This　study　explores　an　integrated　framework　combining　in-situ　test-based　numerical　and　data-driven　modeling　to　assess　the　performance　of　a　deep　excavation-tunnel　system.　To　achieve　the　goal,　a　case　history　of　deep　excavations　adjacent　to　existing　tunnels　in　silt/sand-dominated　sediments　is　introduced　to　establish　a　base　three-dimensional　finite　element　(3D-FE)　model.　In-situ　tests　such　as　cone　penetration　test　(CPT/CPTU)　and　seismic　dilatometer　test　(DMT/SDMT),　as　an　alternative　to　laboratory　testing,　are　used　to　determine　a　set　of　advanced　constitutive　model　parameters.　The　established　excavation-tunnel　numerical　model　is　then　validated　against　filed　monitoring　data.　A　dataset　from　numerical　simulation　is　created　for　training　and　testing　four　machine　learning　models　(i.e.,　artificial　neural　network　(ANN),　support　vector　machines　(SVM),　random　forest　(RF),　and　light　gradient　boosting　machine　(LightGBM)),　which　predict　the　maximum　wall　deflection,　ground　surface　settlement,　horizontal　and　vertical　displacements　of　the　tunnel.　Results　show　that　the　ANN　model　outperforms　other　models　in　prediction　capacity.　Its　generalization　ability　in　practice　is　further　enhanced　by　comparing　field　measurement　data　and　empirical　equations.　The　findings　suggest　that,　with　the　integrated　in-situ　tests,　FE　and　ANN　modeling　could　be　used　to　predict　deformation　responses　of　deep　excavations　close　to　existing　tunnels　in　soft　soil.　The　present　study　is　useful　and　valuable　for　practical　risk　assessment　and　mitigation　decisions.　©　2025　Tongji　University