Pile Capacity Prediction Research Articles

Evaluation of the Ultimate Capacity of Friction Piles

The precise prediction of maximum load carrying capacity of bored piles is a complex problem because the load is a function of a large number of factors. These factors include method of boring, method of concreting, quality of concrete, expertise of the construction staff, the ground conditions and the pile geometry. To ascertain the field performance and estimate load carrying capacities of piles, in-situ pile load tests are conducted. Due to practical and time constraints, it is not possible to load the pile up-to failure. In this study, field pile load test data is analyzed to estimate the ultimate load for friction piles. The analysis is based on three pile load test results. The tests are conducted at the site of The Cultural and Recreational Complex project in Port Said, Egypt. Three pile load tests are performed on bored piles of 900 mm diameter and 50 m length. Geotechnical investigations at the site are carried out to a maximum depth of 60 m. Ultimate capacities of piles are determined according to different methods including Egyptian Code of practice (2005), Tan-gent-tangent, Hansen (1963), Chin (1970), Ahmed and Pise (1997) and Decourt (1999). It was concluded that approxi- mately 8% of the ultimate load is resisted by bearing at the base of the pile, and that up to 92% of the load is resisted by friction along the shaft. Based on a comparison of pile capacity predictions using different method, recommendations are made. A new method is proposed to calculate the ultimate capacity of the pile from pile load test data. The ultimate capacity of the bored piles predicted using the proposed method appears to be reliable and compares well to different available methods.

Predicting axial capacity of driven piles in cohesive soils using intelligent computing

An accurate prediction of pile capacity under axial loads is necessary for the design. This paper presents the development of a new model to predict axial capacity of pile foundations driven into cohesive soils. Gene expression programming technique (GEP) has been utilized for this purpose. The data used for development of the GEP model is collected from the literature and comprise a series of in-situ driven piles load tests as well as cone penetration test (CPT) results. The data are divided into two subsets: training set for model calibration and independent validation set for model verification. Predictions from the GEP model are compared with experimental data and with predictions of number of currently adopted CPT-based methods. The results have demonstrated that the GEP model performs well with coefficient of correlation, mean and probability density at 50% equivalent to 0.94, 0.96 and 1.01, respectively, indicating that the proposed model predicts pile capacity accurately.

Complex CBR (of BC Soil-RHA-Cement Mix) Estimation: Made Easy by ANN Approach [a Soft Computing Technique

In past days many researchers have been worked on the expansive soil to determine the California Bearing Ratio (CBR) values in a conventional ways, which are time consuming and require lot of manual involvements. So we the authors of this research paper attempted to develop a soft computing technique to prognosticate CBR value by using Artificial Neural Network (ANN), a data driven technique. ANN is a mathematical model inspired from the human brain’s information-processing characteristics, including the parallel processing ability. Over the last few years, the use of ANN has increased in many areas of engineering. In particular have been applied to many geotechnical engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANN has been used successfully in the pile capacity prediction, site characterization and so on. In the present study the Black Cotton (BC) soil has been stabilized by using Rice Husk Ash (RHA) and cement, several experiments have been conducted for different mix combinations under soaked condition. From the obtained results, it is observed that the CBR value of BC soil increases with the addition of RHA and cement combination. The soaked CBR value found to be maximum for the mix of BC soil + 15% RHA + 12% cement. The present study deals with collection of input data base from experimental results, ANN’s training and its testing are adopted to fix the appropriate weighted matrix (Illustrated in Fig (1)) which in turn Prognosticates the CBR value. Experimental results have been compared with the CBR values prognosticated by using ANN and comparison graphs also plotted (Illustrated in fig (4)). The results of this study will contribute for the prognostication of CBR, which will assist a geotechnical engineer in estimation of CBR, with minimum effort.

On the Study of Pile Driving Formula Based on Blow Counts

Accuracy of predicting pile capacities by pile driving formulas have been investigated. Five test piles were driven up to a depth of about 9 m of clay deposit and the penetrations due to final blows were recorded. The pile bearing capacity of each pile was predicted using 6 different pile driving formulas and the predicted pile capacity was compared with measured pile capacity from the pull up tests. Hiley formula, Modified Engineering News Record (ENR) formula, Janbu formula, Dutch formula, Danish formula, and Gates formula were used. The performance and accuracy of each formula was evaluated and the correlation coefficient of each pile driving formula was determined for a more accurate pile capacity prediction. Methods used to evaluate the performance of each formula were; (1) the best fit line for Qp versus Qm (2) cumulative probability for Qp/Qm and (3) the arithmetic mean and standard deviation for Qp/Qm. From the study, it was found that using Dutch formula provided the most accurate pile capacity estimate compared to the other formulas with an average of 7% deviation from value obtained from the field pull up test. It was followed by the Danish formula, Janbu formula, Hiley formula, Modified ENR formula, and Gates formula. The ability to predict the accuracy of estimating pile capacity using an appropriate method is very important and valuable to contractors, developers, geotechnical engineers, and manufacturers.

