本文主要以有限差分連續分析之套裝軟體－FLAC程式進行承載行為之探討。基礎加載是以應變控制為主，從荷重－沈陷曲線及擾動範圍找出極限承載力，其中擾動範圍是由位移向量圖所定義出。由Case A－硬砂岩下覆軟頁岩 (HS-SS)；Case B－軟頁岩下覆硬砂岩 (SS-HS)，可瞭解互層地盤之承載行為，砂頁岩分別代表典型的力學特性。此外，利用倒傳類神經網路發展廣義的互層地盤承載力評估公式，由FLAC模擬出不同地層參數資料共910組進行迴歸，參數範圍在摩擦角(f) = 0~30°，凝聚力(c) = 0~1 MPa之間，並考慮三種形式分別為無凝聚性、無凝聚性及凝聚性、凝聚性土壤互層，以建立評估公式。
結果顯示，硬砂岩下覆軟頁岩其極限承載力會隨層厚比之增加而增強，而軟頁岩下覆硬砂岩則減少，前者影響深度為4倍基礎寬度，而後者影響深度為2.5倍基礎寬度。上下層之凝聚力、摩擦角在某一深度內會影響極限承載力(並不是僅有每層之承載力會影響)。當類神經網路測試預估之偏差量小於10﹪時，則即建立起一正確互層地盤承載力之預估公式，另外，在線性分析中，可找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，上層摩擦角為其最大影響因子。

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 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 a two-layered formation system, two cases were selected: Case A with a hard sandstone layer (HS) of thickness (H1) overlying a soft shale (SS), and Case B with SS overlying HS, each material assigned with typical mechanical properties. Besides, the neural networks analysis with backward propagation algorithm (NNAB) was adopted to develop a general bearing capacity formulas for a two-layered formation system, and about nine hundreds of cases were run by FLAC with formation properties (for common soils): the friction angle (f) varying from 0 to 30° and cohesion (c) from 0 to 1MPa. Three situations were considered: both layers cohesionless, one cohesionless and another cohesive soils, and both cohesive.
The simulation results of HS-SS system show that qu increases with H until H approaches 4B for Case A, qu decreases with H until H approaches 2.5B for Case B, and both (f,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 a two-layered soil system, 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 the friction angle of the top layer (f1) for a two-layered cohesionless system with a weaker top layer.

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