Long-span　bridges　often　serve　as　critical　infrastructures.　It　is　therefore　important　to　accurately　assess　the　immediate　structural　performance　and　build　high-fidelity　finite　element　(FE)　models　of　these　bridges　to　improve　the　disaster　prevention　capacity　of　urban　transportation.　Achieving　the　above　goals　relies　on　efficient　and　reliable　model　updating　and　analysis　techniques　suitable　for　complex　structures.　However,　most　of　the　previous　studies　were　based　on　commercial　software　platforms,　which　were　expensive,　slowly　updated　and　had　complicated　application　programming　interfaces,　and　thus　limited　the　development　of　further　in-depth　research.　Based　on　the　open-source　software　OpenSees　and　Python,　a　program　framework　suitable　for　the　model　updating　of　complex　structures　was　proposed.　An　efficient　parallel　optimization　algorithm　for　FE　model　updating　(FEMU)　of　complex　civil　structures　was　developed　in　Python.　The　program　interfaces　were　developed　to　link　the　model　analysis　module　and　the　parallel　computing　module　to　realize　the　parallel　analysis　and　FEMU　of　complex　structures.　On　this　basis,　the　damage　identification　of　a　simply-supported　beam　was　taken　as　an　example　to　validate　the　feasibility　and　accuracy　of　the　program　framework.　Further,　the　scaled　physical　model　of　Sutong　cable-stayed　bridge　in　a　shake　table　test　was　selected　as　the　object.　The　modal　assurance　criterion　(MAC)　was　used　to　match　the　simulated　modal　shapes　and　frequencies　with　those　identified　from　the　shake　table　test　to　form　the　objective　function　for　the　FEMU.　The　FEMU　of　the　bridge　was　carried　out　on　a　high-performance　computing　platform　to　validate　the　computational　efficiency　of　the　program　framework.　The　results　show　that　the　framework　can　effectively　tune　the　high-fidelity　FE　model　of　the　long-span　cable-stayed　bridge　to　match　up　with　the　experimental　measurement.　The　error　between　the　simulated　frequencies　and　the　physical　model　was　below　1%.　Furthermore,　the　accuracy　of　the　updated　model　was　verified　by　comparing　the　measured　responses　with　the　predicted　ones　using　the　updated　FE　models　under　the　seismic　input　with　a　PGA　of　0.1　g.　The　research　outcome　can　provide　references　for　high-fidelity　and　data-driven　modeling　of　complex　long-span　bridges　based　on　open-source　platforms.　©　2025　Tsinghua　University.　All　rights　reserved.