This study presents a comprehensive scientometric review of cement-less ultra-high-performance concrete (UHPC) with the objective of identifying research trends, key contributors, dominant themes, and critical knowledge gaps in this emerging field. A systematic bibliometric analysis was conducted using the Scopus database, from which 59 peer-reviewed journal articles published between 2014 and 2024 were selected following rigorous screening criteria. Scientometric mapping was performed using VOSviewer to analyze publication trends, keyword co-occurrence, leading journals, influential authors, and active research regions. The findings reveal a sharp increase in research output after 2020, reflecting growing interest in geopolymer-based UHPC due to sustainability concerns. Existing studies predominantly focus on mechanical properties, particularly compressive strength and steel fiber reinforcement, while durability-related aspects such as corrosion resistance, fire performance, and long-term structural behavior remain underexplored. Higher sand-to-binder ratios (up to 0.8) were found to improve packing density and mechanical performance, achieving compressive strengths up to 160.7 MPa, while silica fume contents around 30% enhanced compressive strength by approximately 25% and fracture energy by nearly 50%. The novelty of this work lies in being the first dedicated scientometric assessment of cement-less UHPC, providing a quantitative overview of research evolution while systematically highlighting critical gaps and future research directions to support its effective structural application.
Reliable Digital Terrain Models (DTMs) are crucial for most engineering and environmental applications, especially where accurate elevation is required. While conventional leveling offers high vertical accuracy, it needs long time periods, causing high work costs, particularly for wide regions. GNSS-based methods that provide fast data acquisition may serve as an effective alternative; however, achieving reliable vertical accuracy remains a challenge. Accordingly, this study proposed a practical approach that integrates the Post-Processed Kinematic GNSS technique with Constrained Triangulated Irregular Network (TIN) modeling to improve elevation accuracy. In this method, accurate leveling cross sections distributed along the study area are used as vertical constraints to improve interpolation reliability. The performance of the model is validated using independent cross-section data observed using precise leveling. Statistical analysis demonstrates a strong correlation between generated DTM elevations and leveling data, evidenced by a coefficient of determination (R2) of 0.9915 and a vertical RMSE of 0.0608 m, with residuals mainly within +/- 0.10 m for the majority of observations. The results validated that the Constrained TIN modeling method effectively maintains the accuracy of PPK-derived elevations and decreases vertical discrepancies. The proposed methodology, integrating PPK observations with constrained TIN modeling, achieves reliable decimeter-level vertical accuracy, making it appropriate for various engineering and environmental applications that needs high-precision terrain representation.
Data-driven models offer the computational speed needed for rapid post-earthquake assessment, but their uncertainty estimates must be trustworthy to support safety decisions. This study reveals that Monte Carlo dropout uncertainty for RC frame seismic response prediction is severely miscalibrated: 95% prediction intervals capture only 46.6% of actual responses, meaning Immediate Occupancy assessments under ASCE 41-17 would be unconservative in over half of cases. We address this through post-hoc Temperature Scaling calibration. While a global scaling parameter (T* = 4.40) reduces calibration error by 91.4%, we discover that the optimal calibration factor varies systematically across structural locations: T* ranges from 1.94 at fixed-base nodes to 5.52 at mid-height floors—a 2.8-fold variation that single-parameter approaches cannot capture. This spatial variation reflects physical differences in prediction uncertainty: boundary-constrained nodes exhibit lower uncertainty requiring less scaling, while mid-height nodes dominated by higher-mode contributions show greater uncertainty underestimation. Building on this finding, we propose floor-adaptive calibration using location-specific scaling factors. Compared to global calibration, this approach reduces average calibration error by an additional 62%, with improvements of 61-70% at ground and top floors, where global calibration performs worst. The method requires no model retraining—only a lookup table mapping floor levels to optimal scaling factors. Validation across 12 RC frames (3-7 stories), 2,400 analysis cases, and 35,000+ node-level predictions confirms that spatially adaptive calibration provides more reliable uncertainty estimates across all structural locations, enabling trustworthy confidence intervals for performance-based post-earthquake assessment.
Floods are the most frequent disasters caused by combinations of natural and anthropogenic factors. Given the increasing intensity and frequency of floods, especially in Asian mega-urban regions, effective Disaster Risk Reduction (DRR) strategies are critical. This study presents a comparative evaluation of the national flood risk assessment methods in Indonesia and China, followed by a flood risk map analysis calculated using the Chinese and Indonesian standards for flood risk assessment, specifically for a case study in Bandung City, Indonesia. We found that the Chinese standard method produces a broader spatial identification of high flood risk areas, influenced by rainfall intensity and topography, which better represents pluvial flood risks. Meanwhile, the Indonesian method produces localized high flood risk near rivers, which better represents fluvial flood risks. In the case study of Bandung City, the occurrence of pluvial floods was more dominant than fluvial floods. Therefore, the spatial accuracy of the Chinese method was slightly higher than the Indonesian method. The study emphasizes the importance of a national flood assessment method that balances accuracy, data availability, computational resources, and local/regional characteristics to cope with the increasing risk in urbanized flood-prone areas.
This study aims to develop a practical and accessible approach for evaluating the mechanical behavior of Cementitious Treated Sand (CTS) under passive confinement using Glass Fiber Reinforced Polymer (GFRP) wraps. A method utilizing three GFRP layer configurations was applied to investigate the confinement effect and assess the role of confining stiffness. Path-dependency was analyzed through derived confining pressure rates, and Mohr-Coulomb failure analysis was used to determine shear-strength parameters. Analysis of plastic volumetric behavior revealed that after an initial elastic state, the material dilates upon yielding—activating the confinement mechanism—before recompacting under sufficient confining pressure due to pore structure collapse. Results indicate that the proposed novel constitutive model successfully predicts both axial and lateral stress-strain responses. It accurately represents the nonlinear stress-strain relationship, the transition in volumetric behavior, and the interaction between axial and lateral strains through the proposed dilation formulation. The model incorporates a plastic dilation rate model to capture the dilation-to-compaction transition and demonstrates excellent agreement with experimental results across all confinement levels. This framework provides a reliable analytical tool for designing soil stabilization schemes using passive confinement, offering engineers a practical alternative to conventional geotechnical analysis while enhancing reproducibility, sustainability, and applicability across diverse construction projects.
