A new method for signle pile settlement prediction and analysis

Due to the presence of uncertainties, errors inevitably arise with the estimations of pile settlement. To properly consider serviceability requirements in limit state design, it is necessary to characterize the performance of commonly used settlement prediction models. In this work, information from 64 cases of long driven steel H-piles from field static loading tests in Hong Kong is utilized to evaluate the errors of three settlement prediction models for single piles: two elastic methods and a nonlinear load–transfer method. Commonly adopted soil parameters recommended in two Hong Kong design guidelines are used to reflect the uncertainty arising from evaluation of soil properties. The model error is represented by a bias factor. A conventional statistical analysis was first conducted to study the variability of model bias. A regression analysis method was then proposed as a supplemental analysis of model bias when only limited test data were available or when the measured settlement data distribute in a large range. Both methods result in very similar mean biases. The mean bias of each prediction model tends to vary with the load level and the bearing stratum at the pile toe; while the coefficient of variation of model bias only varies in narrow ranges.

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Settlement Prediction of Roadbed Based on Mixture Model with Exponential Curve and ANN

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.663.76 ◽

2013 ◽

Vol 663 ◽

pp. 76-79 ◽

Cited By ~ 1

Author(s):

Guo Heng Li ◽

Han Bing Liu ◽

Xu Xi Qin

Keyword(s):

Mixture Model ◽

High Precision ◽

Measured Data ◽

New Method ◽

Settlement Prediction ◽

Exponential Curve ◽

Network Scale

A mixture method based on exponential curve and ANN is presented according to settlement prediction of roadbed with measured data. Based on this method, the rule of roadbed settlement is classified into sure part and uncertain part. Exponential curve is used to model the sure part, and ANN to model the uncertain part, thus the mixture settlement model can be obtained. Prediction results show that the mixture model has advantages of high precision and small network scale; it provides a new method for settlement prediction of roadbed.

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On the Use of Neuro-Swarm System to Forecast the Pile Settlement

Applied Sciences ◽

10.3390/app10061904 ◽

2020 ◽

Vol 10 (6) ◽

pp. 1904 ◽

Cited By ~ 15

Author(s):

Danial Jahed Armaghani ◽

Panagiotis G. Asteris ◽

Seyed Alireza Fatemi ◽

Mahdi Hasanipanah ◽

Reza Tarinejad ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Hybrid Model ◽

Particle Swarm ◽

Sensitivity Analyses ◽

Coefficient Of Determination ◽

Hybrid Technique ◽

Swarm Optimization ◽

Settlement Prediction ◽

Modeling Process ◽

Pile Settlement

In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R2) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique.

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New Method of Surface Settlement Prediction for Saint-petersburg Metro Escalator Tunnels Excavated by EPB TBM

Procedia Engineering ◽

10.1016/j.proeng.2016.07.283 ◽

2016 ◽

Vol 150 ◽

pp. 2266-2271 ◽

Cited By ~ 6

Author(s):

S.U. Novozhenin ◽

M.G. Vystrchil

Keyword(s):

Surface Settlement ◽

New Method ◽

Settlement Prediction ◽

Saint Petersburg ◽

Epb Tbm ◽

Surface Settlement Prediction

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Selective Sampling and Orientation of Non-Homogeneous Tissues

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100100238 ◽

1968 ◽

Vol 26 ◽

pp. 98-99

Author(s):

C. C. Clawson ◽

L. W. Anderson ◽

R. A. Good

Keyword(s):

Electron Microscopy ◽

Electron Microscope ◽

Light Microscopy ◽

Random Sampling ◽

New Method ◽

Microscope Examination ◽

Small Areas ◽

Selective Sampling ◽

Large Numbers ◽

Specific Orientation

Investigations which require electron microscope examination of a few specific areas of non-homogeneous tissues make random sampling of small blocks an inefficient and unrewarding procedure. Therefore, several investigators have devised methods which allow obtaining sample blocks for electron microscopy from region of tissue previously identified by light microscopy of present here techniques which make possible: 1) sampling tissue for electron microscopy from selected areas previously identified by light microscopy of relatively large pieces of tissue; 2) dehydration and embedding large numbers of individually identified blocks while keeping each one separate; 3) a new method of maintaining specific orientation of blocks during embedding; 4) special light microscopic staining or fluorescent procedures and electron microscopy on immediately adjacent small areas of tissue.

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