本研究提出一個以前向式倒傳遞類神經網路為基礎模式，來預測三維開口槽溝對震波垂直振
幅之阻隔效果。輸入前人對於槽溝阻隔震波振幅研究之相關重要參數，先經前處理之處理分析程
序篩選後，選擇了槽溝尺寸、形狀、面積及振動基礎與三維開口槽溝相關位置、振動基礎沉埋、
土壤性質等具推廣性之輸入研究參數。在網路訓練過程中，使用.. Cascade Correlation學習程序，和藉由自動調整學習速率與慣性因子的 Extended-Delta-Bar-Delta (EDBD) 演算法，來改善前人採試誤法來決定隱藏層節點個數和網路的學習參數的缺點，建立以三維開口槽溝對震波阻隔成效所代表的平均垂直振幅降低比.. (Ary )open 為其輸出值的網路。經研究後，依網路訓練後的驗證程序，結果顯示類神經網路模式在此方面具有相當良好的學習及預測能力。

This paper presents a back-propagation neural network model to estimate the screening effects of vertical amplitude of seismic waves on 3D open trenches. Input parameters are selected by using genetic algorithm with important parameter analysis and pre-processing transfer functions. The parameters are the size of trench dimension, the relative position of vibrating foundation and trenches, the surrounding environment of soil material property and so on. The number of hidden layer nodes in the network is determined by Cascade Correlation learning processing. The learning parameters of network are determined by Extended-Delta-Bar-Delta algorithm to regulate learning-rate and momentum constant automatically. The output parameter of network is the average vertical amplitude reduction rate.
According to the results of this study, the neural network model is a very good approach for 3D open trench in estimating the screening effect of vertical amplitude of seismic wave.