Earthquake Damage Assessment Based on Fuzzy Logic and Neural Networks

2001 ◽  
Vol 17 (1) ◽  
pp. 89-112 ◽  
Author(s):  
Mauricio Sánchez-Silva ◽  
Libardo García
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Damage Assessment ◽  
Structural Damage ◽  
Management Strategies ◽  
Computer Software ◽  
Good Representation ◽  
Feed Forward Neural Network ◽  
Risk Management Strategies ◽  
Potential Damage

Potential damage assessment is fundamental for defining mitigation procedures and risk management strategies. Damage assessment involves the difficulties of defining, assessing, and modeling the variables involved, as well as handling uncertainty. Seismic damage estimation of structures does not only depend on the behavior of the structural system, but it involves other factors, which differ in nature. The paper presents a methodology for damage assessment of structures that combines systems theory, fuzzy logic, and neural networks. A feed-forward neural network supported on the systemic organization of information is used to assess the expected structural damage for a given earthquake. The methodology provides a very useful environment to consider the context of the building structure. The network has been trained using the damage observed in the recent earthquake that occurred in central Colombia. Several sets of structures were evaluated and the results compared to the damage observed. The model showed to be highly reliable and a good representation of experts' opinions. Computer software ERS-99 was developed and is currently being used for teaching and consulting purposes.

scholarly journals The Importance of Scientific Research: the Case of Insurance for Climate Change

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Donatella Porrini
Keyword(s):  
Climate Change ◽  
Management Strategies ◽  
Scientific Research ◽  
Extreme Weather Events ◽  
Potential Contribution ◽  
Insurance Sector ◽  
Natural Catastrophes ◽  
Weather Events ◽  
Risk Management Strategies ◽  
Potential Damage

<p>Climate change is likely to cause extreme weather events in the world with the consequence of an increased number of natural catastrophes. The expected damages pose serious challenges to governments in terms of policy choice and a crucial point is to define the role can be played by insurance sector, particularly as a tool to reduce potential damage, as well as to stimulate mitigation. Scientific research and good knowledge of risk are necessary in guiding policy decisions to manage the risks deriving from climate change. In this direction, the author analyses the fact that risks connected with climate change and the potential contribution of the insurance sector need to be analysed by scientific research in order to plan the correct risk management strategies in the future.</p>

scholarly journals The Importance of Scientific Research: the Case of Insurance for Climate Change

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Donatella Porrini
Keyword(s):  
Climate Change ◽  
Management Strategies ◽  
Scientific Research ◽  
Extreme Weather Events ◽  
Potential Contribution ◽  
Insurance Sector ◽  
Natural Catastrophes ◽  
Weather Events ◽  
Risk Management Strategies ◽  
Potential Damage

<p>Climate change is likely to cause extreme weather events in the world with the consequence of an increased number of natural catastrophes. The expected damages pose serious challenges to governments in terms of policy choice and a crucial point is to define the role can be played by insurance sector, particularly as a tool to reduce potential damage, as well as to stimulate mitigation. Scientific research and good knowledge of risk are necessary in guiding policy decisions to manage the risks deriving from climate change. In this direction, the author analyses the fact that risks connected with climate change and the potential contribution of the insurance sector need to be analysed by scientific research in order to plan the correct risk management strategies in the future.</p>

scholarly journals Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 24 ◽  
Author(s):  
Yijun Liao ◽  
Mohammad Ebrahim Mohammadi ◽  
Richard L. Wood
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Damage Assessment ◽  
Structural Damage ◽  
Point Clouds ◽  
Depth Information ◽  
3D Point Clouds ◽  
Aerial Imaging ◽  
Damage States

Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures.

scholarly journals Real-Time Structural Damage Assessment Using Artificial Neural Networks and Antiresonant Frequencies

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
V. Meruane ◽  
J. Mahu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Damage Assessment ◽  
Structural Damage ◽  
Model Updating ◽  
Assessment Process ◽  
Artificial Neural ◽  
Assessment Problems

