Long-span　bridges,　often　exposed　to　challenging　harsh　natural　environments　with　severe　weather　conditions,　necessitate　real-time　examination　of　load-deformation　characteristics　to　ensure　structural　integrity　and　safety.　Previous　studies　have　primarily　focused　on　investigating　the　causes　of　deformation　in　bridge　structures　under　different　single-load　conditions　during　severe　natural　disasters,　utilizing　physics-based,　mechanics-based,　and　data-driven　methods.　However,　these　methods　cannot　achieve　fully　achieve　effective　analysis　of　the　real-time　effects　of　multi-factor　loads　on　bridge　deformation,　particularly　in　the　presence　of　dynamic　and　simultaneous　loads　such　as　wind　or　temperature　variations.　A　novel　data-driven　method　is　proposed　based　on　a　state-of-the-art　real-time　updating　artificial　neural　networks　(ANNs)　algorithm　to　investigate　the　real-time　coupling　relationship　between　multi-loads　and　bridge　deformation,　enabling　real-time　prediction　of　bridge　deformations.　Additionally,　the　real-time　characteristics　between　structural　deformation　and　multi-loads　are　explained　by　incorporating　SHapley　Additive　exPlanation　(SHAP)　in　harsh　natural　environments.　The　proposed　method　has　been　validated　on　the　1,006-meter　Forth　Bridge　in　Scotland,　showing　high　accuracy　in　real-time　displacement　prediction.　The　9-day　testing　dataset　demonstrated　the　R2　values　for　Y　and　Z　direction　deformations　were　found　to　be　0.98　and　0.87,　respectively.　The　performance　metrics　for　each　day　indicated　that　the　majority　of　Y　and　Z　direction　deformations　had　R2　values　exceeding　0.8,　with　RMSE　and　MAE　values　below　30　mm.　The　SHAP　analysis　revealed　that　an　increase　in　wind　speed　leads　to　intensified　Y　direction　deformation　(larger　SHAP　values),　while　temperature　has　a　significant　impact　on　Z　direction　deformation　(smaller　SHAP　values).　Moreover,　the　weight　influences　of　each　load　on　the　deformation　are　not　fixed.　The　study＇s　findings　demonstrate　that　the　proposed　method　enables　accurate　long-term　prediction　and　assessment,　allowing　precise　monitoring　and　prevention　of　abnormal　risks　in　bridges　under　harsh　environmental　conditions.　©　2024