- 學位論文
應用有限元素法與類神經網路建立動力撓度反算模式
Integrate the FEM and ANN into the Dynamic Model of Rigid Pavement Backcalculation
摘要
落重撓度儀於鋪面強度檢測之應用已逐漸成熟且為目前之趨勢，各種軟體亦不斷被發展以更加精確地反算鋪面之勁度值。在反算軟體中，參數之設定，例如初始勁度值、各層厚度與堅硬層深度等，將會影響反算結果之精確性。本研究利用有限元素軟體模擬剛性鋪面受動力荷重之反應情形，並探討在不同鋪面組成下各參數間之相互關係。利用撓度盤以及最大撓度回彈時間兩個參數，觀察其與堅硬層深度與勁度、面層厚度與勁度以及路基土壤之勁度值等因素間是否有關係性存在。結果顯示，最大撓度回彈時間、堅硬層深度以及路基土壤勁度三者間之關係最為密切，故利用此三個參數建立堅硬層深度之計算公式；而路基土壤勁度值則利用BDI-F2(Richard,1998)建立公式計算得知。以上述兩個公式進行迭代與利用靜力反算程式(MODCOMP)設定不同堅硬層深度，以及以ABAQUS假設不同層之勁度值之試誤法三者所得之路基土壤勁度值進行比較。由結果得知以迭代以及利用ABAQUS試誤法兩者之結果較為接近，並且MODCOMP未考慮堅硬層所得之反算結果與AQBAQUS所得者相差最大。因此，建議往後使用反算軟體時，不可將堅硬層參數忽略。另一方面，本研究亦將有限元素軟體之模擬結果用以建構動力類神經網路反算模式。將此一模式與其他常用之反算軟體以現地機場撓度進行反算得知，本研究所建構之類神經網路反算模式，其反算值較靠近實驗室值且數值穩定，尤以路基土壤勁度之反算結果最為明顯。因此，本研究所構建之類神經反算模式不但可確實反算鋪面勁度，其運算速度亦較一般傳統反算軟體快。
並列摘要
The use of falling weight deflectometer (FWD) in pavement strength inspection is getting matured and popular. Also, different kinds of software have been developed in order to backcalculate more precisely the pavement stiffness. However, the setting of parameters, such as initial stiffness, layer thicknesses, and depth of stiff layer, would influence the accuracy of backcaluation results. Thus, the finite element software, ABAQUS, was used in this research for simulating pavement responses to dynamic loadings and for evaluating the relationships between different parameters under various types of pavement compositions. Deflection bowls and rebounding time were analyzed to observe the relations with the depth and stiffness of stiff and surface layers as well as the stiffness of subgrade. It was found that rebounding time had close relationships with stiff layer depth and subgrade stiffness, and they were used to develop the equation for the calculation of stiff layer depth. In addition, subgrade stiffness could be obtained by the equation composed of BDI and F2. By comparing the backcalculated subgrade stiffness obtained with three different methods, i.e. by the iteration of two equations mentioned above, by setting different stiff layer depths in static backcalculation software, MODCOMP, and by using different layer stiffness in ABAQUS, it was found that the results of the first and the last methods were closest. In those of the second method, the result without considering stiff layer thickness was quite different. Therefore, it was shown that the depth of stiff layer could not be neglected while using softwares to backcalculate. Besides, the simulation results of the finite element software were also used as the training data of artificial neural network (ANN) to build dynamic backcalculation models. The backcalculation results of the dynamic ANN backcalculation models were compared with those of other common backcalculation softwares, and it was found that the former ones were more stable and closer to the laboratory values, especially for those of subgrade stiffness. Therefore, the dynamic ANN backcalculation model built in this research can backcalculate pavement stiffness more precisely and faster.
參考文獻
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