Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.

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An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand

Jurnal Teknologi ◽

10.11113/jt.v72.4004 ◽

2015 ◽

Vol 72 (3) ◽

Cited By ~ 1

Author(s):

Ramli Nazir ◽

Ehsan Momeni ◽

Kadir Marsono ◽

Harnedi Maizir

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Bearing Capacity ◽

Predictive Model ◽

Linear Regression Analysis ◽

Load Test ◽

Coefficient Of Determination ◽

Ann Model ◽

Cohesionless Soils ◽

Artificial Neural

This study highlights the application of Back-Propagation (BP) feed forward Artificial Neural Network (ANN) as a tool for predicting bearing capacity of spread foundations in cohesionless soils. For network construction, a database of 75 recorded cases of full-scale axial compression load test on spread foundations in cohesionless soils was compiled from literatures. The database presents information about footing length (L), footing width (B), embedded depth of the footing (Df), average vertical effective stress of the soil at B/2 below footing (s΄), friction angle of the soil (f) and the ultimate axial bearing capacity (Qu). The last parameter was set as the desired output in the ANN model, while the rest were used as input of the ANN predictive model of bearing capacity. The prediction performance of ANN model was compared to that of Multi-Linear Regression analysis. Findings show that the proposed ANN model is a suitable tool for predicting bearing capacity of spread foundations. Coefficient of determination R2 equals to 0.98, strongly indicates that the ANN model exhibits a high degree of accuracy in predicting the axial bearing capacity of spread foundation. Using sensitivity analysis, it is concluded that the geometrical properties of the spread foundations (B and L) are the most influential parameters in the proposed predictive model of Qu.

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Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity

PLoS ONE ◽

10.1371/journal.pone.0243030 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243030

Author(s):

Tuan Anh Pham ◽

Van Quan Tran ◽

Huong-Lan Thi Vu ◽

Hai-Bang Ly

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Bearing Capacity ◽

Hybrid Model ◽

Network Architecture ◽

Mean Squared Error ◽

Load Test ◽

Coefficient Of Determination ◽

Foundation Design ◽

Driven Pile

Determination of pile bearing capacity is essential in pile foundation design. This study focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network (DLNN) algorithm to predict the bearing capacity of driven pile. For this purpose, a Genetic Algorithm (GA) was developed to select the most significant features in the raw dataset. After that, a GA-DLNN hybrid model was developed to select optimal parameters for the DLNN model, including: network algorithm, activation function for hidden neurons, number of hidden layers, and the number of neurons in each hidden layer. A database containing 472 driven pile static load test reports was used. The dataset was divided into three parts, namely the training set (60%), validation (20%) and testing set (20%) for the construction, validation and testing phases of the proposed model, respectively. Various quality assessment criteria, namely the coefficient of determination (R2), Index of Agreement (IA), mean absolute error (MAE) and root mean squared error (RMSE), were used to evaluate the performance of the machine learning (ML) algorithms. The GA-DLNN hybrid model was shown to exhibit the ability to find the most optimal set of parameters for the prediction process.The results showed that the performance of the hybrid model using only the most critical features gave the highest accuracy, compared with those obtained by the hybrid model using all input variables.

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Towards building a neural network model for predicting pile static load test curves

MATEC Web of Conferences ◽

10.1051/matecconf/201814902031 ◽

2018 ◽

Vol 149 ◽

pp. 02031

Author(s):

A. K. Alzo’ubi ◽

Farid Ibrahim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Static Load ◽

Load Test ◽

Static Load Test ◽

Deep Foundation ◽

Suggested Approach ◽

Artificial Neural

In the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behaviour, the Static Load Test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. In this paper we will utilize the Artificial Neural Network approach to build a model that can predict a complete Static Load Pile test. We will show that by integrating the pile configuration, soil properties, and ground water table in one artificial neural network model, the Static Load Test can be predicted with confidence. We believe that based on this approach, the model is able to predict the entire pile load test from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.

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Application of Artificial Neural Network Over Nickel-Based Catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM)

Journal of Nanoscience and Nanotechnology ◽

10.1166/jnn.2020.17627 ◽

2020 ◽

Vol 20 (9) ◽

pp. 5716-5719 ◽

Cited By ~ 1

Author(s):

Cho Hwe Kim ◽

Young Chul Kim

Keyword(s):

Neural Network ◽

Carbon Dioxide ◽

Artificial Neural Network ◽

Mean Squared Error ◽

Coefficient Of Determination ◽

Feed Ratio ◽

Squared Error ◽

Highly Nonlinear ◽

Feed Forward Network ◽

Artificial Neural

The application of artificial neural network (ANN) for modeling, combined steam-carbon dioxide reforming of methane over nickel-based catalysts, was investigated. The artificial neural network model consisted of a 3-layer feed forward network, with hyperbolic tangent function. The number of hidden neurons is optimized by minimization of mean square error and maximization of R2 (R square, coefficient of determination) and set of 8 neurons. With feed ratio, flow rate, and temperature as independent variables, methane, carbon dioxide conversion, and H2/CO ratio, were measured using artificial neural network. Coefficient of determination (R2) values of 0.9997, 0.9962, and 0.9985 obtained, and MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error) showed low value. This study indicates ANN can successfully model a highly nonlinear process and function.