Intelligent computing for modeling axial capacity of pile foundations

In the last few decades, numerous methods have been developed for predicting the axial capacity of pile foundations. Among the available methods, the cone penetration test (CPT)-based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the CPT results, which have been found to yield more reliable soil properties; hence, more accurate axial pile capacity predictions. In this paper, one of the most commonly used artificial intelligence techniques, i.e., artificial neural networks (ANNs), is utilized in an attempt to develop artificial neural network (ANN) models that provide more accurate axial capacity predictions for driven piles and drilled shafts. The ANN models are developed using data collected from the literature and comprise 80 driven pile and 94 drilled-shaft load tests, as well as CPT results. The predictions from the ANN models are compared with those obtained from the most commonly used available CPT-based methods, and statistical analyses are carried out to rank and evaluate the performance of the ANN models and CPT methods. To facilitate the use of the developed ANN models, they are translated into simple design equations suitable for hand calculations.

Analysis of piles in a residual soil—The ISC'2 prediction

The 2nd International Conference on Site Characterization (ISC'2), held in 2004, included a seminar for prediction of pile capacity involving three 6 m embedment length test piles, one 350 mm square driven concrete pile, and two 600 mm diameter, strain-gage instrumented, bored piles. Invited predictors were provided with results of in situ, laboratory tests and dynamic tests. Test layout, soil information, and pile data are presented with calculations of pile capacity and load distribution, submitted predictions, and results of the static loading tests. The CPT-calculated capacities show considerable scatter—total values ranged from 500 to 1400 kN for the driven pile and from 1000 to 1900 kN for the bored piles. The static loading test on the driven pile showed an offset limit load of 1200 kN and a plunging capacity of 1500 kN. Despite pile movements of 100 mm for 1200 kN of applied load, neither of the bored piles showed signs of having reached an ultimate resistance value. Effective stress analysis of strain measurements for the bored piles showed the data to correlate to a β coefficient of 1.0 and a toe coefficient of 16. Most submitted predictions underestimated the capacity of the driven pile and overestimated the capacities of the bored piles.Key words: pile capacity, effective stress analysis, shaft and toe resistances, β coefficient, CPTU, dynamic testing.

A New Computer Program for Pile Capacity Prediction using CPT Data

An interactive computer program “GLAMCPT” is developed for application in soil profiling and prediction of pile load capacity using cone penetration test (CPT) and laboratory soil test results. GLAMCPT calculates pile capacity according to 10 selected methods from European design codes, refereed international publications and recommendations of professional institutions. To demonstrate the capabilities of the program, a database of comprehensive ground investigation and full-scale pile tests in sand, at a Belgian site, is analysed using GLAMCPT. The database comprises 11 static tests and 12 dynamic tests on piles of different construction techniques, including driven pre-cast concrete piles and screwed cast in-situ piles, installed using 5 different procedures. Prior to pile installation, CPTs were carried out at each proposed pile location. Comparison of GLAMCPT predictions with the observed pile capacities reveals that the most accurate of the existing methods yields an average, μ, of predicted to observed pile head capacity [P uh(p)/P uh(m)] equal to 0.94. The most consistent method produces a coeffcient of variation (COV) of [P uh(p)/P uh(m)] equal to 0.1 and ranking index (RI) of 0.08. Parametric studies have been carried out using GLAMCPT to formulate an improved predictive method, which yielded: μ = 0.99, COV = 0.07 and RI = 0.04.

Pile Driving Process Monitoring Based on Field Energy Measurements

ABSTRACT Modern hydraulic hammers are usually equipped with on-board energy measurement devices. Energy measurements in the field can therefore be taken continuously during entire pile driving processes. This paper outlines a method that directly uses energy measurements from the field for quantitative pile driving process monitoring. The kinetic energy delivered over one meter of pile penetration (KEMP) and the effective transferred energy delivered over one meter of pile penetration (EEMP) against soil resistance are calculated based on field measurements and are used as two indicators for pile driving process monitoring. The relationship between the EEMP and the pile capacity is confirmed by dynamic tests and static load tests on several well-monitored long steel H-piles. The EEMP values compare well with pile capacity predictions from a pile driving analyzer and from static analyses based on standard penetration tests, and with those measured from static load tests. The two energy indicators can also be used to effectively monitor the integrity of piles during construction. Potential pile damage caused by obstructions is illustrated by interpreting the KEMP and EEMP records of three abandoned long steel H-piles.

