本文採用最佳化分析概念發展混合式基因演算法動力回算程式DBFWD-HGA，並與迭代法DBFWD與基因演算法DBFWD-GA等回算程式進行比較研究，以模擬國內柔性鋪面結構及實際案例鋪面結構之回算結果。研究結果顯示：在理論分析回算結果中，使用局部搜尋的策略不僅可以增加基因演算法的局部搜尋能力，提高收斂率，亦可有效解決基因演算法於微調上不足的問題，減少運算時間，故在搜尋最佳解的方法上，能有效改善基因演算法運算效率；但在案例分析上則差強人意，未若傳統迭代法DBFWD或資料庫程式MODULUS為佳，其可能為所探討之回算參數影響因子有限而致。 In this study, a hybrid genetic algorithm computer program DBFWD-HGA is developed for data analysis of Falling Weight Deflectometer(FWD) test on flexible pavements. In order to explore and compare same forward calculation program with respect to different algorithms, this research simulates the results of flexible pavement and field case study by using DBFWD, DBFWD-GA, and DBFWD-HGA. In the theoretical analysis backcalculation, the results show that using the local search method can not only increase the local search capability and convergence rate of genetic algorithm but also effectively solve the fine-tuning deficiency of genetic algorithm and save operation time. Therefore, in the means of searching the optimum solution, this method can effectively improve the operation efficiency of genetic algorithm. However, regarding the case analysis, the results show that the method is not superior to traditional DBFWD and MODULUS due to limited variable study on factors affecting the backcalculations.
