本研究利用GeoStudio軟體分析掩埋場受降雨影響之穩定性，首先使用SEEP/W模組計算降雨入滲至掩埋場內之孔隙水壓值，利用各不同之降雨參數(如降雨強度、降雨延時及降雨雨型)來模擬掩埋場內降雨入滲之情形。掩埋場穩定性則是利用SLOPE/W模組來分析，並針對以下參數：降雨強度(I)、降雨延時(T)、降雨雨型(RP)、掩埋面高度(H)、掩埋面長度(L)、掩埋場背靠邊坡角度(α)、掩埋完成面邊坡角度(β)、垃圾層單位重(γ1)、地工膜布單位重(γ2)、垃圾層凝聚力(c1)、地工膜布凝聚力(c2)、垃圾層之內摩擦角( )，以及掩埋面底部界面摩擦角( 2)等，進行掩埋場參數變異性分析，以了解各參數對掩埋場邊坡穩定性之影響程度。接著以假設場址為例，利用結合類神經網路 (ANN) 及一階可靠度法 (FORM) 或蒙地卡羅模擬法 (MCS) 之可靠度分析技術 (ANN-based FORM、ANN-based MCS)，來探討掩埋場邊坡受降雨影響之可靠度。此法相較於傳統之一階二次矩法(FOSM)，無論在系統反應之模擬、計算效率及可靠度精度之提昇上，都有明顯的改善。透過本文之探討，本研究在降雨對坡地型廢棄物掩埋場邊坡穩定性影響之分析上，具體提供了一個可資落實，及具風險觀念的可靠度評估方法，其成果可作為擬定防災策略的參考。
Due to limited land resources and high population density, a large portion of municipal solid waste (MSW) landfills in Taiwan are constructed in mountainous regions. Being located at the sub-tropical area and surrounded by sea, slopes are usually unstable because of the concentrated rainfall during the wet period and thus failures of MSW landfills occurred occasionally. As to slope stability analysis, safety factors are common used in engineering practice. However, this deterministic approach not only does not consider the influence of randomness and uncertainties of soil properties, analysis model and associated parameters on the analysis results, but also has not any implications about the failure probability of the critical state according to the factor of safety. In this research, a probabilistic method to evaluate the reliability for the stability of MSW landfills on slope is proposed. The rainfall conditions and uncertainties of each analysis parameter will be taken into account. By the probabilistic approach, the evaluation results will be more representative with application value.
In this research, the software, GeoStudio, is used to analyze the stability of MSW landfills on slope considering rainfall-infiltration. Seepage analysis is carried out by SEEP/W module to simulate the pore water pressure distribution, and then the slope stability of MSW landfills is assessed by SLOPE/W module. Parameter studies are first done to explore the influence of factors on the stability of MSW landfills. These factors include rainfall intensity (I), rainfall duration (T), rainfall pattern (RP), and the geometric and mechanical properties of MSW landfills, including height of landfill (H), length of landfill (L), slope angle of the back (α), slope angle of the waste body (β), unit weight of waste, unit weight of geomembrane, cohesion of waste, cohesion of geomembrane, friction angle of waste, and interfacial friction angle of geomembrane. Then 100 different combinations of parameters are generated and associated stability analyses of MSW landfills on slope are performed assumed that each parameter is uniform distributed around its reason ranges. Following, the performance of the stability of MSW landfills is interpreted by the artificial neural network (ANN) trained and verified according to the above-mentioned 100 analysis results. The rainfall fragilities for the stability of MSW landfills are then evaluated by first-order reliability method (FORM), or Monte-Carlo simulation (MCS) in terms of different level of required factor of safety based on varied rainfall intensity, duration, pattern, etc. The evaluation model of ANN-based FORM or ANN-based MCS proposed in this study is superior to traditional reliability method, such as first-order second-moment method (FOSM), in view of many aspects, such as system modeling, computational efficiency, and analysis precision. Based on these methods, the rainfall-related reliability of the MSW landfills on slope can be assessed easily, efficiently and accurately. It can be used as an effective auxiliary tool to design the countermeasures for disaster mitigations.