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

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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Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.576.91 ◽

2012 ◽

Vol 576 ◽

pp. 91-94 ◽

Cited By ~ 8

Author(s):

Erry Yulian Triblas Adesta ◽

Muataz H.F. Al Hazza ◽

M.Y. Suprianto ◽

Muhammad Riza

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

End Milling ◽

Back Propagation ◽

Cutting Temperature ◽

Neural Network Modeling ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Cnc End Milling

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

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Creep analysis of bimaterial microcantilever beam for sensing device using artificial neural network (ANN)

ASEAN Journal on Science and Technology for Development ◽

10.29037/ajstd.95 ◽

2017 ◽

Vol 23 (1&2) ◽

pp. 89 ◽

Cited By ~ 1

Author(s):

WaiChi Wong ◽

HingWah Lee ◽

Ishak A. Azid ◽

K.N. Seetharamu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Feed Forward Back Propagation ◽

The Neural Network ◽

Creep Analysis ◽

Simulation Results ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Microcantilever Beam

In this study, a feed-forward back-propagation Artificial Neural Network (ANN) is used to predict the stress relaxation and behavior of creep for bimaterial microcantilever beam for sensing device. Results obtained from ANSYS® 8.1 finite element (FE) simulations, which show good agreement with experimental work [1], is used to train the neural network. Parametric studies are carried out to analyze the effects of creep on the microcantilever beam in term of curvature and stress deve loped with time. It is shown that ANN accurately predicts the stress level for the microcantilever beam using the trained ANSYS® simulation results due to the fact that there is no scattered data in the FE simulation results. ANN takes a small fraction of time and effort compar ed to FE prediction.

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Forecasting Coconut Yield: A Comparative Study between the Use of Traditional Forecasting and Feed Forward Back Propagation Artificial Neural Network

UIC Research Journal ◽

10.17158/320 ◽

2014 ◽

Vol 18 (2) ◽

Author(s):

Eric John G. Emberda ◽

Den Ryan L. Dumas ◽

Timothy Pierce M. Rentillo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Ann Model ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Forecast Data ◽

The Mean ◽

Artificial Neural ◽

Artificial Neural Network Ann

This study compared the use of Linear Regression and Feed Forward Backpropagation Artificial Neural Network (ANN) in forecasting the coconut yield and copra yield of a selected area in Davao region. Raw data were gathered from the Philippine Coconut Authority, Davao Research Center. An ANN model was created and tested repeatedly to the best combination of nodes. Accuracy of the forecast between the two methods was compared by looking at the mean square error and the standard error for variable x and y. Results showed that the use of Feed Forward Back Propagation Artificial Neural Network gives better accuracy of the forecast data.

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Evaluation of the Use of Artificial Neural Networks to Predict the Photovoltaic Power Generation Factors by Using Feed Forward Back Propagation (FFBP) Technique

International Journal of Current Science Research and Review ◽

10.47191/ijcsrr/v4-i2-06 ◽

2021 ◽

Vol 04 (02) ◽

Author(s):

Shreeniket Trivedi ◽

Keyword(s):

Neural Network ◽

Power Generation ◽

Artificial Neural Network ◽

Environmental Factors ◽

Solar Power ◽

Back Propagation ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Artificial Neural Network Ann

The solar power generation (renewable energy) is the cleanest form of energy generation method and the solar power plant has a very long life and also is maintenance-free, but due to the high unpredictability of the generated solar power due to dynamically changing environmental factors it cannot be used as the reliable source of power. This prevents the maximum utilization of solar energy. In this project we are designing the artificial neural network model to predict the power generated depending on the various environmental factors like visibility, cloud cover (sky cover), etc. the intensity of the incident of the solar radiation decreases and thus the plant is not able to work at its rated capacity. We use Artificial Neural Network (ANN) with Feed Forward Back Propagation (FFBP) technique and predicted the percentage of the maximum plant capacity which will be generated by considering the environmental factors like temperature, pressure, distance to solar noon, day light, sky cover, visibility, humidity, wind speed, wind direction and compared our results with available data and find quite encouraging results.

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Optimization and Prediction of Motorcycle Injection System Performance with Feed-Forward Back-Propagation Method Artificial Neural Network (ANN)

American Journal of Engineering and Applied Sciences ◽

10.3844/ajeassp.2016.222.235 ◽

2016 ◽

Vol 9 (2) ◽

pp. 222-235 ◽

Cited By ~ 2

Author(s):

La Ode Ichlas Syahrullah ◽

Nazaruddin Sinaga

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

System Performance ◽

Back Propagation ◽

Injection System ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Artificial Neural Network Ann

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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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ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

10.12783/asc36/35819 ◽

2021 ◽

Author(s):

DEVIN NIELSEN ◽

TYLER LOTT ◽

SOM DUTTA ◽

JUHYEONG LEE

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Architecture ◽

Back Propagation ◽

Training Dataset ◽

Predictive Tool ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

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Predicting hearing loss from otoacoustic emissions using an artificial neural network

South African Journal of Communication Disorders ◽

10.4102/sajcd.v49i1.215 ◽

2002 ◽

Vol 49 (1) ◽

Author(s):

Rouviere De Waal ◽

René Hugo ◽

Maggi Soer ◽

Johann J. Krüger

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Otoacoustic Emissions ◽

Back Propagation ◽

Normal Hearing ◽

Hearing Impaired ◽

Distortion Product ◽

Hearing Ability ◽

Artificial Neural ◽

Artificial Neural Network Ann

Normal and impaired pure tone thresholds (PTTs) were predicted from distortion product otoacoustic emissions (DP using a feed-forward artificial neural network (ANN) with a back-propagation training algorithm. The ANN used a present and absent DPOAEs from eight DP grams, (2fl -f2 = 406 - 4031 Hz) to predict PTTs at 0.5, 1, 2 and 4 kHz. With normal hearing as < 25 dB HL, prediction accuracy of normal hearing was 94% at 500, 88% at 1000, 88% at 2000 and 93% at 4000 Hz. Prediction of hearing-impaired categories was less accurate, due to insufficient data for the ANN to train on. This research indicates the possibility of accurately predicting hearing ability within 10 dB in normal hearing individuals and in hearing-impaired listeners with DPOAEs and ANNsfrom 500 - 4000 Hz.

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Artificial neural network classification-based skin cancer detection

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.10364 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 591

Author(s):

M. Shyamala Devi ◽

A.N. Sruthi ◽

P. Balamurugan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Skin Cancer ◽

Back Propagation ◽

Modern Technology ◽

Data Set ◽

Processing Strategies ◽

Skin Cancers ◽

Artificial Neural ◽

Artificial Neural Network Ann

At present, skin cancers are extremely the most severe and life-threatening kind of cancer. The majority of the pores and skin cancers are completely remediable at premature periods. Therefore, a premature recognition of pores and skin cancer can effectively protect the patients. Due to the progress of modern technology, premature recognition is very easy to identify. It is not extremely complicated to discover the affected pores and skin cancers with the exploitation of Artificial Neural Network (ANN). The treatment procedure exploits image processing strategies and Artificial Intelligence. It must be noted that, the dermoscopy photograph of pores and skin cancer is effectively determined and it is processed to several pre-processing for the purpose of noise eradication and enrichment in image quality. Subsequently, the photograph is distributed through image segmentation by means of thresholding. Few components distinctive for skin most cancers regions. These features are mined the practice of function extraction scheme - 2D Wavelet Transform scheme. These outcomes are provides to the Back-Propagation Neural (BPN) Network for effective classification. This completely categorizes the data set into either cancerous or non-cancerous.

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