Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

2014 ◽  
Vol 28 (1) ◽  
pp. 73-83 ◽  
Author(s):  
Abozar Nasirahmadi ◽  
Mohammad H. Abbaspour-Fard ◽  
Bagher Emadi ◽  
Nasser Behroozi Khazaei
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Strength Properties ◽  
Milled Rice ◽  
Artificial Neural ◽  
Hidden Layer

Abstract The present investigation deals with analyzing the compressive strength properties of two varieties (Tarom and Fajr) of parboiled paddy and milled rice including: ultimate stress, modulus of elasticity, rupture force and rupture energy. Combined artificial neural network and genetic algorithm were also applied to model these properties. The parboiled samples were prepared with three soaking temperatures (25, 50 and 75°C) and three steaming times (10, 15 and 20 min). The samples were then dried to final moisture contents of 8, 10 and 12% (w.b.). In general, Tarom variety had higher compressive strength properties for paddy and milled rice than Fajr variety. With increase in steaming time from 10 to 20 min, all mentioned properties increased significantly, whereas these properties were decreased with increasing moisture content from 8 to 12% (w.b.). Coupled artificial neural network and genetic algorithm model with one hidden layer, three inputs (soaking temperature, steaming time and moisture content), was developed to predict the compressive strength properties as model outputs. Results indicated that this model could predict these properties with high correlation and low mean squared error.

scholarly journals Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Kraiwut Tuntisukrarom ◽  
Raungrut Cheerarot
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Bottom Ash ◽  
Strength Development ◽  
Ann Model ◽  
Explicit Equation ◽  
Strength Behavior ◽  
Artificial Neural ◽  
Hidden Layer

The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.

scholarly journals Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xin Xiong ◽  
Feng Gao ◽  
Keping Zhou ◽  
Yuxu Gao ◽  
Chun Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Rock Mass ◽  
Ann Model ◽  
Cold Regions ◽  
Freeze Thaw ◽  
Triaxial Compressive Strength ◽  
Artificial Neural

Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model.

scholarly journals Metaheuristic Prediction of the Compressive Strength of Environmentally Friendly Concrete Modified with Eggshell Powder Using the Hybrid ANN-SFL Optimization Algorithm

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6172
Author(s):  
Seyed Vahid Razavi Tosee ◽  
Iman Faridmehr ◽  
Chiara Bedon ◽  
Łukasz Sadowski ◽  
Nasrin Aalimahmoody ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Optimization Algorithm ◽  
Environmentally Friendly ◽  
Shuffled Frog Leaping ◽  
Artificial Neural

The aim of this article is to predict the compressive strength of environmentally friendly concrete modified with eggshell powder. For this purpose, an optimized artificial neural network, combined with a novel metaheuristic shuffled frog leaping optimization algorithm, was employed and compared with a well-known genetic algorithm and multiple linear regression. The presented results confirm that the highest compressive strength (46 MPa on average) can be achieved for mix designs containing 7 to 9% of eggshell powder. This means that the strength increased by 55% when compared to conventional Portland cement-based concrete. The comparative results also show that the proposed artificial neural network, combined with the novel metaheuristic shuffled frog leaping optimization algorithm, offers satisfactory results of compressive strength predictions for concrete modified using eggshell powder concrete. Moreover, it has a higher accuracy than the genetic algorithm and the multiple linear regression. This finding makes the present method useful for construction practice because it enables a concrete mix with a specific compressive strength to be developed based on industrial waste that is locally available.

scholarly journals Prediction of soil unconfined compressive strength using Artificial Neural Network Model

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Hoang-Anh Le ◽  
Thuy-Anh Nguyen ◽  
Duc-Dam Nguyen ◽  
Indra Prakash
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Unconfined Compressive Strength ◽  
Civil Engineering ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
Engineering Structures ◽  
Artificial Neural ◽  
Civil Engineering Structures

The main objective of the present study is to apply Artificial Neural Network (ANN), which is one of the most popular machine learning models, to accurately predict the soil unconfined compressive strength (qu) for the use in designing of foundations of civil engineering structures. For the development of model, data of 118 soil samples were collected from Long Phu 1 power plant project, Soc Trang Province, Vietnam. The database of physicomechanical properties of soils was prepared for the model study, where 70% data was used for the training and 30% for the testing of the model. Standard statistical indices, namely Root Mean Squared Error (RMSE) and Pearson Correlation Coefficient (R) were used in the validation of the model’s performance. In addition, Partial Dependence Plots (PDP) was used to evaluate the importance of the input variables used for modeling. Results showed that the ANN model performed well for the prediction of the qu (RMSE = 0.442 and R = 0.861). The PDP analysis showed that the liquid limit is the most important input factor for modeling of the qu. The present study demonstrated that the ANN is a promising tool that can be used for quick and accurate prediction of the qu, which can be used in designing the civil engineering structures like bridges, buildings, and powerhouses.

