Pavement　deflections　are　critical　indicators　in　pavement　design　and　acceptance　programs.　Falling　weight　deflectometer　(FWD)　technique　has　been　widely　adopted　to　measure　the　dynamic　deflections　in　pavement　engineering.　Although　the　dynamic　effect　of　FWD　impulse　loading　on　deflections　is　significant,　FWD　deflections　are　still　commonly　interpreted　and　examined　using　the　static　analysis　programs　in　acceptance　projects　because　of　their　ease　of　implementation　and　high　computational　efficiency.　This　may　lead　to　an　inability　to　effectively　control　the　construction　quality　of　pavements.　Accordingly,　this　study　aims　to　propose　a　simple　and　accurate　approach　for　converting　static　deflection　to　dynamic　deflection.　Layered　elastic　theory　(LET)　and　spectral　element　method　(SEM)　were　adopted　to　generate　the　theoretical　static　and　dynamic　deflection　databases,　respectively.　Based　on　the　databases,　a　simple　approach　is　proposed　to　predict　the　dynamic　deflection　only　using　four　static　deflections.　The　proposed　approach　shows　lower　computational　workload　and　higher　accuracy　than　other　traditional　formula　forms　which　include　equivalent　layer　moduli　and　thicknesses.　In　the　field　engineering　cases,　the　calculated　static　deflections　can　be　2　∼　3　times　higher　than　the　measured　ones,　while　the　predicted　dynamic　deflections　from　the　proposed　approach　match　the　theoretical　dynamic　deflections　well　and　are　comparable　to　the　measured　deflections　for　both　asphalt　pavements　with　asphalt　binder　or　inorganic　binder　bases　and　granular　bases.　This　indicates　that　dynamic　deflections　can　more　effectively　control　the　construction　quality　of　asphalt　pavements　in　the　FWD　deflection-based　design　program.　Meanwhile,　the　drawbacks　of　traditional　prediction　methods　are　investigated　and　evaluated.　The　proposed　approach　exhibits　simpler　form　and　higher　accuracy,　while　avoiding　the　drawbacks　of　traditional　methods.　Combining　with　the　common　static　LET-based　programs,　the　proposed　approach　provides　a　more　reasonable　and　practical　way　for　the　construction　quality　acceptance　of　asphalt　pavements　using　FWD　data.　©　2024　Elsevier　Ltd