The influence of partial soil saturation on pile behaviour

Finite element analyses of geotechnical problems that involve partially saturated soils are commonly carried out using conventional fully saturated soil models. Such models, however, cannot successfully reproduce partially saturated soil behaviour. This paper investigates the influence of partial soil saturation on the behaviour of piles. A total stress constitutive model for partially saturated soils has been developed and implemented into the Imperial College finite element Program (ICFEP). This model, which is presented here, was used to perform a series of single pile analyses. The results of these analyses show the effect of partial saturation and in particular of fluctuations of the groundwater table on the behaviour of single piles. The effects of a rising groundwater table on pile behaviour are also investigated in the case of a pile foundation of a high-rise building in London. The paper indicates where prediction of pile capacity and displacement under different load and groundwater table fluctuations may be very different when using fully or partially saturated constitutive models.

Instrumented load tests in mudstone: pile capacity and settlement prediction

The present work stems from the design of a viaduct in South Wales, U.K., where full-scale pile testing was carried out to assess whether the proposed design methods would meet the required load capacity and settlement criteria for the working piles. Five fully instrumented large diameter bored cast in situ piles, up to 30 m deep, were installed in weathered mudstone and tested under vertical loading. A sixth pile, which had no shaft instrumentation, was formed with a voided toe. In conjunction with vast soil data from 218 site investigation boreholes, the extensive data produced from the load tests were analyzed to quantify the key parameters considered to influence load transfer and settlement behaviour. Each pile was first calibrated using four methods to establish the as-built stiffness, taking into account the nonlinearity of concrete and the effect of partial steel encasement. It is demonstrated that the current national norms for bored pile design in cohesive soil – soft rock are overconservative for South Wales ground conditions. To ameliorate this, alternative methods are proposed, which lead to improved reliability and accuracy in shaft and base capacity assessment. In addition, a numerical model is developed that can be used to predict the complete load-settlement variation up to the ultimate state. The model is sufficiently expounded to allow its immediate application in pile design by geotechnical engineers.Key words: piled foundations, load tests, bearing capacity and settlement, Mercia mudstone.

Evaluation of Pile Capacity Prediction Methods Based on Cone Penetration Testing Using Results from I-15 Load Tests

Nine axial pile load tests were performed in connection with the reconstruction of I-15 through Salt Lake Valley, Utah. In addition, load test data were collected for seven other piles in the valley. The soil profile at these test sites generally consisted of saturated soft to medium stiff silts and clays underlain by a dense sand bearing stratum at 25 to 30 m. Test piles consisted of closed-end steel pipe piles 300 to 600 mm in diameter with a concrete fill. Because cone penetration soundings were available for 15 of these test piles, these data were used to evaluate the accuracy of five available design methods that are based on cone penetration testing. Methods evaluated were the Dutch, Schmertmann, Tumay, Laboratoire des Ponts et Chaussées (LPC), and Fellenius methods. The Davisson method was used to define ultimate capacity. For most design methods, the estimated skin friction was on average only 40 to 60 percent of the measured resistance. The Dutch method predicted 82 percent of measured skin friction, on average, but still exhibited significant scatter for individual tests. Underprediction of skin friction by the LPC method was caused by unnecessarily severe restrictions for steel piles. Without these restrictions, the standard error was cut in half and estimated capacity was 96 percent of the measured capacity, on average. The LPC method also provided the best match with measured end bearing resistance (within 11 percent on average), whereas other methods overestimated capacity by 60 to 80 percent. Overall, the LPC method without side friction limitations gave 97 percent of the measured capacity on average and had the lowest standard error of any of the methods.

Prediction of Pile Capacity Using Neural Networks

A back-propagation neural network model for estimating static pile capacity from dynamic stress-wave data is proposed. The training and testing of the network were based on a database of 37 precast reinforced concrete (RC) piles from 21 different sites. The CAPWAP-predicted soil parameters were used as the desired output in training. Three different network models were used to study the ability of the neural network to predict the desired output to increasing degree of detail. The study showed that the neural network model predicted the total capacity reasonably well. The neural-network-predicted soil resistance along the pile was also in general agreement with the CAPWAP solution. The capability of the network to generalize from limited training examples was verified by its performance against dynamic test data obtained from non-RC piles.

Estimation of Vertical Capacity of Single Piles Considering Uncertainty

The uncertainty in soil parameters and the models that are used for predicting the vertical load-carrying capacity of single piles are addressed. The uncertainty is expressed in terms of fuzzy numbers and incorporated explicitly in the solution process. A new approach is developed by which the model uncertainty, the uncertainty associated with different empirical methods for predicting pile capacity, is reduced through the use of an aggregation technique. The weights used in the aggregation are determined on the basis of sensitivity analysis of each pile-capacity prediction model. Case studies are presented to illustrate the new approach and the developed computer program. The comparison between the predicted and the measured ultimate pile capacity indicates that the predicting capability of the developed computer program is very good.

