ABSTRACT

This study was performed to examine pile uplift bearing capacity and to develop a novel approach to simulate pile load-settlement response using a new supervised computational intelligence (CI) approach. To reach the planned aim, a series of experimental studies were conducted on concrete piles subjected to uplift loading. . According to the statistical analysis, pile effective length (lc), applied load (P), pile flexural rigidity (EA), sand-pile friction angle (δ), and pile aspect ratio (lc/d) were pronounced to play a key role, at different contribution levels, on model output. To evaluate and verify the efficiency of the proposed approach, comparison have been made between the employed training algorithm with experimental pile load-test, and with those specified by a number of design procedures. The results revealed an outstanding agreement between the target and predicted pile-load settlement, thus yielding a coefficient of correlation (R), and a minimum root mean square error (RMSE) of 0.985 and 0.059 respectively, thus, in parallel with a relatively insignificant mean square error level (MSE) of 0.0039.

摘要

这项研究旨在检查桩的抗拔承载能力，并开发一种使用新的监督计算智能 (CI) 方法来模拟桩荷载-沉降响应的新方法。为了达到计划的目标，对承受上浮荷载的混凝土桩进行了一系列试验研究。. 统计分析表明，桩的有效长度（lc）、外加荷载（P）、桩的抗弯刚度（EA）、砂桩摩擦角（δ）、桩的纵横比（lc/d）是关键。在不同的贡献级别上对模型输出的作用。为了评估和验证所提出方法的效率，已将所采用的训练算法与实验桩载荷测试与许多设计程序指定的算法进行了比较。