Ground settlement is a multifaceted geological phenomenon driven by natural and man-made forces, posing a significant impediment to sustainable urban development. Thus, ground settlement susceptibility (GSS) mapping has emerged as a critical tool for understanding and mitigating cascading hazards in seismically active and anthropogenically modified sedimentary basins. Here, we develop an integrated framework for assessing GSS in the Pohang region, South Korea, by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR)-derived vertical land motion (VLM) data with seismological, geotechnical, and topographic parameters (i.e., peak ground acceleration (PGA), effective shear-wave velocity (Vs30), site period (Ts), general amplification factor (AF), seismic vulnerability index (Kg), soil depth, topographic slope, and landform classes) through ensemble machine learning models such as Random Forest (RF), XGBoost, and Decision Tree (DT). Analysis of 56 Sentinel-1 SLC images (2017–2023) revealed persistent subsidence concentrated in Quaternary alluvium, reclaimed coastal plains, and basin-fill deposits. Among the tested models, RF achieved the best performance and strongly agreed with field evidence of sand boils, liquefaction, and structural damage from the 2017 Pohang earthquake. The very-high-susceptibility zones exhibited mean subsidence rates of −3.21 mm/year, primarily within soft sediments (Vs30 < 360 m/s) and areas of thick alluvium deposits. Integration of the optimal RF-based GSS index with regional building inventories revealed that nearly 65% of existing buildings fell within high- to very-high-susceptibility zones. The proposed framework demonstrates that integrating PSInSAR and ensemble learning provides a robust and transferable approach for quantifying ground settlement hazards and supporting risk-informed urban planning in seismically active and complex geological coastal environments.

The lack of modularity in building design information within multi-domain building performance analysis environments impedes efficient multidisciplinary analysis during the building design process. This study proposes a Functional Mock-up Interface (FMI)-based data model to facilitate the translation of building design information into a Building Information Modeling (BIM)-integrated building performance analysis framework that can be seamlessly integrated with object-oriented physical models. The proposed data model employs both FMI and BIM to decouple the design information required for physics-based analysis from existing Building Information Models. It then generates a physical BIM-based Functional Mock-up Unit (PBIM-FMU), which encapsulates the necessary building design information and can operate independently within a multi-domain building performance analysis environment. The PBIM-FMU can be readily interfaced with object-oriented physical modeling (OOPM)-based analysis models, as demonstrated in this study through its integration with an OOPM-based thermal analysis model for estimating annual building energy demand. To validate the proposed framework, simulation results from a manually constructed thermal analysis model were compared with those from a model integrated with the PBIM-FMU. The results were consistent, confirming that the data model supports accurate data exchange between BIM and multi-domain building performance simulation platforms.