Prediction of pile bearing capacity using artificial neural networks

It is well known that the human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully. In this study, error back propagation neural networks were utilized to predict the ultimate bearing capacity of piles. For the verification of applicability of neural networks, results of model pile load tests performed by the authors were simulated. In addition, the results of in situ pile load tests obtained from a literature survey were also used. The results showed that the maximum error of prediction did not exceed 25%, except for some bias data. These limited results indicated the feasibility of utilizing neural networks for pile capacity prediction problems.

Prediction of pile capacity from stress-wave measurements: a neural network approach: Y. K. Chow, W. T. Chan, L. F. Liu & S. L. Lee, International Journal for Numerical & Analytical Methods in Geomechanics, 19(2), 1995, pp 107–126

Prediction of pile capacity from stress-wave measurements: a neural network approach: Y. K. Chow, W. T. Chan, L. F. Liu & S. L. Lee, International Journal for Numerical & Analytical Methods in Geomechanics, 19(2), 1995, pp 107–126

Prediction of pile capacity from stress‐wave measurements: A neural network approach

AbstractA neural network approach for the prediction of pile bearing capacity by the stress‐wave matching technique is presented. The main advantage of this approach over the traditional manual or automated matching approach is that it avoids the time‐consuming process of iterative adjustment. This makes it feasible to determine the static pile capacity in real time in the field. Another benefit of this approach is that as more case histories become available, the neural network can be improved by learning from these new examples.A three‐layer back‐propagation network is set up to illustrate the capability of the proposed approach for 70 dynamically tested concrete bored piles. A wave equation model developed at the National University of Singapore and coded in the NUSWAP computer program is used to formulate the problem. Up to 14 of the 70 piles (20 percent) are used in training the network. The NUSWAP program is used to generate simulation training examples based on the manually fitted training examples for further training of the network. Different network configurations are examined. The trained network produces results exhibiting good stress‐wave matching qualities compared to those obtained by manual fitting. The pile bearing capacities predicted by the two approaches agree very closely. The load‐settlement curve and axial load distribution in the pile computed using the network‐predicted soil parameters are in good agreement with the field measurements obtained from a maintained load test.

Analytical model for an innovative pile test

In recent years a new type of pile test employing a short-duration load was developed that became known as pile statnamic test. The paper describes a method of dynamic analysis specifically formulated for this test. The aim of the analysis is twofold: to calculate the pile load displacements in such a way that a satisfactory match with the data measured during the test is obtained; and using the model parameters established from this comparison to predict the bearing capacity of the pile. The mathematical model is one-dimensional and accounts for slip as well as energy dissipation in the far field. Unlike the empirical parameters of the Smith model for pile driving analysis, the parameters of the proposed model are, for the most part, directly related to standard geotechnical parameters. The case studies conducted thus far and described in the paper indicate that the proposed mathematical model works very well and that the statnamic test may become a useful tool for pile capacity prediction. Key words : piles, pile driving, dynamic response, dynamic tests, bearing capacity.

Loading of piles and prediction of ultimate pile capacity : Popescu, M Proc Ninth Danube European Conference on Soil Mechanics and Foundation Engineering, Budapest, 2–5 October 1990P117–122. Publ Budapest: Akademiai Kiado, 1990

Loading of piles and prediction of ultimate pile capacity : Popescu, M Proc Ninth Danube European Conference on Soil Mechanics and Foundation Engineering, Budapest, 2–5 October 1990P117–122. Publ Budapest: Akademiai Kiado, 1990

Prediction of pile capacity, settlements and stability of piles and pile foundations : Proc 12th International Conference on Soil Mechanics and Foundation Engineering, Rio de Janeiro, 13–18 August 1989 V2, P779–782. Publ Rotterdam: A A Balkema, 1989

Prediction of pile capacity, settlements and stability of piles and pile foundations : Proc 12th International Conference on Soil Mechanics and Foundation Engineering, Rio de Janeiro, 13–18 August 1989 V2, P779–782. Publ Rotterdam: A A Balkema, 1989

Discussion of “ Summary of Pile Capacity Predictions and Comparison with Observed Behavior ” by R. J. Finno, J. Achille, H.‐C. Chen, T. Cosmao, J.‐B. Park, J.‐N. Picard, D. L. Smith, and G. P. Williams at the June 1989, Proc. Symp. on Predicted Observed Axial Behavior of Piles, and published in ASCE Geotechnical Special Publication No. 23 , by ASCE,

Discussion of “ <i>Summary of Pile Capacity Predictions and Comparison with Observed Behavior</i> ” by R. J. Finno, J. Achille, H.‐C. Chen, T. Cosmao, J.‐B. Park, J.‐N. Picard, D. L. Smith, and G. P. Williams at the June 1989, Proc. Symp. on Predicted Observed Axial Behavior of Piles, and published in <i>ASCE Geotechnical Special Publication No. 23</i> , by ASCE,