Advanced Simulation Tool for Improved Damage Assessment 2) Water-Mist Suppression of Large Scale Compartment Fires

2000 ◽  
Author(s):  
Kuldeep Prasad ◽  
Gopal Patnaik ◽  
K. Kailasanath
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Water Mist ◽  
Simulation Tool ◽  
Compartment Fires

A numerical study of water-mist suppression of large scale compartment fires

2002 ◽  
Vol 37 (6) ◽  
pp. 569-589 ◽  
Author(s):  
Kuldeep Prasad ◽  
Gopal Patnaik ◽  
K. Kailasanath
Keyword(s):  
Large Scale ◽  
Numerical Study ◽  
Water Mist ◽  
Compartment Fires

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

Seismic damage assessment of a virtual large-scale city model

2018 ◽  
pp. 1936-1942
Author(s):  
M. Domaneschi ◽  
S. Marasco ◽  
G. Scutiero ◽  
A. Zamani Noori ◽  
O. Kammouh ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Seismic Damage ◽  
City Model ◽  
Seismic Damage Assessment

Numerical Computation of Mitigation and Extinction of Large Scale Fires by Water Mist Systems

2007 ◽  
Author(s):  
Bjorn Fossmo Magnussen ◽  
Trond Evanger ◽  
B. Grimsmo ◽  
Nils Inge Lilleheie
Keyword(s):  
Numerical Computation ◽  
Large Scale ◽  
Water Mist

Simulation of Power Line Communication Using OPNET for Vertical Fish Farm

2018 ◽  
pp. 139-164
Keyword(s):  
Efficient Algorithm ◽  
Large Scale ◽  
Network Simulation ◽  
Power Line ◽  
Fish Farm ◽  
Simulation Tool ◽  
Test Bed ◽  
Simulation Test ◽  
Large Scale System ◽  
Large Scale Simulations

OPNET is a network simulation tool which can simulate various elements in a network. It is able to analyze traffics and even can simulate security events recently. System designers can identify vulnerabilities or find an efficient algorithm/protocol that could reduce costs if simulations are performed prior to actual implementation/construction of a large-scale system. Thus, this chapter includes the method of simulating a PLC-system, designing a PLC-based vertical fish farm and an expandable simulation test bed with which students or researchers will be able simulate their implementation methods. With these methods, it will be possible to perform large-scale simulations.

Verification and validation of a CFD model for simulations of large-scale compartment fires

2011 ◽  
Vol 241 (9) ◽  
pp. 3645-3657 ◽  
Author(s):  
S. Suard ◽  
C. Lapuerta ◽  
F. Babik ◽  
L. Rigollet
Keyword(s):  
Large Scale ◽  
Verification And Validation ◽  
Cfd Model ◽  
Compartment Fires
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