在大地工程反算分析中，對不同材料參數進行數值模擬佔據大部分時間。為
了提高反算分析效率，本研究開發一個大地工程反算分析系統。透過動態分散式
計算架構，運用現有資源進行分散式計算。閒置的計算資源可隨時加入或離開反
算分析中，讓不同材料參數可以同時在各個計算資源中，進行數值模擬工作。並
藉由克利金代理模型取代原有數值模擬，降低整體反算分析中所需的數值模擬工
作量。
在系統開發完成後，本研究在四種最佳化測試函數、三種不同大地工程模擬
案例中，分別探討：第一、本研究實作的基因演算法、蟻群演算法、粒子群演算
法、自適應共變異數矩陣演化策略演算法四種不同最佳化演算法各自的表現；第
二、本研究實作的克利金法代理模型是否在足夠取樣下，可取代真實模型進行計
算；第三、預期改善值與估計相對誤差兩種不同的克利金變異數評估方法，在反
算過程中，對代理模型使用率與最佳化結果的影響；第四、代理模型與動態分散
式計算對整體反算效能的助益。
最後展示本研究開發之系統應用於真實大地工程案例之表現。在兩個真實案
例上，使用預期改善值輔助基於克利金法代理模型最佳化演算法，比不使用代理
模型的最佳化演算法能降低約 40%左右的反算時間。另外，在這兩個真實案例上
本研究開發之動態分散式系統，以 12 個工作節點輔助計算，降低 90%左右的反
算時間。

In the back-analysis in geotechnical engineering, numerical simulation of different material parameters takes up most of time. In order to improve the efficiency of backanalysis in geotechnical engineering, this study develops a back-analysis system for geotechnical engineering, which takes advantage of dynamic distributed computing architecture to use existing resources for distributed numerical simulation. Unoccupied computers can take or quit the numerical simulation task in the back-analysis process at any time; therefore, numerical simulation tasks of different material parameters can be calculated in each computer synchronously. Also, this study replace numerical simulation with surrogate model to reduce the workload of numerical simulation in back-analysis.
After the system has been developed, this study discusses the following things
based on four benchmark functions and three geotechnical simulation cases. First, the performance of genetic algorithm, ant colony optimization, particle swarm optimization and covariance matrix adaptation evolution strategy. Second, whether the Krigingbased surrogate model can replaced real model if there are sufficient samples. Third, the impact of two different kriging variance evaluation methods, expected improvement and estimated relative error, on the usage rate of surrogate model and the optimization result in the process of back-analysis. Last, the improvement of back-analysis efficiency by using surrogate model and dynamic distributed computing.
In the end, this study shows the performance of this system in two real geotechnical cases. In both cases, using expected improvement as evaluation methods to assist the optimization algorithm based on Kriging-based surrogate model is better than the optimization algorithm without surrogate model by reducing 40% of backanalysis time. Additionally, in both cases, the dynamic distributed computing architecture of the this study can reduce 90% of back-analysis time by using 12 computers to assist in numerical simulation.

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