scholarly journals Prediksi Inflasi Indonesia Memakai Model ARIMA dan Artificial Neural Network

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Wahyudin S
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analysis Of Variance ◽  
Mean Squared Error ◽  
Statistics Analysis ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Inflasi merupakan indikator makro ekonomi yang sangat penting. Berbagai macam metoda prediksi inflasi Indonesia telah dipublikasikan. Namun pencarian metoda prediksi inflasi yang lebih akurat masih menjadi topik menarik. Pada penulisan ini diusulkan sebuah metoda baru untuk prediksi inflasi memakai model ARIMA dan Artificial Neural Network (ANN). Data inflasi yang digunakan adalah data inflasi bulanan year-on-year dari tahun 2010 sampai dengan tahun 2018 yang diterbitkan oleh Badan Pusat Statistik (BPS). Pertama dibuat 2 model ARIMA yaitu model ARIMA tanpa siklus tahunan dan dengan siklus tahunan. Prosedur standar dan diagostics test telah dilakukan antara lain: summary of statistics, analysis of variance (ANOVA), significance of coefficients test, residuals normality, heterocesdacity, dan stability. Dari hasil perbandingan kinerja memakai Root Mean Squared Error (RMSE) diperoleh bahwa model ARIMA dengan siklus tahunan lebih baik. Model tersebut berupa model ARIMA (2,1,0) (2,0,0) [12]. Kemudian, untuk meningkatkan kinerja prediksi inflasi, ANN telah dibuat berbasis model ARIMA tersebut. Model ANN memakai satu hidden layer dan dua neuron. Hasil pengujian menunjukkan bahwa model ANN menghasilkan RMSE yang lebih kecil daripada model ARIMA (2,1,0) (2,0,0) [12]. Hal ini kemungkinan disebabkan oleh kemampuan mengolah hubungan nonlinear antara variabel target dan variabel penjelas.

scholarly journals A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price

2018 ◽  
Vol 3 (6) ◽  
pp. 10 ◽  
Author(s):  
Azme Bin Khamis ◽  
Phang Hou Yee
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Artificial Neural

The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed.  Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.

Parallel Dynamic Artificial Neural Network for Temperature and Moisture Content Predictions in Microwave-Vacuum Dried Tomato Slices

2007 ◽  
Vol 2 (3) ◽  
Author(s):  
Poonpat Poonnoy ◽  
Ampawan Tansakul ◽  
Manjeet Chinnan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Microwave Power ◽  
Vacuum Pressure ◽  
Ann Model ◽  
Essential Information ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer

Temperature (T) and moisture content (MC) of non-homogenous food undergoing microwave-vacuum (MV) drying (MVD) are directly dependent on microwave power, vacuum pressure, and the product's physical properties. A two-hidden-layer Artificial Neural Network (ANN) model was developed in an earlier study to predict temperature and moisture content of the product at a given time based on the present state of product conditions and process control parameters. This approach either provided lowest error in temperature prediction or in moisture content prediction but not the lowest error in both the prediction parameters simultaneously. The main objective of this work was to improve the performance of the ANN model for temperature and moisture content predictions in MV dried samples. Experimental data obtained from MVD of tomato slices at different drying conditions was normalized and divided into two groups for training and validating. The parallel dynamic ANN model consisted of two double-hidden-layer feed-forward ANN models with varying node numbers (10, 20, and 30). These models were separately trained, simultaneously for moisture content as well as temperature, with the Levenberg-Marquardt algorithm. Inputs for the ANN models were magnetron on-off status, vacuum pressure, temperature, and moisture content at time `ti'. The previous temperature and moisture content data at time `ti-1, i-2, …, i-n' where n = 0, 10, 20, and 30 were also added to the input layer. Outputs from the ANN models were temperature and moisture content at time `ti+1'. The results indicated that the dynamic ANN model working in parallel with the previous temperature and moisture content data provided results that are more accurate and required less training time than those of ordinary ANN models. Model simulation may supply essential information regarding temperature and moisture content of non-homogenous foods corresponding to microwave power and vacuum pressure levels to the predictive control system. Therefore, improved drying efficiencies and thermal damage prevention may be achieved.

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