本研究利用智慧型自動道路檢測車（Automatic Road Analyzer 簡稱ARAN）進行路網層級（Network Level）的鋪面檢測，蒐集相關鋪面決策資訊，並以SPSS(Statistical Package for the Social Science)對檢測資料進行相關的統計分析，並利用集群分析法（Cluster Analysis）建立鋪面家族(Pavement Family)，以決定其鋪面績效劣化模式。
本研究以國際糙度指標（International Roughness Index）做為鋪面績效指標，並利用類神經網路(Artificial Neural Networks )進行該指標與鋪面破壞之相關性分析，以評估該指標於本土化應用之適用性。本研究以養護效益最大為單目標函數，分析路段養護最適時機，並建構最適養護策略，並以模糊線性規劃(Fuzzy Linear Programming)處理多目標決策問題，以LINDO軟體（Linear Interactive and Discrete Optimizer）求解線性規劃最適解。提供經費限制下，最適的養護決策。

In this research, firstly we use the Automatic Road Analyzer to do the pavement test of network level and to collect the decision information about the pavements. Then, we apply SPSS (Statistical Package for the Social Science) to do some statistical analyses toward the test data. Besides, we take advantage of the cluster analysis to build the pavement family to decide the deteriorated models of pavement performance.
In this research, we use the IRI (International Roughness Index) as the pavement performance index. In addition, we use ANN (Artificial Neural Networks) to do related analyses between IRI and the various damages of pavements in order to evaluate whether the index can be applied properly in local. We regard the maximum of maintenance benefit as the single object function to decide the best moment and the optimal strategy of pavement maintenance. At last, we use the methodology of fuzzy linear programming to deal with multiply object decision problems and apply LINDO（Linear Interactive and Discrete Optimizer）to solve the linear program solution. According to IRI, this research provides the optimal pavement maintenance strategies under the limited budget.

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