Modelling pile capacity and load-settlement behaviour of piles embedded in sand \& mixed soils using artificial intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Alkroosh:thesis,
  title =        "Modelling pile capacity and load-settlement behaviour
                 of piles embedded in sand \& mixed soils using
                 artificial intelligence",
  author =       "Iyad Salim Jabor Alkroosh",
  year =         "2011",
  school =       "Curtin University, Faculty of Engineering and
                 Computing, Department of Civil Engineering",
  address =      "Australia",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, modelling pile capacity,
                 load-settlement behaviour of piles, artificial
                 intelligence, (GEP) and the artificial neural networks
                 (ANNs), numerical modelling techniques",
  URL =          "http://espace.library.curtin.edu.au/Modelling.pdf",
  URL =          "http://espace.library.curtin.edu.au/R/?func=dbin-jump-full&object_id=166155&local_base=GEN01-ERA02",
  size =         "338 pages",
  abstract =     "This thesis presents the development of numerical
                 models which are intended to be used to predict the
                 bearing capacity and the load-settlement behaviour of
                 pile foundations embedded in sand and mixed soils. Two
                 artificial intelligence techniques, the gene expression
                 programming (GEP) and the artificial neural networks
                 (ANNs), are used to develop the models. The GEP is a
                 developed version of genetic programming (GP).
                 Initially, the GEP is used to model the bearing
                 capacity of the bored piles, concrete driven piles and
                 steel driven piles. The use of the GEP is extended to
                 model the load-settlement behaviour of the piles but
                 achieved limited success. Alternatively, the ANNs have
                 been employed to model the load-settlement behaviour of
                 the piles.

                 The GEP and the ANNs are numerical modelling techniques
                 that depend on input data to determine the structure of
                 the model and its unknown parameters. The GEP tries to
                 mimic the natural evolution of organisms and the ANNs
                 tries to imitate the functions of human brain and nerve
                 system. The two techniques have been applied in the
                 field of geotechnical engineering and found successful
                 in solving many problems.

                 The data used for developing the GEP and ANN models are
                 collected from the literature and comprise a total of
                 50 bored pile load tests and 58 driven pile load tests
                 (28 concrete pile load tests and 30 steel pile load
                 tests) as well as CPT data. The bored piles have
                 different sizes and round shapes, with diameters
                 ranging from 320 to 1800 mm and lengths from 6 to 27 m.
                 The driven piles also have different sizes and shapes
                 (i.e. circular, square and hexagonal), with diameters
                 ranging from 250 to 660 mm and lengths from 8 to 36 m.
                 All the information of case records in the data source
                 is reviewed to ensure the reliability of used data.

                 The variables that are believed to have significant
                 effect on the bearing capacity of pile foundations are
                 considered. They include pile diameter, embedded
                 length, weighted average cone point resistance within
                 tip influence zone and weighted average cone point
                 resistance and weighted average sleeve friction along
                 shaft.

                 The sleeve friction values are not available in the
                 bored piles data, so the weighted average sleeve
                 friction along shaft is excluded from bored piles
                 models. The models output is the pile capacity
                 (interpreted failure load).

                 Additional input variables are included for modelling
                 the load-settlement behaviour of piles. They include
                 settlement, settlement increment and current state of
                 load settlement. The output is the next state of
                 load-settlement.

                 The data are randomly divided into two statistically
                 consistent sets, training set for model calibration and
                 an independent validation set for model performance
                 verification.

                 The predictive ability of the developed GEP model is
                 examined via comparing the performance of the model in
                 training and validation sets. Two performance measures
                 are used: the mean and the coefficient of correlation.
                 The performance of the model was also verified through
                 conducting sensitivity analysis which aimed to
                 determine the response of the model to the variations
                 in the values of each input variables providing the
                 other input variables are constant. The accuracy of the
                 GEP model was evaluated further by comparing its
                 performance with number of currently adopted
                 traditional CPT-based methods. For this purpose,
                 several ranking criteria are used and whichever method
                 scores best is given rank 1. The GEP models, for bored
                 and driven piles, have shown good performance in
                 training and validation sets with high coefficient of
                 correlation between measured and predicted values and
                 low mean values. The results of sensitivity analysis
                 have revealed an incremental relationship between each
                 of the input variables and the output, pile capacity.
                 This agrees with what is available in the geotechnical
                 knowledge and experimental data. The results of
                 comparison with CPT-based methods have shown that the
                 GEP models perform well.",
  abstract =     "The GEP technique is also used to simulate the
                 load-settlement behaviour of the piles. Several
                 attempts have been carried out using different input
                 settings. The results of the favoured attempt have
                 shown that the GEP have achieved limited success in
                 predicting the load-settlement behaviour of the piles.
                 Alternatively, the ANN is considered and the sequential
                 neural network is used for modelling the
                 load-settlement behaviour of the piles.

                 This type of network can account for the
                 load-settlement interdependency and has the option to
                 feedback internally the predicted output of the current
                 state of load settlement to be used as input for the
                 next state of load-settlement.

                 Three ANN models are developed: a model for bored piles
                 and two models for driven piles (a model for steel and
                 a model for concrete piles). The predictive ability of
                 the models is verified by comparing their predictions
                 in training and validation sets with experimental data.
                 Statistical measures including the coefficient of
                 correlation and the mean are used to assess the
                 performance of the ANN models in training and
                 validation sets. The results have revealed that the
                 predicted load-settlement curves by ANN models are in
                 agreement with experimental data for both of training
                 and validation sets. The results also indicate that the
                 ANN models have achieved high coefficient of
                 correlation and low mean values. This indicates that
                 the ANN models can predict the load-settlement of the
                 piles accurately.

                 To examine the performance of the developed ANN models
                 further, the prediction of the models in the validation
                 set are compared with number of load-transfer methods.
                 The comparison is carried out first visually by
                 comparing the load-settlement curve obtained by the ANN
                 models and the load transfer methods with experimental
                 curves. Secondly, is numerically by calculating the
                 coefficient of correlation and the mean absolute
                 percentage error between the experimental data and the
                 compared methods for each case record. The visual
                 comparison has shown that the ANN models are in better
                 agreement with the experimental data than the load
                 transfer methods. The numerical comparison also has
                 shown that the ANN models scored the highest
                 coefficient of correlation and lowest mean absolute
                 percentage error for all compared case records.

                 The developed ANN models are coded into a simple and
                 easily executable computer program.

                 The output of this study is very useful for designers
                 and also for researchers who wish to apply this
                 methodology on other problems in Geotechnical
                 Engineering. Moreover, the result of this study can be
                 considered applicable worldwide because its input data
                 is collected from different regions.",
  bibsource =    "OAI-PMH server at espace.library.curtin.edu.au",
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Genetic Programming entries for Iyad Salim Jabor Alkroosh

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