The main problem in damage assessment is the determination of how to ascertain the presence, location, and severity of structural damage given the structure's dynamic characteristics. The most successful applications of vibration-based damage assessment are model updating methods based on global optimization algorithms. However, these algorithms run quite slowly, and the damage assessment process is achieved via a costly and time-consuming inverse process, which presents an obstacle for real-time health monitoring applications. Artificial neural networks (ANN) have recently been introduced as an alternative to model updating methods. Once a neural network has been properly trained, it can potentially detect, locate, and quantify structural damage in a short period of time and can therefore be applied for real-time damage assessment. The primary contribution of this research is the development of a real-time damage assessment algorithm using ANN and antiresonant frequencies. Antiresonant frequencies can be identified more easily and more accurately than mode shapes, and they provide the same information. This research addresses the setup of the neural network parameters and provides guidelines for the selection of these parameters in similar damage assessment problems. Two experimental cases validate this approach: an 8-DOF mass-spring system and a beam with multiple damage scenarios.

scholarly journals PROBLEM PREDICTION DURING TRIP IN AND TRIP OUT PROCEDURES WITH ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 7 (3) ◽  
pp. 71-77
Author(s):  
Ádám Pásztor ◽  
Richárd Ürmös
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Analytical Methods ◽  
Petroleum Industry ◽  
Feed Forward Neural Network ◽  
Drilling Operation ◽  
Artificial Neural

In recent times, the adaptation of artificial intelligence (AI) technologies has been spread in the petroleum industry. Such methods as Artificial Neural Networks (ANN), Fuzzy Logic, or Evolutionary Computing have the potential to improve the currently applied methods in every sector of the industry. They provide an advanced encroachment of the complex physics of downhole parameters, which directly add to their modeling ability compared to the traditional empirical and analytical methods. In this study, the development of a feed-forward neural network is presented. The purpose of the development is to predict the possible problems in case of a drilling operation, during running in and pulling out of the hole (RIH & POOH), based on the data acquired during the drilling of the hole.

The management of risk in a dryland environment

2011 ◽  
pp. 13-21
Author(s):  
D.I. Gray ◽  
J.I. Reid ◽  
D.J. Horne
Keyword(s):  
Risk Management ◽  
Service Providers ◽  
Management Strategies ◽  
Subterranean Clover ◽  
Farming Systems ◽  
Market Risk ◽  
Farm Size ◽  
Forage Crops ◽  
Risk Management Strategies ◽  
Upside Risk

A group of 24 Hawke's Bay hill country farmers are working with service providers to improve the resilience of their farming systems. An important step in the process was to undertake an inventory of their risk management strategies. Farmers were interviewed about their farming systems and risk management strategies and the data was analysed using descriptive statistics. There was considerable variation in the strategies adopted by the farmers to cope with a dryland environment. Importantly, these strategies had to cope with three types of drought and also upside risk (better than expected conditions), and so flexibility was critical. Infra-structure was important in managing a dryland environment. Farmers chose between increased scale (increasing farm size) and geographic dispersion (owning a second property in another location) through to intensification (investing in subdivision, drainage, capital fertiliser, new pasture species). The study identified that there may be scope for further investment in infra-structural elements such as drainage, deeper rooting alternative pasture species and water harvesting, along with improved management of subterranean clover to improve flexibility. Many of the farmers used forage crops and idling capacity (reduced stocking rate) to improve flexibility; others argued that maintaining pasture quality and managing upside risk was a better strategy in a dryland environment. Supplementary feed was an important strategy for some farmers, but its use was limited by contour and machinery constraints. A surprisingly large proportion of farmers run breeding cows, a policy that is much less flexible than trading stock. However, several farmers had improved their flexibility by running a high proportion of trading cattle and buffer mobs of ewe hoggets and trade lambs. To manage market risk, the majority of farmers are selling a large proportion of their lambs prime. Similarly, cattle are either sold prime or store onto the grass market when prices are at a premium. However, market risk associated with the purchase of supplements and grazing was poorly managed.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

Withdrawn: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC)

2019 ◽  
Vol 13 ◽  
Author(s):  
Abeer A. Amer ◽  
Soha M. Ismail
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Recent Advances ◽  
Vascular Center ◽  
Copyright Permission ◽  
Legal Right

The following article has been withdrawn on the request of the author of the journal Recent Advances in Computer Science and Communications (Recent Patents on Computer Science): Title: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC) Authors: Abeer A. Amer and Soha M. Ismail* Bentham Science apologizes to the readers of the journal for any inconvenience this may cause BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

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