過去十多年來，隨著觀光產業的蓬勃發展及平地人口漸達飽和，許多
人口、基礎建設漸漸往山坡地發展，於是人身安全及構造物之保護就成
了邊坡災害的重要議題。而地下水位因素又為邊坡災害防治的主要課題
之一，故本研究針對岩石邊坡利用極限分析法基於 Hoek-Brown 破壞準則
進行岩石邊坡之穩定性分析，接著利用蒙地卡羅法，分析岩石邊坡基於
不同地下水位概率分佈之破壞機率，最後結合人工智慧-極限學習機的技
術，建立岩石邊坡在地下水位影響下之穩定性及可靠度的預測模型，不
僅大幅降低了分析時間，也大大提升了分析的便利度，建立準確、快速
的邊坡預警系統。本研究根據三個不同理論，極限分析法、人工智慧-極
限學習機技術以及蒙地卡羅法進行岩石邊坡於地下水位影響下之穩定性
及可靠度分析。

Over the past few decades, people and infrastructure have started moving to
mountain area because of two factors: Flourishing of tourism industry, and the
saturated population in plain area. As a result, slope disasters are often concerned
as an important issue on infrastructure and personal safety protection. When it
comes to measure the potential of slope disasters, the groundwater level of slope
should always be evaluated carefully. This study primarily aims to use AI
technique to investigate the rock slope stability and reliability base on Hoke
Brown failure criterion. Limit analysis method is used in this study to analyze
the stability of rock slopes, which base on different groundwater level condition
and rock strength parameters. After building the database of rock slope stability,
Monte Carlo method is thus used in this study to investigate the reliability of
rock slope. To shorten the analysis time while ensure the accuracy, AI technique
ELM is used in this study to create prediction model for slope stability and
reliability. This study conducts with three different concepts, namely as, Limit
analysis method, Monte Carlo method and Extreme learning machine.

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