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| ARTIFICIAL NEURAL NETWORK AND ITS APPLICATIONS IN GEOTECHNICAL ENGINEERING |
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| 洪昌祺、倪勝火 |
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| 類神經網路、大地工程、人工智慧 |
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| 類神經網路近年來引起大地工程師之研究興趣，一則希望瞭解類神經網路之基本原理與發展架構，另則希望藉瞭解其類型及特色以研究運用在工程問題之對策。本文大致介紹了類神經網路的發展演進、運作方式、常用模式及可資使用之軟體，文中並說明其與傳統技術方法之比較，以及其在大地工程上之主要應用方向，以提供工程界將來應用之參考。 |
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| The artificial neural network models have drawn engineer’s interest in recent years. The engineers need to study the kind and the feature of artificial neural network, which include the principle of fundamental and the frame of development, and to find the application of engineering. The paper introduces the artificial neural network which includes the operation manner and the process of evolvement and often uses these models and software, and presents the advantage and disadvantage between the artificial neural network and the traditional techniques, and review many results of study that have been used for geotechnical engineering. The effectiveness and applications of the artificial neural network are also presented in this paper. |
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| USE OF NEURAL NETWORK FOR LATERALLY LOADED PILE HEAD LOAD-DISPLACEMENT CURVE ESTIMATION |
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| 林三賢、李茂興 |
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| 基樁、側向承載力、類神經網路、載重變形曲線 |
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| 台灣由於基樁使用日益頻繁，如何有效評估基樁承載力，了解樁體與土壤間之互制作用，一直是國內大地工程學者持續研究、探討的對象。一般推估基樁側向承載力必須利用現地側樁試驗求得P-Y曲線後再以反覆曡代等半經驗方法求解。本文則嘗試利用類神經網路所具有的非線性複雜關係處理、容錯與學習能力，透過大量現地土壤與基樁材料特性等試樁資料的學習過程建立網路系統架構並藉以預測基樁受側力作用時之樁頭載重─變形曲線。 |
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| Increasing use of pile foundations in Taiwan, an efficient method to evaluate pile lateral capacity has been drawn attention to geotechnical engineers. In general, we often need in-situ pile lateral load test to obtain p-y curves for capacity evaluation. This paper is to use the neural network method, via available in situ soil data, pile geometry and material properties, to estimate the load-displacement curve at pile head for laterally loaded piles. |
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| APPLICATIONS OF CASE BASED REASONING ALGORITHM TO DETERMINE THE PRELIMINARY TUNNEL SUPPORT WORKS |
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| 俞旗文 |
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| 案例推理法、類神經網路法、隧道支撐設計 |
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| 本研究就蒐集所得約120個台灣地區隧道工程案例，建立案例資料庫。透過隧道支撐工之以往設計案例經驗，以案例類比分析之方法，研擬適用於台灣全區硬岩至中硬岩類之隧道支撐建議。案例類比分析之方法係採用統計方法結合案例推理法或類神經網路法等人工智能手段，針對案例資料庫有關隧道支撐設計資料部份進行案例分析，進而研擬具一般性之隧道支撐建議方法。類比輸入主要條件包括岩種分類、斷面跨度、岩盤狀況分級等三項參數，電腦類比分析輸出隧道支撐工之建議輸出結果項目包括開挖方法、輪進長度、噴凝土強度、噴凝土厚度、鋼支保尺寸、鋼支保間距、岩栓長度、岩栓縱距、岩栓橫距、襯砌強度、襯砌厚度等十一項。 |
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| In this study, numerous tunnel design case histories in Taiwan during the past 20 years were collected and utilized in order to establish a tunnel support design database. The database is further being analyzed by algorithms of artificial intelligence technologies such as case based reasoning (CBR) as well as artificial neural network (ANN). The results show that, with carefully selected tunnel case histories and adequate data mining method, rational designs of tunnel support works can be obtained by finding the most similar case or cases in the database. It this paper, only the database based on past experiences in the category of those tunnel excavated in the jointed rock mass have been studied. |
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| EVOLUTIONARY FUZZY NEURAL INFERENCE SYSTEM FOR DECISION-MAKING IN GEOTECHNICAL ENGINEERING |
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| 鄭明淵、柯千禾、張文德 |
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| 基因演算法、模糊邏輯、類神經網路、演化式模糊類神經推論模式、物件導向、演化式模糊類神經推論系統、大地工程決策 |
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| 大地工程方面的問題具有不確定、模糊與不完整資訊的特性，因此，在解決相關問題時多仰賴該領域專家經驗與知識進行決策，本研究的主要目的為模擬人類大腦決策過程，發展一「演化式模糊類神經推論系統」，透過過去之工程案例與經驗，學習累積專家決策過程與分析邏輯，輔助大地工程專家進行決策，以提昇大地工程決策的有效性與準確性。 |
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| Problems in geotechnical engineering are full of uncertain, vague, and incomplete information. Therefore, to solve most problems in geotechnical engineering depends on human experts’ decision-makings. The primary objective of this research is to imitate decision-making mechanism of human brain to develop an Evolutionary Fuzzy Neural Inference System (EFNIS) for assisting geotechnical experts to make proper decisions. |
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| APPLICATION OF ARTIFICIAL NEURAL NETWORK IN UNDERGROUND CONSTRUCTIONS |
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| 陳錦清、冀樹勇、俞旗文、詹君治 |
