近年來由於天氣變化劇烈，導致邊坡災害不斷發生，舉凡強降雨、地下水、地震皆會對邊坡造成一定影響，因此邊坡穩定的問題一直以來是土木工程師需要面對的；其中尤其以地震最具不確定性，現今技術仍無法精準預測地震的發生，所以我們必須從歷史發生的地震大小來預先分析邊坡之穩定性，藉此以當做邊坡災害防治的參考依據。在此研究中使用了Hoek-Brown 破壞準則做為岩石邊坡穩定性之評估標準，使用了AI人工智慧技術來代替重複性的工作，並且在預測方面可以達到一定的精準度。本篇研究的主要目的為提供不同AI人工智慧技術來預測大範圍岩石邊坡穩定性及可靠度分析，並且比較不同AI技術的優缺點以及適用性，在本研究中將根據三種不同的方法，極限分析法、三種不同的AI技術及蒙地卡羅法以分析理解岩石邊坡穩定在各種不同岩石種類以及不同地區之地震情況。

In recent years, due to severe weather changes, slope disasters continue to occur. Heavy rainfall, underground water and earthquakes will affect the slope to a certain extent. Therefore, the problem of stability has always been faced by civil engineers. Among them, earthquakes are the most uncertain. Current technology is still unable to accurately predict the occurrence of earthquakes. Therefore, using the historical earthquake records would be a reliable reference for the prevention and control of slope disasters. This concept is employed in this study. Moreover, the Hook-Brown failure criterion was used as an evaluation criterion for rock slope stability. Artificial intelligence technology was used instead of repetitive work, and high accuracy was achieved in prediction. The main purpose of this study is to apply different artificial intelligence techniques to predict the stability and reliability of large-scale rock slopes. In addition, the advantages and disadvantages of different artificial intelligence technology and applicability are compared. In this study, different theories and techniques, limit analysis methods, three different AI techniques, and Monte Carlo method are adopted to analyze the rock slope stability and reliability.

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