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Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles

Earth Sciences Research Journal ◽

10.15446/esrj.v19n1.38712 ◽

2015 ◽

Vol 19 (1) ◽

pp. 85-93 ◽

Cited By ~ 56

Author(s):

Ehsan Momeni ◽

Ramli Nazir ◽

Danial Jahed Armaghani ◽

Harnedi Maizir

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Back Propagation ◽

Load Test ◽

Static Load Test ◽

Concrete Piles ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Artificial Neural Network Ann

Axial bearing capacity (ABC) of piles is usually determined by static load test (SLT). However, conducting SLT is costly and time-consuming. High strain dynamic pile testing (HSDPT) which is provided by pile driving analyzer (PDA) is a more recent approach for predicting the ABC of piles. In comparison to SLT, PDA test is quick and economical. Implementing feed forward back-propagation artificial neural network (ANN) for solving geotechnical problems has recently gained attention mainly due to its ability in finding complex nonlinear relationships among different parameters. In this study, an ANN-based predictive model for estimating ABC of piles and its distribution is proposed. For network construction purpose, 36 PDA tests were performed on various concrete piles in different project sites. The PDA results, pile geometrical characteristics as well as soil investigation data were used for training the ANN models. Findings indicate the feasibility of ANN in predicting ultimate, shaft and tip bearing resistances of piles. The coefficients of determination, R², equal to 0.941, 0.936, and 0.951 for testing data reveal that the shaft, tip and ultimate bearing capacities of piles predicted by ANN-based model are in close agreement with those of HSDPT. By using sensitivity analysis, it was found that the length and area of the piles are dominant factors in the proposed predictive model. ResumenLa Capacidad Axial de Soporte (ABC, en inglés) de un pilote de construcción se determina usualmente a través de una Prueba de Carga Estática (SLT, inglés). Sin embargo, estas pruebas son costosas y demandan tiempo. La evaluación de las Dinámicas de Alto Esfuerzo de Pilotes (HSDPT, inglés), que la provee el programa de Análisis de Excavación (PDA, inglés), es una forma de aproximación más reciente para preveer la Capacidad Axial de Soporte. En comparación con la Prueba de Cargas Estática, la evaluación PDA es rápida y económica. La implementación de Redes Neuronales Arficiales (ANN, en inglés) que permita resolver problemas geotécnicos ha ganado atención recientemente debido a su posibilidad de hallar relaciones no lineales entre los diferentes parámetros. En este estudio se propone un modelo predictivo ANN para estimar la Capacidad Axial de Soporte de pilotes y su distribución. Para fines de una red de construcción se realizaron 36 pruebas PDA en pilotes de diferentes proyectos. Los resultados de los Análisis de Excavación, las características geométricas de los pilotes, al igual que los datos de investigación del suelo se utilizaron para probar los modelos ANN. Los resultados indican la viabilidad del modelo ANN en predecir la resistencia de los pilotes. Los coeficientes de correlación, R², que alcanzaron 0.941, 09.36 y 0.951 para la evaluación de los datos, revelan que la capacidad del pilotaje en el último rodamiento, en el cojinete del eje y en la punta que se predijeron con el modelo ANN concuerda con las establecidas a través del HSDPT. A través del análisis de respuesta se determinó que la longitud y el área de los pilotes son factores dominantes en el modelo predictivo propuesto.

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Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study

Water Practice & Technology ◽

10.2166/wpt.2021.012 ◽

2021 ◽

Author(s):

Nabeel H. Al-Saati ◽

Isam I. Omran ◽

Alaa Ali Salman ◽

Zainab Al-Saati ◽

Khalid S. Hashim

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Mean Squared Error ◽

Moving Average ◽

Stationary Time Series ◽

Coefficient Of Determination ◽

Neural Network Models ◽

Monthly Streamflow ◽

Artificial Neural

Abstract Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination R2 = 0.801), while the (NAR) model gave (RMSE = 93.4) and R2 = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity.

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Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network

Advances in Materials Science and Engineering ◽

10.1155/2019/4654070 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

S. Karthiyaini ◽

K. Senthamaraikannan ◽

J. Priyadarshini ◽

Kamal Gupta ◽

M. Shanmugasundaram

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Analysis ◽

Mechanical Strength ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Mean Squared Error ◽

Fiber Reinforced Concrete ◽

Coefficient Of Determination ◽

Artificial Neural

The present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28 days. The model uses the data from early literatures; the data consist of tensile strength of fiber, percentage of fiber, water/cement ratio, cross-sectional area of test specimen, Young’s modulus of fiber, and mechanical strength of control specimen, and these were used as the input parameters; the respective strength attained was used as the target parameter. The models are created and are used to predict compressive, split tensile, and flexural strength of fiber admixed concrete. These models are evaluated through the statistical test such as coefficient of determination (R2) and root mean squared error (RMSE). The results show that these parameters produce a valid model through both MRA and ANN, and this model gives more precise prediction for the fiber admixed concrete.

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

European Journal of Engineering Research and Science ◽

10.24018/ejers.2018.3.6.758 ◽

2018 ◽

Vol 3 (6) ◽

pp. 10 ◽

Cited By ~ 1

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.

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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