Bridges　are　critical　to　economic　and　social　development　of　a　country.　In　order　to　ensure　the　safe　operation　of　bridges,　it　is　of　great　significance　to　accurately　predict　displacement　of　monitoring　points　from　bridge　Structural　Health　System　(SHM).　In　the　paper,　a　CEEMDAN-KELM　model　is　proposed　to　improve　the　accuracy　of　displacement　prediction　of　bridge.　Firstly,　the　original　displacement　monitoring　time　series　of　bridge　is　accurately　and　effectively　decomposed　into　multiple　components　called　intrinsic　mode　functions　(IMFs)　and　one　residual　component　using　a　method　named　complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(CEEMDAN).　Then,　these　components　are　forecasted　by　establishing　appropriate　kernel　extreme　learning　machine　(KELM)　prediction　models,　respectively.　At　last,　the　prediction　results　of　all　components　including　residual　component　are　summed　as　the　final　prediction　results.　A　case　study　using　global　navigation　satellite　system　(GNSS)　monitoring　data　is　used　to　illustrate　the　feasibility　and　validity　of　the　proposed　model.　Practical　results　show　that　prediction　accuracy　using　CEEMDAN-KELM　model　is　superior　to　BP　neural　network　model,　EMD-ELM　model　and　EMD-KELM　model　in　terms　of　the　same　monitoring　data.　©　2020　Walter　de　Gruyter　GmbH,　Berlin/Boston　2020.