Premature　damage　to　heavy-duty　pavement　has　been　found　to　be　significantly　caused　by　the　vehicle-highway　alignment　interaction,　especially　in　mountainous　regions.　This　phenomenon　was　further　verified　by　field　pavement　damage　investigations　and　field　tests.　In　order　to　elucidate　the　potential　mechanism　of　this　interaction,　it　is　important　to　address　the　vehicle　dynamic　loads　generated　by　the　interaction　between　vehicle　and　pavement.　Based　on　this,　the　paper　realizes　a　new　method　of　vehicle　dynamic　load　prediction　using　data　mining　techniques,　namely　artificial　neural　network　(ANN)　and　support　vector　machine　(SVM)).　The　data,　including　dynamic　loads　and　highway　geometric　characteristics,　were　collected　by　a　wheel　force　transducer　(WFT)　and　global　positioning　system　(GPS),　respectively.　The　coefficient　of　determination　(R-2)　and　root　mean　square　error　(RMSE)　were　used　to　evaluate　the　performance　of　the　prediction　models.　The　results　showed　that　the　proposed　dynamic　load　prediction　model　established　by　ANN　was　better　than　that　by　SVM.　Moreover,　the　model　implied　that　dynamic　loads　were　highly　correlated　with　curvature　and　longitudinal　grade,　and　furthermore,　curvature　was　found　to　have　a　larger　effect.　The　proposed　dynamic　load　prediction　technique　provides　a　feasible　and　rapid　approach　to　identify　pavement　damage　under　complex　vehicle-highway　alignment　interactions.