臺灣博碩士論文加值系統

新北市平地開發已趨近於飽和，且因應都市的快速開發及拉近城鄉差距，交通建設在山坡地開發已無可避免，但邊坡經人為挖填整地後，易受邊坡內、外在因素影響，常有滑動問題，故本研究案例之人工邊坡係以地錨擋土設施來進行穩固，但如何確保地錨擋土設施的穩定，是管理維護單位重要的課題，也直接影響到用路人安全。
本研究透過文獻整理及監測因子，彙整分析可能影響地錨荷重量之初步因子，利用統計軟體SPSS對初步因子與輸出變數進行相關性分析，客觀挑選出影響人工邊坡地錨荷重量之影響因子作為預測模式的輸入變數，以地錨荷重量作為輸出變數，並應用各種演化式推論模式透過資料庫的學習訓練與測試，找出輸入與輸出變數間的最佳映射關係，以得到最高預測準確率之推論模式。
經各種演化式推論模式預測及比較，結果顯示以「SOS-LSSVM」整體準確性最佳，測試之絕對百分比誤差(MAPE)為9.53%，屬於精準的預測，所以此模式有效取代傳統主觀經驗之預測，並可運用在無裝設地錨荷重計之人工邊坡預測，以快速且準確了解地錨的受力狀況，做為後續應變處理的參考。

Development in the flat area of New Taipei City is approaching saturation. To respond to the rapid development of the city and reduce the gap between urban and rural areas, building traffic facilities on hillsides has become inevitable. However, after land excavation, filling, and preparation, hillsides become susceptible to the influence of various internal and external factors and tend to slide. Therefore, this study investigated artificial slopes secured by ground anchors and retaining structures. Assuring the stability of ground anchors and retaining structures is a crucial topic for administrative and maintenance units, because the results can directly affect the safety of road users.
Through a literature review and factor monitoring, this study compiled and analyzed preliminary factors that can affect the load of ground anchors. Correlation analysis was conducted on the preliminary factors and output variables with statistical software. Factors that affect the load of artificial slopes were selected objectively as the input variables of the prediction model, and the load of ground anchors was used as the output variables. Subsequently, multiple evolutionary inference models were used to perform database learning, training, and testing, through which the optimal mapping relationships between input and output variables were identified. The inference model with the highest prediction accuracy was then obtained.
The prediction and comparison of various artificial intelligence reasoning models, the results show that the overall accuracy of "SOS-LSSVM" is the best. The mean absolute percent error (MAPE) for the test is 9.53%, this is a precise prediction. Therefor, this model effectively replaces the prediction of traditional subjective experience, it can be used in prediction of artificial slopes without load cell, and so fast and accurate understanding of the ground anchor force, therefor, it can be a reference for subsequent contingency processing.

目錄
第1章 緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 研究範圍與限制 4
1.4 研究方法與流程 5
1.4.1 研究方法 5
1.4.2 研究流程 6
1.5 論文架構 8
第2章 文獻回顧 9
2.1 五重溪順向坡監測系統 9
2.1.1 新北側環快五重溪段人工邊坡介紹 9
2.1.2 五重溪段人工邊坡之監測儀器 11
2.1.3 地錨荷重計監測資料庫 13
2.2 影響地錨荷重之因素 15
2.2.1 地錨之介紹 15
2.2.2 地錨荷重之影響因素 16
2.2.3 邊坡滑動影響因子 17
2.3 演化式推論模式 21
2.3.1 類神經網路(ANN) 21
2.3.2 倒傳遞類神經網路(BPNN) 23
2.3.3 支持向量機(SVM) 24
2.3.4 最小平方差支持向量機(LS-SVM) 26
2.3.5 演化式最小平方差支持向量機(ELSIM) 28
2.3.6 生物共生演算法最小平方差支持向量機(SOS-LSSVM) 29
第3章 地錨荷重推估模式建立 35
3.1 確立影響因子 35
3.1.1 因子評估 35
3.1.2 篩選因子 41
3.1.3 因子確立 44
3.2 建立資料庫 45
3.2.1 資料庫建立 45
3.2.2 正規化 46
3.3 模式訓練與測試 47
3.3.1 推論模式選用 47
3.3.2 模式執行過程 48
3.3.3 各模式十摺推論結果 57
3.4 模式成果比較 59
3.4.1 誤差衡量 59
3.4.2 各模式結果比較 61
第4章 推估模式之應用 66
4.1 案例資料及輸出結果 66
4.2 處置措施 70
4.3 實務應用機制 76
第5章 結論與建議 78
5.1 結論 78
5.2 建議 79
參考文獻 80

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