![]() ![]() Numerical results are compared with field loading test measurements and very good agreement is obtained. ![]() Necessary soil parameters were determined from extensive laboratory and in-situ soil tests. Modified Mohr-Coulomb constitutive model has been used to define the drained condition for sandy soil layers and undrained condition for clayey soil layers. Ī 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. 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. However, providing such a huge dataset of LDBP loaded to failure tests might be very complicated. ![]() 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. 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. However, in most cases, the obtained load-settlement curves from LDBP loading tests tend to increase without reaching the failure point or an asymptote. 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). ![]()
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