以FLAC程式探討傾斜互層地盤之承載行為

The Study on the Bearing Behavior of An Inclined Two-layer Ground Using the FLAC code

王克仁(Ke-Jen Wang)

指導教授 ： 馮道偉 ； 葛德治

中原大學/工學院/土木工程研究所/碩士(2003年)

https://doi.org/10.6840/cycu200300315

摘要

摘 要
台灣常見的地質構造如層面、斷層、節理、葉理等弱面，而他們現地之傾態可能為水平或傾斜。若地盤的組成包含上述弱面，當然其力學行為和均質完整地盤會有所差別；其承載擾動區也不再如 Terzaghi 者所假設對稱發展之 I、II、III 區。本論文主要目的是利用數值分析方法以研究剛性條形基礎座落於傾斜互層地盤上之承載行為，並且以類神經網路來初步建立其極限承載力之預估公式。
本研究主要以有限差分連續分析之套裝軟體 － FLAC程式進行傾斜互層承載行為之探討，影響因子為加載邊界與互層界面露頭之距離 (D) 與基礎寬度 (B) 之比值 (D/B)、互層之傾角 ()、上下二層之凝聚力 (c1, c2) 及摩擦角 (1, 2)。基礎加載是以應變控制為主，由荷重－沈陷曲線及擾動範圍找出極限承載力，其中擾動範圍是以位移向量圖所定義出。由堅硬層下覆軟弱層及軟弱層下覆堅硬層兩種型式之基本例可瞭解傾斜互層地盤細部之承載行為及破壞機制。此外，利用倒傳類神經網路來發展廣義的互層地盤極限承載力評估公式，共選用3000組不同地層參數資料進行迴歸，參數範圍為凝聚力 (c) = 0~1MPa、摩擦角 () = 0~30a，並考慮兩層無凝聚性、無凝聚性及凝聚性、兩層凝聚性三種地盤互層型式。
分析結果顯示，堅硬層下覆軟弱層其極限承載力會隨 (D/B) 及傾角 () 之增加而增加 (最終趨近堅硬上層之極限承載力)；而軟弱層下覆堅硬層其極限承載力並不會因 (D/B) 及傾角 () 的改變而有太大的變化 (皆趨近軟弱下層之極限承載力)。而上下層之凝聚力 (c1, c2) 及摩擦角 (1, 2) 亦對地層之極限承載力有所影響。至於類神經網路分析，乃以測試預估之偏差量小於 10% 之條件來建立一個互層地盤極限承載力之預估公式；此外於其線性分析模式中，可初定出各種互層形式下之最大影響因子，例如：非凝聚性互層土壤-軟在上時，互層傾角 () 為其最大影響因子。

關鍵字

類神經網路分析 ； FLAC程式 ； 極限承載力 ； 承載行為 ； 傾斜互層地盤

並列摘要

ABSTRACT
The geologic structures such as faults, joints, bedding planes, inter-bedding, and foliation, are very common in Taiwan, and their in-situ orientations may be horizontal or inclined. The rock formation associated with these features, of course, yields a mechanical behavior totally different from that of a homogeneous, intact mass. Especially when subjected to surface loading, the bearing disturbance regions developed within it may not resemble the symmetrical zones I, I, III as ideally proposed by Terzaghi. The main objective of this thesis is to numerically study the bearing mechanism of a rigid strip foundation of width (B) sitting on an inclined two-layer ground, and to establish a preliminary prediction model of ultimate bearing capacity for such a case by use of the neural networks analysis.
The bearing behavior of such a mass was simulated by the FLAC code, in which finite-difference scheme is employed for both the spatial and time domains. The influence factors examined were the ratio of the distance between footing corner and bedding outcrop (D) to footing width (B), bedding inclination (), the shear strengths (c1, c2, 1, 2) of two layers. The foundation loading mode used is strain-controlled, and the ultimate bearing capacity (qu) is determined from the loading versus settlement curve and the disturbed zone beneath the foundation is localized according to the displacement vector plots. For understanding the distinct bearing behavior of an inclined two-layer mass, two conditions were selected: one with a strong layer (S) overlying a weak layer (W), and another with W overlying S. Besides, the neural networks analysis with backward propagation algorithm (NNAB) was adopted to develop a general bearing capacity formulas for such a ground system, and about three thousand of cases were run by FLAC with formation properties: the friction angle () varying from 0 to 30a and cohesion (c) from 0 to 1MPa. Three classes were considered: both layers cohesionless, one cohesionless and another cohesive, and both cohesive.
The simulation results show that: 1) for a S/W case, qu increases with both D/B and , with a trend approaching to the qu of a pure S mass; 2) for a W/S case, qu does not vary a lot with D/B and , and its value is very close to the qu of a pure W mass; 3) both (,c) values of two formations affect qu to some certain extent (but not merely qu of each formation). The NNAB established a fair predictive model of qu for an inclined two-layer mass, with a prediction error less than 10%. In its linear model analysis, the most influential factor for each situation was also identified, and for instance, such a factor is bedding inclination () for a two-layered cohesionless system with a weaker top layer.

並列關鍵字

FLAC ； ultimate bearing capacity ； inclined two-layer ground ； neural networks analysis. ； bearing behavior

參考文獻

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