http://yetl.yabesh.ir/yetl1/handle/yetl/19022 2026-04-07T13:14:32Z http://yetl.yabesh.ir/yetl1/handle/yetl/4309124 Automatic Density-Based Clustering for Operational Modal Analysis Upama Bhusal; Jale Tezcan Estimation of modal parameters from ambient response measurements is a central task in structural health monitoring and rapid condition assessment of structures after natural disasters or other damaging events. This task has traditionally required considerable interaction from the user. Automation of this task enables online assessment of the integrity of structures, increases the accuracy of results by removing user error, and reduces analysis time and associated costs. This paper proposes an unsupervised approach for automatic extraction of modal parameters from measured vibration data. A novel heuristic to automate an existing clustering algorithm called density-based spatial clustering of application with noise was introduced and validated. This heuristic uses a histogram as a nonparametric density estimator and is applicable to data sets containing arbitrarily shaped clusters. The automated clustering procedure can be used with any output-only system identification method that produces modal estimates over a range of model orders. An application was presented using numerical simulations of a 5-story shear frame model under ambient excitations. System identification was performed using covariance-based stochastic subspace identification, and modal estimates were obtained using the proposed approach. The modal estimation process was repeated using 200 independent realizations of structural responses. The accuracy of the predictions was investigated by comparing the predicted modal parameters to the theoretical values from eigenvalue analysis. The results demonstrate the promise of the proposed approach. Validation of the proposed method on real structures will be addressed in future studies. 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4309123 Computer Vision-Enabled Roof Subassembly Damage Detection from Hurricanes Using Aerial Reconnaissance Imagery Rachel Hamburger; Tracy Kijewski-Correa Coastal communities are increasingly vulnerable to devastating losses caused by extreme climatological events such as hurricanes. As risk escalates, an urgent need arises to accelerate learning from these disasters. Investments in postdisaster data collection have yielded comprehensive imagery databases that, when coupled with breakthroughs in computer vision algorithms, present new opportunities for automated damage assessments. Research in this area has been primarily directed toward assigning building-level damage ratings from at-a-distance imagery or localizing highly granular damages from up-close imagery. This study introduces a workflow to quantify granular subassembly damages using at-a-distance imagery, focusing on residential roof subassemblies most vulnerable to hurricane winds: the roof cover, substrate, and framing. The workflow optimizes computational resources by sequencing aerial images through two classification models before feeding into a semantic segmentation model to quantify damage on a HAZUS-MH compatible scale. To test the performance of this workflow and the influence of image quality, we deployed the models using a sample of 373 single-family homes in Calcasieu Parish, Louisiana—a community heavily impacted by Hurricane Laura in August 2020. We explored differences in ground truth damage data from homeowners and engineers, opting for the latter because it is less likely to factor in interior and content losses not visible from imagery. The results demonstrate the potential to advance computer vision techniques for the quantification of granular damages from reconnaissance imagery. Although models tend to classify subassembly damage states in high-resolution images with moderate accuracy, accuracy decreases with the damage level likely because more severe damage states manifest as less structured images. This may suggest the need to better refine the features distinguishing more severe damage and expand training sets with a wider variety of severe damage images encompassing a broader range of disorganization. Additionally, limiting the number of classes in segmentation tasks can lead to more accurate results. 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4309122 Rapid Detection of Landslides for the Timely Response of Disaster Mitigation and Relief Defang Liu; Guoyang Liu Landslides on the Tibetan Plateau can induce disaster chains, which can extend the damage from one point to an area. At present, the timely treatment of landslides after they have occurred is the only feasible way to avoid the expansion of the disaster. Searching on foot is still the primary method of locating landslides. However, this method is inefficient and time-consuming. To address this problem, this paper designs a one-shot landslide detector (AOSLD) to quickly locate sudden landslide hazards on the Tibetan plateau. AOSLD introduces decoupled head and anchor-free modules based on YOLO-V3. Mixup and Moscia are used to enhance the robustness of the model. The results show that AOSLD can simultaneously achieve multitype, multiobjective, and multiscale landslide detection. AOSLD outperforms 16 kinds of state-of-the-art methods. Moreover, AOSLD is deployed to smartphones, which dramatically improves landslide detection and the efficiency of translating landslide research results into applications. AOSLD is also combined with the F-16 aerial simulator to detect sudden landslide hazards on the Tibetan plateau. To our knowledge, this is the first time that a landslide detection model has been deployed to multiple application vectors for the rapid detection of landslides in the Tibetan Plateau region. Those are important for avoiding the expansion of disaster chains on the Tibetan plateau, protecting life and property in downstream areas, and promoting the translation of landslide research results into applications. 2025-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4309121 Prepositioning and Multiperiod Distribution of Relief Supplies in Humanitarian Logistics: A Case Study on Earthquakes in Ya’an, China Yusheng Wang; Xiangqi Shan; Qingze Zhang Large-scale natural disasters result in significant human casualties and economic losses. Effective disaster management necessitates the strategic prepositioning of relief supplies before disasters and their swift distribution afterward. Different from prior studies that focused on single-period distribution with the objective of minimizing total costs, this paper presents a novel biobjective stochastic programming model that integrates prepositioning and multiperiod distribution of relief supplies in humanitarian logistics. The model concurrently aims to minimize overall costs and maximize transportation efficiency. We evaluate the model through a case study based on the Ya’an, China, earthquakes, demonstrating its effectiveness in optimizing relief supply strategies. Sensitivity analyses explore the impact of varying objective function weights, road capacity, and supply urgency, providing valuable managerial insights into humanitarian logistics decision-making. The insights gained from this study highlight the importance of proactive planning and strategic investment in infrastructure and logistics. By employing our biobjective stochastic programming model, decision-makers can develop robust strategies that balance cost and efficiency, ensuring a more effective and responsive disaster relief operation. 2025-01-01T00:00:00Z