Acquisition, storage, retrieval and dissemination of disaster related data

Ahammed, S; Badam, Karthik; Bhaskar, Guddanti V.; Chhuttani, Sumit; Gangwar, Anuj; Garg, Vaibhav; Gupta, Lokesh; Jain, Anubhav; Jindal, Rohit; Kumar, Neeraj; Manku, Kiran K.; Midde, Vijay K.; Nagarjuna, Malempati; Patil, Rajkumar V.; Singh, Saurabh Kumar; Sneha, N; Palli, Srinivas; Tanwani, Mudit; Wasnik, Sameer; Guravannavar, Ravindra; Kalyanasundaram, Subrahmanyam; Kataoka, Kotaro; Rao, M. S. Narasinga; Sivadasan, Naveen

doi:10.1109/r10-htc.2014.7026329

Cited by 4 publications

(1 citation statement)

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“…The acquisition of disaster data to be the primary task that can reasonably evaluate the catastrophe situation and residual functions of an infrastructure (Ahammed et al 2014). Different types of infrastructure have slightly different requirements for disaster data; they mainly focus on displacement, deformation, modal characteristics, point clouds, images, videos, and catastrophe data.…”

Section: Acquisition and Fusion Of Multi-source Datamentioning

confidence: 99%

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Dong

2022

Nat Hazards

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Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

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Section: Acquisition and Fusion Of Multi-source Datamentioning

confidence: 99%