Damage Estimation of Explosions in Urban Environments By Simulation

Abstract

<p><strong>Abstract.</strong> Precise models of the impact of explosions in urban environments provide novel and valuable information in disaster management for developing precautionary, preventive and mitigating measures. Yet to date, no methods enabling accurate predictions of the process and effect of detonations at particular locations exist. We propose a novel approach mitigating this gap by combining state-of-the-art methods from photogrammetric 3D reconstruction, semantic segmentation and computational based numerical simulations. In a first step, we create an accurate urban 3D reconstruction from georeferenced aerial images. The resulting city model is then enriched with semantic information obtained from the original source images as well as from registered terrestrial images using deep neural networks. This allows for an efficient automatic preparation of a 3D model suitable for the use as a geometry for the numerical investigations. Using this approach, we are able to provide recent and precise models of an area of interest in an automated fashion. Within the model, we are now able to define the explosive charge size and location and simulate the resulting blast wave propagation using CFD simulation. This provides a full estimation for the expected pressure propagation of a defined charge size. From these results, arising damages and their extent, as well as possible access routes or countermeasures, can be estimated. Using georeferenced sources allows for the integration and utilization of simulation results into existing geoinformation systems of disaster management units, providing novel inputs for training, preparation and prevention. We demonstrate our proposed approach by evaluating expected glass breakage and expected damages impairing the structural integrity of buildings depending on the charge size using a 3D reconstruction from aerial images of an area in the inner city of Graz, Austria.</p>

Accidental Explosions, Beirut ANFO Blast, Ghana Truck Explosion, Global Terrorism Index 2021-22, and Blast Mitigation: A Review

“…Utilizing numerical technologies connected to the Beirut blast would enable the collection of far more data. Using georeferenced aerial pictures and semantic information, [46] reconstructed a realistic urban 3D scene of the Beirut blast. The geometry that is generated is then used to perform a numerical assessment of the structural strength of buildings in scenarios involving explosions [46].…”

“…Using georeferenced aerial pictures and semantic information, [46] reconstructed a realistic urban 3D scene of the Beirut blast. The geometry that is generated is then used to perform a numerical assessment of the structural strength of buildings in scenarios involving explosions [46]. Such publications greatly benefit disaster intervention teams by either facilitating the implementation of emergency response plans or giving unique insights for disaster preparedness, response, and prevention.…”

A Review of Blast Loading in the Urban Environment

“…Numerical simulations have previously been used to study blast propagation in simple urban areas with some success [92,93]. Luccioni et al [94] and Ambrosini et al [95] determined the equivalent mass and location of the explosive device used in the 1994 Buenos Aires terror attack by correlating the observed damage with the loading distributions from a range of numerical analyses in a congested urban environment.…”

Review on quick safety assessment of building structures in complex urban environment after extreme explosion events

“…Wang et al (2017) proposed an overset grid meshing strategy based on a component view, which allows for idealized meshes in an automated fashion. Mohr et al (2019) developed a 3D reconstruction method by combining geo-referenced aerial images and semantic information through deep neural networks. An advanced computational package specialized in rapid assessment and accurate prediction of the blast waves in complex scenarios was also reported (Cullis et al, 2016).…”