An ANN Model for Predicting Level Ultimate Bearing Capacity of PHC Pipe Pile

2010 ◽  
Author(s):  
Zhao Jianbin ◽  
Tu Jiewen ◽  
Shi Yongqiang
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Ann Model ◽  
Pipe Pile

Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

2015 ◽  
Vol 28 (2) ◽  
pp. 391-405 ◽  
Author(s):  
Danial Jahed Armaghani ◽  
Raja Shahrom Nizam Shah Bin Raja Shoib ◽  
Koohyar Faizi ◽  
Ahmad Safuan A. Rashid
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Ann Model

Time Effect Research of Prestressed Pipe Pile Based on High Strain Dynamic Test

2012 ◽  
Vol 256-259 ◽  
pp. 531-534
Author(s):  
Jia Tao Wang ◽  
Hong Li Zhao
Keyword(s):  
Bearing Capacity ◽  
High Strain ◽  
Dynamic Test ◽  
Ultimate Bearing Capacity ◽  
Time Effect ◽  
Load Test ◽  
Single Pile ◽  
Dead Load ◽  
Pipe Pile ◽  
Effect Mechanism

More detailed information about the bearing capacity and integrity of the pile can be obtained by high strain dynamic test than by dead-load test [1]. Engineering examples show that the bearing capacity of the prestressed pipe pile gradually increase with the growth of the resting time, and the ultimate bearing capacity of the pile can reach up to 2 times more than the initial bearing capacity. Through the study of the time effect mechanism, it is found that the increment of ultimate bearing capacity of the single pile is mainly caused by side soil resistance. The end resistance has little influence on the time effect of bearing capacity of pile.

Prediction of Ultimate Bearing Capacity of Prestressed Pipe Pile Based on BP Neural Network

2011 ◽  
Vol 101-102 ◽  
pp. 228-231
Author(s):  
Jian Ping Jiang
Keyword(s):  
Neural Network ◽  
Bearing Capacity ◽  
Bp Neural Network ◽  
Ultimate Bearing Capacity ◽  
Pipe Pile ◽  
Absolute Value ◽  
Independent Variables ◽  
Average Value ◽  
Standard Value ◽  
Bp Neural Network Model

Based on BP neural network, this paper had a prediction on ultimate bearing capacity of prestressed pipe pile. Taking pile diameter, effective pile length, ultimate average value of friction standard value, ultimate average value of end resistance standard value as influences factors, the prediction model of pile bearing capacity based on BP neural network was obtained. It was found that, the average value of absolute value for the relative error of fitting value of pile bearing capacity compared with the observed value for 70 groups of independent variables training BP neural network model was 3.1498%; And the average value of absolute value for the relative error of prediction value of pile bearing capacity compared with the observed value for 10 groups of independent variables validating BP neural network model was 3.50126% whose precision was better than ANFIS’5.32293%. The following conclusion can be drawn that, the prediction model of ultimate bearing capacity of prestressed pipe pile based on BP neural network is feasible.

Numerical Analysis of Compacted Forming Concrete Pile in the Subsea Soil

2011 ◽  
Vol 317-319 ◽  
pp. 2258-2265
Author(s):  
Jian Min Chen ◽  
Xiao Dong Hao ◽  
Zu Chang Song
Keyword(s):  
Elastic Modulus ◽  
Numerical Analysis ◽  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Feature Point ◽  
Steel Pipe ◽  
Pipe Pile ◽  
Concrete Pile ◽  
Steel Pipe Pile

Based on the present tecnology of pile, a method of compacted forming concrete pile applied in the subsea base is studied. Using the method of finite different the procedure of compacted forming at the end of steel pipe pile has been simulated in the particular geology soil, the effects of the elastic modulus, cohesion, friction and dilation on the compacted behaviour are aquired and the bearing capacity has been calculated. The results show that the ultimate bearing capacity of this pile increases approximate 3 times bigger than the steel pipe pile with the same dimentions, in addition, its curve of Q-S is smooth and ultimate feature point is indistinct, which proves that this tecnology of compacted forming concrete pile is able to increase the bearing capacity prominently.

