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Hurricanes are among the most devastating natural disasters in the United States, causing billions of dollars of property damage and insured losses. During extreme wind events, unsecured objects in jobsites can easily become airborne debris, which results in substantial loss to construction projects and neighboring communities. Towards a systematic disaster preparedness in construction jobsites, this paper presents a novel vision-based digital twinning and threat assessment framework. We encode the context of disaster risk into deep-learning architectures to identify and analyze the characteristics and impacts of potential wind-borne debris in construction site digital twin models. Case studies on nine piles of construction materials are presented to demonstrate and discuss the fidelity of the proposed computational modules. The proposed methods are expected to help provide heads up for practitioners to quickly recognize, localize, and assess potential wind-borne derbies in construction jobsites, and thereby implementing hurricane preparedness in an effective and timely manner. • A new AI-based framework for construction site disaster preparedness. • Digital twinning of potential wind-borne debris and 3D point cloud segmentation. • Case studies were conducted to evaluate the performance of the proposed method. • Potential for effective risk-informed decision making for disaster preparedness. [ABSTRACT FROM AUTHOR]

Wildfires have caused increasingly negative impacts with increasing occurrences close to densely populated regions. Evacuations are among the most critical measures in the immediate wildfire relief measures. While social media have been used in natural disasters, there has been limited understanding of the efficacy of using social media to aid evacuations. This paper presents a data-driven study of social media-aided evacuations for the 2020 wildfires in the western United States, based on 53,990 relevant tweets. First, we analyzed the aggregated social media data and validated its reliability against information from official channels. Both the temporal and spatial investigations show good agreements with official information. Further, we classified the tweets into pre- and on-evacuation based on extracted word patterns. The classifications align well with evacuation levels from official channels. Next, we demystified the information dissemination patterns via network analysis. We have found that government channels, news agencies, and public figures prevail among top users. The top users for on-evacuations tend to be more local-focused than pre-evacuations. This study demonstrates the efficacy of using social media to aid evacuations. In addition, it provides guidelines for future studies on extracting high-priority information from social media for disaster relief. • Efficacy of using social media to aid evacuations during wildfires is investigated. • Temporal and spatial analysis shows social media matches official information. • Social network analysis reveals information dissemination patterns. [ABSTRACT FROM AUTHOR]