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.

Prediction of Long-Term Strength of Concrete Based on Artificial Neural Network

2013 ◽  
Vol 357-360 ◽  
pp. 905-908
Author(s):  
Xiao Ming Yang ◽  
Dan Shi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Civil Engineering ◽  
Term Strength ◽  
Engineering Structures ◽  
Cement Ratio ◽  
Civil Engineering Structures ◽  
Trained Neural Network ◽  
Cementing Material

Recently, the safety of existing civil engineering structures attracts more and more attention. The long-term strength of concrete plays a key role during the assessment of safety and durability for civil engineering structures. The strength of concrete will gradually decrease during the service of civil engineering structures. It is significant to accurately predict the strength deterioration of concrete for correctly evaluating the safety of structures. The factors affecting the long-term strength of concrete include environment type, age, climate, water cement ratio, amount of cementing material and so on. In this paper, artificial neural network with powerful mapping ability has been selected to predict the long-term strength of concrete. First, there-layer BP neural network with age, water cement ratio, amount of cementing material as input and long-term strength as output was built. Then, the neural network was trained by the samples measured in real structures and the well-trained neural network was test. From the test results, the trained neural network can accurately predict the long-term strength of concrete with the error less then 9%.

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 Predictive modeling of COVID-19 New Confirmed Cases in Algeria using Artificial Neural Network

2021 ◽  
Author(s):  
Messaoud Djeddou ◽  
Ibrahim A Hameed ◽  
Aouatef Hellal ◽  
Abolfazel Nejatian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Taylor Diagram ◽  
Ann Models ◽  
Artificial Neural ◽  
Predicted Values ◽  
High Level

This study investigates the potential of a simple artificial neural network for the prediction of COVID-19 New Confirmed Cases in Algeria (CNCC). Four different ANN models were built (GRNN, RBFNN, ELM, and MLP). The performance of the predictive models is evaluated based on four numerical parameters, namely root mean squared error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and Pearson correlation coefficient (R). Taylor diagram was also used to examine the similarities and differences between the observed and predicted values obtained from the proposed models. The results showed the potential of the multi-layer perceptron neural network (MLPNN) which exhibited a high level of accuracy in comparison to the other models.

scholarly journals Artificial neural network model for predicting load capacity of driven piles

2021 ◽  
Vol 10 (1) ◽  
pp. e12210111526
Author(s):  
Alcineide Dutra Pessoa ◽  
Gean Carlos Lopes de Sousa ◽  
Rodrigo da Cruz de Araujo ◽  
Gérson Jacques Miranda dos Anjos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Load Capacity ◽  
Pearson Correlation ◽  
Driven Piles ◽  
Load Tests ◽  
Artificial Neural

In geotechnics, several models, empirical or not, have been proposed for the calculation of load capacity in deep foundations. These models run mainly through physical assumptions and construction of approximations by mathematical models. Artificial Neural Networks (ANN), in addition to other applications, are excellent computational mechanisms that, based on biological neural learning, can perform predictions and approximations of functions. In this work, an application of artificial neural networks is presented. The objective here is to propose a mathematical model based on artificial intelligence focused on Artificial Neural Network (ANN) learning capable of predicting the load capacity for driven piles. The results obtained through the neural network were compared with actual values of load capacities obtained in the field through load tests. For this quantitative comparison, the following metrics have been chosen: Pearson correlation coefficient and mean squared error. The database used to carry out the project consisted of 233 load tests, carried out in diverse cities and different countries, for which load capacity, hammer weight, hammer drop height, pile length, pile diameter and pile penetration per blow values ​​were available. These values have been used as input values in a multilayer perceptron neural network to estimate the load capacities of the respective piles. It has been found that the proposed neural model presented, in general, correlation with field values above 90%, reaching 96% in the best result.

scholarly journals Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Van Quan Tran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Water Content ◽  
Unconfined Compressive Strength ◽  
Cement Content ◽  
Ann Model ◽  
Dredged Sediments ◽  
Important Input ◽  
Artificial Neural

Stabilized dredged sediments are used as a backfilling material to reduce construction costs and a solution to environmental protection. Therefore, the compressive strength is an important criterion to determine the stabilized dredged sediments application such as road construction, building construction, and highway construction. Using the traditional method such as empirical approach and experimental methods, the determination of compressive strength of stabilized dredged sediments is difficult due to the complexity of this composite material. In this investigation, the artificial neural network (ANN) model is introduced to forecast the compressive strength. To perform the simulation, 51 experimental datasets were collected from the literature. The dataset consists of 4 input variables (water content, cement content, air foam content, and waste fishing net content) and output variable (compressive strength). Evaluation of the models was made and compared on training dataset (70% data) and testing dataset (30% remaining data) by the criteria of Pearson’s correlation coefficient (R), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results show that the ANN model can accurately predict the compressive strength of stabilized dredged sediments with low water content. The cement content is the most important input affecting the unconfined compressive strength. The important input affecting the unconfined compressive strength can be in the following order: cement content > air foam content > water content > waste fishing net.

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