Buildings

To address the small ATC-63 record set for collapse-oriented motion selection and the limited interpretability of data-driven approaches, this study proposes a framework for identifying structural-collapse-critical ground motions. Using 5074 records from the PEER NGA-West2 database, we applied STA/LTA event detection and extracted multi-source features. A Gaussian mixture model (GMM) was then used to perform unsupervised clustering and identify four physically interpretable groups. LightGBM, XGBoost, and Random Forest were employed to test the separability of the cluster labels, with all three models achieving F1 scores above 0.89 and LightGBM reaching an accuracy of about 93%. SHAP-based feature-importance analysis was used at the model level to clarify feature contributions and improve interpretability. Cluster 2 exhibits markedly higher relative seismic energy, stronger time-domain variability, and more dominant frequencies, forming a typical strong-motion hazard signature. For external engineering verification, 22 ATC-63 far-field records were mapped onto the full dataset to examine cluster-level enrichment and coverage. Cluster 2 shows significant enrichment in engineering markers and high coverage and is therefore identified as the collapse-sensitive phenotype cluster (COP). Overall, the framework provides a technical basis for ground-motion selection in collapse assessment, fragility analysis, and design evaluation.

Urban waterfront walkways are everyday public built environments where people commonly engage in slow walking, yet evidence remains limited that links what pedestrians see to immediate psychophysiological responses under controlled first-person dynamic exposure. To address this gap, we developed a fixed-speed, fixed-duration VR walk-through model using real-world 360° panoramic video and quantified scene visual composition via computer vision-based image analysis. Based on the visible shares of key components (greenery, water, sky, hardscape, and built structures), clips were grouped into four interpretable waterfront typologies: Vegetation-Enclosed, Built-Dominant, Hardscape-Plaza, and Blue-Open. Fifty healthy adults completed within-subject VR exposures to the four typologies (50 s per clip), while multimodal physiological signals and brief affect and landscape ratings were collected before and after exposure. The results showed that scenes with more water and vegetation coverage, along with expansive views, were associated with promoted autonomic nervous system calming responses, whereas scenes with fewer natural elements and higher built structure density were more likely to induce tension responses. Negative emotions decreased significantly across all four scene experiences, though artificial scenes concurrently exhibited emotional improvement alongside physiological tension. Overall, brief first-person dynamic VR exposure can yield immediate emotional benefits, and waterfront designs combining water proximity, abundant greenery, and expansive vistas may maximize short-term restorative potential, offering quantitative targets for health-supportive planning and retrofitting.

The construction sector in developing countries continues to face persistent challenges related to cost overruns, projects delays, and compromised quality performance. In Oman, these concerns have hindered the ability of construction industry to achieve sustainable project success. This study aimed to empirically assess the effect of value management (VM) implementation on overall project success (OPS) in building projects in Oman using the partial least squares structural equation modelling (PLS-SEM). The questionnaire content was validated by 30 construction management experts using the content validity index (CVI). Data from 272 professionals across the construction sector in Oman were collected using a structured questionnaire to assess VM activities and OPS. The results indicated that VM implementation has a significant positive effect on OPS, with all phases except the creativity phase showing statistically significant relationships. The analysis showed a high coefficient of determination (R² = 0.803) and a large effect size (f² = 0.793), indicating that VM implementation explains over 80% of the variance in project success. Cost (β = 0.945) was the most significant success measure, followed by time (β = 0.883) and quality (β = 0.843). These results highlighted that the application of VM improves the performance of construction projects through the optimization of cost efficiency, improving scheduling reliability, and ensuring quality compliance. This study provided empirical evidence on the role of VM implementation in improving the overall project success by quantitatively linking VM phases to cost, time, and quality performance in developing countries. The findings offered practical guidance for policymakers and practitioners to integrate VM early in construction projects.