Analysis of Principal Component - Application of SVM Model in Prediction of Ultimate Bearing Capacity of Static Pressure Pipe Pile

2012 ◽  
Vol 166-169 ◽  
pp. 1345-1352
Author(s):  
Jian Bin Zhao ◽  
Chao Xu ◽  
Hui Guo
Keyword(s):  
Bearing Capacity ◽  
Static Pressure ◽  
Principal Component ◽  
Ultimate Bearing Capacity ◽  
Support Vector ◽  
Pipe Pile ◽  
Pressure Pipe ◽  
Svm Model ◽  
Geological Conditions ◽  
The Impact

Vertical ultimate bearing capacity of static pressure pipe pile is influenced by comprehensive factors, such as pile body, soil around pile and construction conditions., and the relationship between the impact factors and ultimate bearing capacity of a single pile is highly complexity and non-linear. This paper is based on collecting the data from static load tests in typical geological conditions of Liao-shen area, construction records, and test pile sites. And then combine analysis of principal component with SVM to analysis the prediction of the single pile’s vertical ultimate bearing capacity. This model can reduce the number of SVM input variable dimension to improve speed of training support vector effectively. At the same time it can eliminate the influence factors of multiple correlation. The results show that the proposed principal component analysis SVM model has good predictive accuracy and generalization ability, and opens up new avenue of research for analysis of static pressure pipe pile vertical bearing properties.

scholarly journals Evaluation of Ultimate Bearing Capacity of Pre-Stressed High-Strength Concrete Pipe Pile Embedded in Saturated Sandy Soil Based on In-Situ Test

2020 ◽  
Vol 10 (18) ◽  
pp. 6269 ◽  
Author(s):  
Yingjie Wei ◽  
Duli Wang ◽  
Jiawang Li ◽  
Yuxin Jie ◽  
Zundong Ke ◽  
...  
Keyword(s):  
Bearing Capacity ◽  
High Strength Concrete ◽  
High Strength ◽  
Ultimate Bearing Capacity ◽  
Penetration Test ◽  
Pipe Pile ◽  
Strength Concrete ◽  
Survey Report ◽  
Pile Length

Estimation of ultimate bearing capacity (UBC) of pre-stressed high-strength concrete (PHC) pipe pile is critical for optimizing pile design and construction. In this study, a standard penetration test (SPT), static cone penetration test (CPT) and static load test (SLT) were carried out to assess, determine and compare the UBC of the PHC pipe pile embedded in saturated sandy layers at different depths. The UBC was calculated with three methods including the JGJ94-2008 method, Meyerhof method and Schmertmann method based on in-situ blow count (N) of SPT (SPT-N) which was higher than the values recommended in survey report regardless of pile length. The average UBC values calculated with cone-tip resistance and sleeve friction from CPTs was also higher than the value recommended in the survey report. Moreover, the actual UBC values directly obtained by load-displacement curves from SLTs were in line with the calculated values based on in-situ SPTs and CPTs, but approximately twice as high as the values recommended in the survey report regardless of pile length. For the SPT method, the application of bentonite mud in saturated sand layers is critical for the assessment of pile capacity in the survey phase, CPTs can provide reliable results regardless of soil characteristics and groundwater if the soil layer can be penetrated, and SLTs are necessary to accurately determine the UBC in complex stratum.

scholarly journals Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

2019 ◽  
Vol 9 (21) ◽  
pp. 4594 ◽  
Author(s):  
Hossein Moayedi ◽  
Bahareh Kalantar ◽  
Anastasios Dounis ◽  
Dieu Tien Bui ◽  
Loke Kok Foong
Keyword(s):  
Bearing Capacity ◽  
Prediction Models ◽  
Soil Layer ◽  
Ultimate Bearing Capacity ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Fe Modelling ◽  
Ann Models ◽  
Circular Footing

In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.

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