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| 人工智慧、類神經網路、隧道支撐設計、連續壁變形預測 |
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| 由於地下開挖工程之複雜性及影響因素之難以確實掌握，在目前地工技術發展之條件下，利用經驗指導設計是目前工程之主流。近年來快速發展的人工智慧技術，包括專家系統、案例推理系統及類神經網路，可綜合專家或工程案例經驗，有效解決新的工程問題。根據初步應用類神經網路於岩盤隧道支撐系統設計與軟地盤深開挖連續壁變形預測結果顯示，類神經網路已能利用以往工程案例經驗或施工監測資料，提供良好的工程設計建議或工程行為預測。 |
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| Due to the complexity and uncertainty of factors affecting the underground construction, utilization of previous experiences as guidance in solving new problem is the major trend of engineering design for such projects. The rapidly developing artificial intelligence technologies, including expert system, case-based reasoning and artificial neural network, can provide an effective way of solving new problem by using experts’ experiences or engineering case histories. Based on the preliminary results of applying the artificial neural network to rock tunnel support design and prediction of diaphragm wall deflection in braced excavation, it appears that the artificial neural network can be a viable method in providing design recommendation or predicting engineering performance by using the previous case histories or monitoring data |
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| PREDICTING AND ANALYZING GEOTECHNICAL DESIGN PARAMETERS FROM IN-SITU TESTS USING ARTIFICIAL NEURAL NETWORKS |
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| 盧炳志、倪勝火 |
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| 電子錐貫入試驗、相對密度、倒傳遞網路、半徑式函數網路、通用迴歸型網路 |
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| 對大地工程師而言，從現場試驗資料求取或預測工程所需設計參數是一件非常重要且具挑戰性的工作。本文討論應用類神經網路模式由現場試驗資料預測地工設計參數的流程與方法。除介紹倒傳遞網路的架構建立及其訓練方法之外，本文以一個利用電子錐貫入試驗(CPT)資料建立用來預測砂土相對密度Dr值的實際網路，探討以類神經網路模式預測地工設計參數的成效及其與統計迴歸方法之比較。此外，本文亦討論應用於分析現地試驗資料所具空間變異性之類神經網路，並以電子貫入錐之錐頭阻抗為例，比較不同型態類神經網路在分析空間變異性特性上之適用性。 |
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| Predicting and analyzing engineering parameters from in-situ tests is an important and challenging task for geotechnical engineers. In this study, an artificial neural network approach is proposed to predict these design parameters. In this paper, a brief introduction of back-propagation networks is provided and then a network for predicting relative density Dr from CPT measurements is established. Discussions on the established network are presented, along with comparisons of the results by this network with existing methods. Meanwhile, the application of artificial neural network approach on the spatial variability characteristic of in-situ tests is also discussed in this study. Some details of the development of various network models for analyzing CPT measurements and the comparisons of the network predictions are presented. |
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| PREDICTION OF DIAPHRAGM WALL DEFLECTION IN DEEP EXCAVATIONS USING NEURAL NETWORKS |
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| 冀樹勇、陳錦清、詹君治 |
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| 深開挖，類神經網路，誤差倒傳遞 |
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| 本文以以往案例資料累積為深開挖資料庫，透過誤差倒傳遞類神經網路模式對資料庫案例之學習與施工壁體監測變形之饋入，預測後續施工所可能產生之壁體變形。壁體變形的預測結果，可進一步透過理論計算與經驗法檢討壁體的應力與地盤沉陷，提供施工單位作為整體安全及鄰房損害評估之依據。文中並以台北地區深開挖之工程案例，印證預測之結果和實際監測值是否相符。 |
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| In this paper, the diaphragm wall deflections of case histories and the construction stages of the concerned project were collected as the input database of the artificial neural network (ANN) with the error back-propagation (BP) model for the learning algorithm, and thus the wall deflections can be predicted for the next construction stage. Furthermore, the stress of the wall and the settlement of the ground evaluated by theoretical calculation or empirical formulae can be used as the basis for safety assessment. Case histories were adopted to verify the agreement of wall deflection between predicted and measured data. |
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| RESEARCH ON EARTHQUAKE PREDICTION FROM THE VIEW OF GEOTECHNICAL ENGINEERING |
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| 鄭魁香 |
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| 地震預測、地震趨勢分析、大地工程 |
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| 本文由大地工程的板塊構造學說、類神經網路應用、地質災害分析和大地遙測與監測技術應用等出發，完成(1)台灣地區的地震地體構造區分圖；(2)台灣地區未來10年的地震危險圖；(3)年度地震趨勢分析方法；以及(4)嘉義示範區的地震危險性與地質災害分析等有關地震與震害預測之研究。 |
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| Bared on the theory of plate tectonics, neural network, geologic hazards analysis and the remote sensing and monitoring of geotechnical techniques, it also finishes about the map of seismotectonics in Taiwan area, seismic risk map form 2001 to 2010 in Taiwan areas, the yearly tendency analysis of seismicity and the harmful analysis of seismic at Chiayi city. |
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