scholarly journals Estimation of load-set behavior of driven concrete piles using artificial neural network and cone penetration test

2021 ◽  
Vol 1928 (1) ◽  
pp. 012055
Author(s):  
I V Ofrikhter ◽  
A B Ponomarev
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cone Penetration Test ◽  
Penetration Test ◽  
Cone Penetration ◽  
Concrete Piles ◽  
Artificial Neural

Appraising cone penetration test based liquefaction resistance evaluation methods: artificial neural network approach

1999 ◽  
Vol 36 (3) ◽  
pp. 443-454 ◽  
Author(s):  
C Hsein Juang ◽  
Caroline Jinxia Chen ◽  
Yong-Ming Tien
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Success Rate ◽  
Sandy Soils ◽  
Cone Penetration Test ◽  
Penetration Test ◽  
Cone Penetration ◽  
Liquefaction Resistance ◽  
Ann Models ◽  
Artificial Neural

This paper evaluates and compares two comprehensive cone penetration test (CPT) based methods for evaluating liquefaction resistance of sandy soils. The comparison is made based on the results obtained from artificial neural network (ANN) analyses. Two methods are compared, one by Olsen and his colleagues at the Waterways Experiment Station and one by Robertson and his colleagues at the University of Alberta. ANN models are created to approximate the two CPT-based methods so that they can easily be compared using a large database. The results show that ANN models can approximate both Robertson and Olsen methods well, and that both methods are fairly accurate in predicting liquefaction resistance. The Robertson method has a success rate of 89% in predicting liquefied cases, a success rate of 76% in predicting nonliquefied cases, and an overall success rate of 84%. The success rates for the Olsen method are 68%, 89%, and 77%, respectively, in predicting liquefied cases, nonliquefied cases, and all cases. Both methods are considered accurate in predicting liquefaction resistance of sandy soils. The Robertson method is slightly more accurate than the Olsen method. The issue of the propagation of potential uncertainties in the soil parameters and solution model is also discussed.

scholarly journals Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks

2006 ◽  
Vol 43 (6) ◽  
pp. 626-637 ◽  
Author(s):  
Mohamed A Shahin ◽  
Mark B Jaksa
Keyword(s):  
Neural Networks ◽  
Cone Penetration Test ◽  
Ann Model ◽  
Pullout Capacity ◽  
Penetration Test ◽  
Cone Penetration ◽  
Cross Sectional ◽  
Ground Anchors ◽  
Artificial Neural ◽  
The Impact

Marquees are temporary light structures that are connected to the ground by small anchors that act in tension and are designed to resist uplift forces. Due to the temporary nature of these structures, little, if any, attention is given to the pullout capacity of the anchors used to secure them. Failures of such structures are not rare and have resulted in deaths and tens of thousands of dollars of damage. This paper reports on a series of 119 in situ anchor pullout tests conducted on rough mild steel anchors of various lengths, cross-sectional shapes, and areas. Comparison tests are carried out to investigate the impact of the factors affecting the pullout capacity of small anchors. Six methods that determine the axial pile capacity directly from cone penetration test (CPT) data are presented and used to calculate the pullout capacity of small ground anchors. The capacities obtained from these CPT-based methods are compared with predictions from a recently developed artificial neural network (ANN) model. The actual pullout loads are compared with predictions from the CPT and ANN methods, and statistical analyses are carried out to evaluate and rank their performance. The results indicate that the ANN-based method provides superior predictions of the pullout capacity of small ground anchors, whereas the Schmertmann method provides the best performance of the CPT-based techniques examined.Key words: ground anchors, pullout capacity, cone penetration test, artificial neural networks.

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

2015 ◽  
Vol 19 (1) ◽  
pp. 85-93 ◽  
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

<p class="MsoNormal" style="text-align: justify; line-height: 200%;">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.</p><p class="MsoNormal" style="text-align: justify; line-height: 200%;"> </p><p class="MsoNormal" style="text-align: justify; line-height: 200%;"><strong>Resumen</strong></p><p class="MsoNormal" style="text-align: justify; line-height: 200%;">La 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.</p>

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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