由於本省交通量大幅成長，胎壓高達898kPa(130psi)，遠超過早期設計用之標準胎壓552kPa(8Opsi)，為了探討重車超載破壞，本研究採用本省軸重超載範圍(62kN~169kN)，運用有限元素程式進行車轍與龜裂力學分析，討論載次、軸重及力學數值等影響參數之相關性，並經類神經網路學習訓練，以最小總平方誤差最佳化建立軸重當量因子(EALF)模式。結果發現，重車超載對路面破壞乃成4.4~4.5次幾何級數地增加，即如以二倍標準軸重的超載卡車輾壓路面，鋪面龜裂與車轍破壞程度可能變成標準軸重的21~23倍，超載破壞程度可見一斑。

Due to rapid growth in the size and weight of heavy vehicles in Taiwan， the highest tire pressure reaches 898kPa (130psi)，which exceeds designated tire pressure 552kPa (80psi). The equivalent axle load factor (EALF) using overloading ranging from 62kN to 169kN， based on mechanistic analyses of a finite-element program related to rutting and cracking， axle-load， and loading number， was established by minimum total squared error optimization through Artificial Neural Network (ANN) training and was proposed to analyze overloading effects on flexible pavement in Taiwan. Based on the EALF model in this study， it was found that the growing rate of pavement damage due to overloaded trucks was approximated by the“4.4 or 4.5-power law" progression; thus， two times the standard axle-load rolling on pavement could cause 21 or 23 times the serious damage， for example.