Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks

The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other and design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load-settlement response of piles to be well predicted. However, it is well known that the actual load-settlement response of pile foundations can only be obtained by load tests carried out in-situ, which are expensive and time-consuming. In this technical note, the recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the load-settlement response of steel driven piles subjected to axial loading. The developed RNN model was calibrated and validated using several in-situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the developed RNN model has the ability to reliably predict the load-settlement response of axially loaded steel driven piles and can thus be used by geotechnical engineers for routine design practice

Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks

The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other, and the design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load–settlement response of piles to be well predicted. However, it is well known that the actual load–settlement response of pile foundations can be obtained only by load tests carried out in situ, which are expensive and time-consuming. In this paper, recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the full load–settlement response of drilled shafts (bored piles) subjected to axial loading. The developed RNN model was calibrated and validated using several in situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the developed RNN model has the ability to reliably predict the load–settlement response of axially loaded drilled shafts and can thus be used by geotechnical engineers for routine design practice

Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

© 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. In this study, the least square support vector machine (LSSVM) algorithm was applied to predicting the bearing capacity of bored piles embedded in sand and mixed soils. Pile geometry and cone penetration test (CPT) results were used as input variables for prediction of pile bearing capacity. The data used were collected from the existing literature and consisted of 50 case records. The application of LSSVM was carried out by dividing the data into three sets: a training set for learning the problem and obtaining a relationship between input variables and pile bearing capacity, and testing and validation sets for evaluation of the predictive and generalization ability of the obtained relationship. The predictions of pile bearing capacity by LSSVM were evaluated by comparing with experimental data and with those by traditional CPT-based methods and the gene expression programming (GEP) model. It was found that the LSSVM performs well with coefficient of determination, mean, and standard deviation equivalent to 0.99, 1.03, and 0.08, respectively, for the testing set, and 1, 1.04, and 0.11, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the LSSVM was accurate in predicting the pile bearing capacity. The results of comparison also showed that the proposed algorithm predicted the pile bearing capacity more accurately than the traditional methods including the GEP model

Grammatical inference techniques and their application in ground investigation

Ground investigations often use trial pits and borehole cores on construction sites to determine the strata likely to be encountered at various depths. The data obtained from trial pits can be coded into a form that can be used as sample observations for input to a grammatical inference machine. A grammatical inference machine is a black box, which when presented with a sample of observations of some unknown source language, produces a grammar which is compatible with the sample. This article presents a heuristic model for a grammatical inference machine, which takes as data sentences and non-sentences identified as such, and is capable of inferring grammars in the class of context-free grammars expressed in Chomsky Normal Form. An algorithm and its corresponding software implementation have been developed based on this model. The software takes, as input, coded representations of ground investigation data, and produces as output a grammar which describes and classifies the geotechnical data observed in the area, and also promises the possibility of being able to predict the likely configuration of strata across the site

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set

Simulating pile load-settlement behavior from CPT data using intelligent computing

Analysis of pile load-settlement behavior is a complex problem due to the participation of many factors involved. This paper presents a new procedure based on artificial neural networks (ANNs) for simulating the load-settlement behavior of pile foundations embedded in sand and mixed soils (subjected to axial loads). Three ANN models have been developed, a model for bored piles and two other models for driven piles (a model for each of concrete and steel piles). The data used for development of the ANN models is collected from the literature and comprise a series of in-situ piles load tests as well as cone penetration test (CPT) results. The data of each model is divided into two subsets: Training set for model calibration and independent validation set for model verification. Predictions from the ANN models are compared with the results of experimental data and with predictions of number of currently adopted load-transfer methods. Statistical analysis is used to verify the performance of the models. The results indicate that the ANN model performs very well and able to predict the pile load-settlement behaviour accurately