Despite the difficulties in obtaining the ultimate capacity of the large diameter bored piles (LDBP) using the in situ loading test, this method is the most recommended by several codes and design standards. However, several settlement-based approaches, alongside the conventional capacity-based design approach for LDBP, are proposed in the event of the impossibility of performing a pile-loading test during the design phase. With that in mind, natural clays usually involve some degree of over consolidation; there is considerable debate among the various approaches on how to represent the behavior of the overconsolidated (OC)stiff clay and its design parameters, whether drained or undrained, in the pile-load test problems. In this paper, field measurements of axial loaded to failure LDBP load test installed in OC stiff clay (Alzey Bridge Case Study, Germany) have been used to assess the quality of two numerical models established to simulate the pile behavior in both drained and undrained conditions. After calibration, the load transfer mechanism of the LDBP in both drained and undrained conditions has been explored. Results of the numerical analyses showed the main differences between the soil pile interaction in both drained and undrained conditions. Also, field measurements have been used to assess the ultimate pile capacity estimated using different methods.

Abstract

The full-scale static pile loading test is without question the most reliable methodology for estimating the ultimate capacity of large diameter bored piles (LDBP). However, in most cases, the obtained load-settlement curves from LDBP loading tests tend to increase without reaching the failure point or an asymptote. Loading an LDBP until reaching apparent failure is seldom practical because of the significant amount of settlement usually required for the full shaft and base mobilizations. With that in mind, the supervised learning algorithm requires a huge labeled data set to train the machine properly, which makes it ideal for sensitivity analysis, forecasting, and predictions, among other unsupervised algorithms. However, providing such a huge dataset of LDBP loaded to failure tests might be very complicated. In this paper, a novel practice has been proposed to establish a labeled dataset needed to train supervised machine learning algorithms on accurately predicting the ultimate capacity of an LDBP. A comprehensive numerical parametric study was carried out to investigate the effect of both pile geometrical and soil geotechnical parameters on both the ultimate capacity and settlement of an LDBP. This study was based on field measurements of loaded to failure LDBP tests. Results of the 29 applied models were compared with the calibrated model results, and the variation in LDBP behavior due to change in any of the hyperparameters was discussed. Accordingly, three primary characteristics were identified to diagnose the failure of LDBPs. Those characteristics were utilized to establish a decision tree of a supervised machine learning algorithm that can be used to predict the ultimate capacity of an LDBP.

A finite element model is established using MIDAS GTS NX 2018 software, in order to simulate the behavior of an instrumented large diameter bored pile, installed in multi layered soil and tested under three different loading and unloading cycles at Damietta Port Grain Silos project site. Modified Mohr-Coulomb constitutive model has been used to define the drained condition for sandy soil layers and undrained condition for clayey soil layers. Necessary soil parameters were determined from extensive laboratory and in-situ soil tests. Numerical results are compared with field loading test measurements and very good agreement is obtained. The effect of dilatancy angle on pile load transfer mechanism was investigated, and results of the study showed important effect for the dilatancy angle on the pile settlement values and the load distribution along the pile shaft. Results obtained also showed that the plastic zone below the base of the pile at failure extended laterally to about seven times the pile diameter and vertically to about 5 times the pile diameter.

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