhya Eddine Hamitouche
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drinking Water ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Mean Square ◽  
Ann Model ◽  
Network Modelling ◽  
Data Set ◽  
Artificial Neural

The region of Médéa (Algeria) located in an agricultural site requires a large amount of drinking water. For this purpose, the water analyses in question are imperative. To examine the evolution of the drinking water quality in this region, firstly, an experimental protocol was done in order to obtain a dataset by taking into account several physicochemical parameters. Secondly, the obtained data set was divided into two parts to form the artificial neural network, where 70 % of the data set was used for training, and the remaining 30 % was also divided into two equal parts: one for testing and the other for validation of the model. The intelligent model obtained was evaluated as a function of the correlation coefficient nearest to 1 and lowest mean square error (RMSE). A set of 84 data points were used in this study. Eighteen parameters in the input layer, five neurons in the hidden layer, and one parameter in the output layer were used for the ANN modelling. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid, and linear transfer function were used, respectively, for the hidden and the output layers. The results obtained during the present study showed a correlation coefficient of <i>R</i> = 0.99276 with root mean square error RMSE = 11.52613 mg dm<sup>–3</sup>. These results show that obtained ANN model gave far better and more significant results. It is obviously more accurate since its relative error is small with a correlation coefficient close to unity. Finally, it can be concluded that obtained model can effectively predict the rate of soluble bicarbonate in drinking water in the Médéa region.

scholarly journals The artificial neural network modelling of the piezoelectric actuator vibrations using laser displacement sensor

2017 ◽  
Vol 68 (5) ◽  
pp. 371-377 ◽  
Author(s):  
Levent Paralı ◽  
Ali Sarı ◽  
Ulaş Kılıç ◽  
Özge Şahin ◽  
Jiří Pěchoušek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Piezoelectric Actuator ◽  
Displacement Sensor ◽  
Laser Displacement Sensor ◽  
Ann Model ◽  
Network Modelling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sine Signal

Abstract We report an improvement of the artificial neural network (ANN) modelling of a piezoelectric actuator vibration based on the experimental data. The controlled vibrations of an actuator were obtained by utilizing the swept-sine signal excitation. The peak value in the displacement signal response was measured by a laser displacement sensor. The piezoelectric actuator was modelled in both linear and nonlinear operating range. A consistency from 90.3 up to 98.9% of ANN modelled output values and experimental ones was reached. The obtained results clearly demonstrate exact linear relationship between the ANN model and experimental values.

scholarly journals Integrated predictive artificial neural network fatigue endurance limit model for asphalt concrete pavements

2019 ◽  
Vol 46 (2) ◽  
pp. 114-123 ◽  
Author(s):  
Mayzan M. Isied ◽  
Mena I. Souliman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Asphalt Concrete ◽  
Endurance Limit ◽  
Laboratory Data ◽  
Limit Strain ◽  
Concrete Pavements ◽  
Ann Model ◽  
Validation Data ◽  
Artificial Neural

Asphalt endurance limit is a strain value if experienced by asphalt pavement layer, no accumulated damage will occur and is directly related to asphalt healing. Therefore, if the pavement experiences this value of strain, or lower, no fatigue damage would accumulate within that pavement section. Beam fatigue test data conducted under the NCHRP Project 9-44A were extracted and utilized to create an artificial neural network predictive model (ANN) to determine the endurance limit strain values for conventional asphalt concrete pavements. The developed ANN model architecture as well as how to utilize it to predict the endurance limit were demonstrated and discussed in detail. Also, a stand-alone equation that is capable in the prediction of the endurance limit strain value, separate from the ANN model environment, was derived utilizing the eclectic extraction approach. The model training and validation data included 934 beam fatigue laboratory data points, as extracted from NCHRP Project 9-44A report. The developed model was able to determine the endurance limit strain value as a function of the stiffness ratio, number of cycles to failure, initial stiffness and rest period, and had a reasonable coefficient of determination (R2) value, which indicates the reliability of both the developed ANN model and the stand-alone equation. Furthermore, a correlation between the endurance limit strain values, as predicted utilizing the generated regression model under the NCHRP project 944-A, and the endurance limit strain values predicted utilizing the stand-alone ANN derived equation was found with a high R2 value.

scholarly journals Application of Hybrid ARIMA and Artificial Neural Network Modelling for Electromagnetic Propagation: An Alternative to the Least Squares Method and ITU Recommendation P.1546-5 for Amazon Urbanized Cities

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ramz L. Fraiha Lopes ◽  
Simone G. C. Fraiha ◽  
Herminio S. Gomes ◽  
Vinicius D. Lima ◽  
Gervasio P. S. Cavalcante
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Least Squares ◽  
Urban Areas ◽  
Least Squares Method ◽  
Moving Average ◽  
Ann Model ◽  
Network Modelling ◽  
International Telecommunication Union ◽  
Artificial Neural

This study sets out an empirical hybrid autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) model designed to estimate electromagnetic wave propagation in densely forested urban areas. Received signal power intensity data was acquired through measurement campaigns carried out in the Metropolitan Area of Belém (MAB), in the Brazilian Amazon. Comparisons were made between estimates from classical least squares (LS) fitting and ITU (International Telecommunication Union) recommendation P. 1546-5. The results indicate the model is, at least, 44% more precise than every ITU estimate and, in some situations, is at least 11% better than an LS estimate, depending on the respective values of the relative error (RE).

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.

Sign in / Sign up

Export Citation Format

Share Document