The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Sublime, Jérémie

doi:10.3390/geosciences11030133

Cited by 5 publications

(3 citation statements)

References 78 publications

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“…We can mention the offshore Nuclear Power Plant (NPP) in RUSSIAN far east regions and floating Liquefied Natural Gas (LNG) storage tanks to give a few important applications. The design of such mega-structures has to take into account all possible risks including the risk of tsunamis, as the TOHOKU 2011 event notoriously demonstrated to us [39,47]. The result of a tsunami wave/body interaction may be catastrophic for the environment when the body is a floating NPP.…”

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confidence: 99%

Long wave interaction with a partially immersed body. Part II: Numerical results

Khakimzyanov¹,

Dutykh²,

Gusev³

2022

Preprint

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In this manuscript we perform an extensive numerical study of the long wave interaction problem with a fixed partially immersed body into a fluid layer. The incident wave is assumed to be an isolated solitary wave. The body in this study is assumed to be fixed with a rectangular section which is not touching the bottom of the channel. The mathematical modelling of this problem is based on Part I [22] of this series and considered models include the Nonlinear Shallow Water Equations (NSWE), fully nonlinear weakly dispersive Serre-Green-Naghdi Equations (SGN equations) (completed with appropriate compatibility conditions on solid/fluid boundaries) and the free surface irrotational full Euler equations (FEE). We study the influence of the floating body elongation, immersion depth and incident wave amplitude on the wave field before and after the obstacle. The comparison of all three models predictions and the data of small-scale laboratory experiments is performed. Moreover, in the framework of the FEE model we investigate the anomalous wave run-up behind the floating body in the close presence of a vertical wall. We demonstrate the cases where the vertical wall creates extreme wave amplitudes behind the body, but also we show the cases where the wall attenuates wave amplitudes comparing to the wave field without a wall.

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confidence: 99%

Long wave interaction with a partially immersed body. Part II: Numerical results

Khakimzyanov¹,

Dutykh²,

Gusev³

2022

Preprint

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“…A similar strategy is also very often adopted to assess the risk of buildings in seismic areas [24] or areas subject to the risk of floods [25], tsunamis [26] or tornadoes [27].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the assessment of this phenomenon, the problem addressed in this paper shows several analogies to the impact of an exceptional natural environment on buildings. A similar strategy is also very often adopted to assess the risk of buildings in seismic areas [24] or areas subject to the risk of floods [25], tsunamis [26] or tornadoes [27].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

2022

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This paper presents the results of comparative studies on the implementation of machine learning methods in the damage intensity assessment of masonry buildings. The research was performed on existing residential buildings, subjected to negative impacts of the industrial environment induced by coal mining plants during their whole technical life cycle. The research was justified on the grounds of safety of use, as well as potential energy losses and CO2 emissions generated by the inefficient management of building materials resources resulting from poor planning of retrofitting. In this field, the research is in line with the global trends of large-scale retrofitting of existing buildings in European countries due to their thermal insulation parameters and seismic hazard. By combining this with the effects of material degradation throughout the technical lifecycle of buildings, the proposed methods allow for a more efficient approach to maintaining quality management of large groups of buildings, which is part of the sustainable development framework. Due to the multidimensionality of the undertaken problem and the necessity of mathematical representation of uncertainty, it was decided to implement a machine learning approach. The effectiveness of the following methods was analysed: probabilistic neural network, support vector machine, naive Bayes classification and Bayesian belief networks. The complexity of individual methods dictated the order of the adopted research horizon. Within such a research plan, both model parameters were learned, and model structure was extracted from the data, which was applied only to the approach based on Bayesian networks. The results of the conducted analyses were verified by assuming classification accuracy measures. Thus, a method was extracted that allows for the best realisation of the set research objective, which was to create a classification system to assess the intensity of damage to masonry buildings. The paper also presents in detail the characteristics of the described buildings, which were used as input variables, and assesses the effectiveness of the obtained results in terms of utilisation in practice.

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Automatic post-tsunami loss modeling using deep learning CNN case study: Miyagi and Fukushima Japan tsunami

Ahmed

Güneyli

2023

Nat Hazards

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The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Cited by 5 publications

References 78 publications

Long wave interaction with a partially immersed body. Part II: Numerical results

Long wave interaction with a partially immersed body. Part II: Numerical results

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

Automatic post-tsunami loss modeling using deep learning CNN case study: Miyagi and Fukushima Japan tsunami

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