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.

This study introduces a novel space feasibility assessment process and evaluates its effectiveness through a comparative analysis with a conventional manual process. The proposed method is designed to enhance spatial comprehension and integrate building performance analysis, thereby supporting budgetary considerations during the early design phase. By providing a more intuitive and interactive environment, the system enables stakeholders—such as building owners—to communicate their spatial requirements to architects and professionals more clearly and efficiently. To validate the effectiveness of the proposed approach, participants completed two distinct scenarios: (1) a manual space feasibility assessment, and (2) a system-supported space feasibility assessment utilizing the proposed method. Participant performance was measured in terms of speed and accuracy in each scenario. Additionally, a user satisfaction survey was conducted to evaluate the usability of the system’s functionality. The experimental results provide an empirical basis for comparing the proposed process with the manual approach. Findings demonstrate that the proposed process enables more efficient and accurate space feasibility assessments, thereby validating its effectiveness as a user-centered decision-support tool during early-stage architectural planning.

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (Vs30), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide.

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.

This study introduces a novel space feasibility assessment process and evaluates its effectiveness through a comparative analysis with a conventional manual process. The proposed method is designed to enhance spatial comprehension and integrate building performance analysis, thereby supporting budgetary considerations during the early design phase. By providing a more intuitive and interactive environment, the system enables stakeholders—such as building owners—to communicate their spatial requirements to architects and professionals more clearly and efficiently. To validate the effectiveness of the proposed approach, participants completed two distinct scenarios: (1) a manual space feasibility assessment, and (2) a system-supported space feasibility assessment utilizing the proposed method. Participant performance was measured in terms of speed and accuracy in each scenario. Additionally, a user satisfaction survey was conducted to evaluate the usability of the system’s functionality. The experimental results provide an empirical basis for comparing the proposed process with the manual approach. Findings demonstrate that the proposed process enables more efficient and accurate space feasibility assessments, thereby validating its effectiveness as a user-centered decision-support tool during early-stage architectural planning.

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (Vs30), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide.

This study addresses the inverse problem of identifying adiabatic temperature rise (ATR) characteristics for mass concrete using the Physics-Informed Neural Network (PINN). The characteristics are defined by parameters representing the maximum ATR and temperature increasing rate. The PINN-based identification of these parameters was conducted using virtual experimental data generated through numerical simulation with three different ATR models. To assess the robustness of the PINN in the identification process, noise was introduced into the data. The observation period and noise condition of the data were used as variables to evaluate the performance of PINN-based parameter identification. In addition, 10 independent PINN training sessions were conducted, and the results were statistically analyzed. The identification performance of the unknown parameters was influenced by the observation period. The PINN accurately identified the parameters used in the virtual experiments, even with short-term observation data, regardless of the noise. Statistical analysis indicates that the PINN demonstrates significant reliability and consistency in parameter identification.

The demand for compact housing is on the rise, driven by the need for floor plans that accommodate stakeholders’ preferences. However, clients frequently struggle to convey their spatial needs to professionals, such as architects, due to a lack of means to present evidence, such as spatial configurations or cost projections. This study seeks to develop a methodology that translates sketched, data-driven spatial requirements into 3D building components within BIM (Building Information Modeling) to enhance spatial comprehension and offer building performance analysis, assisting in budget considerations during the initial design stages. The research methodology encompasses the formulation of a process model, its implementation, and subsequent validation. The process model outlines the data flow within the system and delineates necessary functionalities. Implementation includes the creation of systems and user interfaces for the integration of various components. Validation confirms the system’s capability to automatically transform sketched spatial requirements into BIM model elements, such as walls, floors, and roofs, and to autonomously compute material and energy expenses based on the BIM model. This system enables clients to effectively generate 3D building components from sketches, aiding stakeholders in spatial understanding and building performance evaluation through the generated BIM models.

At the foundational phase of architectural design, it is of the utmost importance to precisely capture and articulate the visions and requirements of stakeholders, including building owners. This critical step ensures that professionals, including architects, can effectively translate the initial concepts into actionable designs. This research was directed towards developing a framework to facilitate the decision-making process by efficiently depicting the client’s intentions. This study demonstrates a framework that leverages deep learning to automate the creation of Building Information Modeling (BIM) models from sketched data. The framework’s methodology includes defining the necessary processes, system requirements, and data for system development, followed by the actual system implementation. It involves several key phases: (1) developing a process model to outline the framework’s operational procedures and data flows, (2) implementing the framework to translate sketched data into a BIM model through system and user interface development, and, finally, (3) validating the framework’s ability to precisely convert sketched data into BIM models. Our findings demonstrate the framework’s capacity to automatically interpret sketched lines as architectural components, thereby accurately creating BIM models. In the present study, the methodology and framework proposed enable clients to represent their understanding of spatial configuration through Building Information Modeling (BIM) models. This approach is anticipated to enhance the efficiency of communication with professionals such as architects.

This study demonstrates the research and development of a visualization method called thermal performance simulation. The objective of this study is providing the results of thermal performance simulation results into building information modeling (BIM) models, displaying a series of thermal performance results, and enabling stakeholders to use the BIM tool as a common user interface in the early design stage. This method utilizes a combination of object-oriented physical modeling (OOPM) and BIM. To implement the suggested method, a specific BIM authoring tool called the application programming interface (API) was adopted, as well as an external database to maintain the thermal energy performance results from the OOPM tool. Based on this method, this study created a prototype called the thermal energy performance visualization (TEPV). The TEPV translates the information from the external database to the thermal energy performance indicator (TEPI) parameter in the BIM tool. In the TEPI, whenever BIM models are generated for building design, the thermal energy performance results are visualized by color-coding the building components in the BIM models. Visualization of thermal energy performance results enables non-engineers such as architects to explicitly inspect the simulation results. Moreover, the TEPV facilitates architects using BIM as an interface in building design to visualize building thermal energy performance, enhancing their design production at the early design stages.