Flood-routing modeling with neural network optimized by social-based algorithm

Nikoo, Mehdi; Ramezani, Fatemeh; Hadzima-Nyarko, Marijana; Nyarko, Emmanuel Karlo; Nikoo, Mohammad Reza

doi:10.1007/s11069-016-2176-5

Cited by 55 publications

(30 citation statements)

References 27 publications

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“…to see the differences in the earthquake-resistance characteristics of structures or to distinguish: earthquake resistant structures variants of earthquake-resistant structures conditions of structures prior to earthquake reconstruction conditions of structures after earthquake reconstruction ductile structures non-ductile structures. (2). to recognize the characteristics that define a particular class of structures (by persons who test the same structural system in the field or use field data) in order to classify a structure in a valid class (3).…”

Section: Criteria For Building Taxonomiesmentioning

confidence: 99%

“…More than half of the world's population lives in high-risk areas exposed to at least one type of natural hazard: floods, cyclones, droughts, or earthquakes. Earthquakes are one of the most devastating and terrifying natural disasters which a human being can experience, and they can cause almost two-thirds of total annual world economic losses [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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Development of Seismic Vulnerability and Exposure Models—A Case Study of Croatia

et al. 2020

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Assessing earthquake risk and building vulnerability requires an exposure model. These exposure models quantify the building stock in terms of structural characteristics, spatial location, and occupancy. The most significant exposure parameters are the structural characteristics of buildings, which must be uniformly covered by structural typologies. Structural typologies that take into account the regional specificities of design and construction provide more accurate and reliable exposure models. Despite the long history of earthquake engineering in the Republic of Croatia, the assessment of exposure and vulnerability of buildings is a rather new concept, hindered by the fact that no city in the Republic of Croatia has a database on the number, types, and characteristics of existing buildings. The article presents the creation of a building exposure model for the city of Osijek, points out the problems and concerns that the realization process brings, and details the practical solutions and strategies that have been used to achieve the set goals.

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Section: Criteria For Building Taxonomiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Seismic Vulnerability and Exposure Models—A Case Study of Croatia

et al. 2020

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“…The development and training of the ANNs occurs with a number of hidden layers ranging from 1 to 2 and with a number of neurons ranging from 1 to 30 for each hidden layer. Each one of the ANNs is developed and trained for a number of different activation functions, such as the Log-sigmoid transfer function (logsig), the Linear transfer function (purelin) and the Hyperbolic tangent sigmoid transfer function (tansig) [85][86][87][88][89][90][91].…”

Section: Bpnn Model Developmentmentioning

confidence: 99%

Masonry Compressive Strength Prediction Using Artificial Neural Networks

Argyropoulos

Cavaleri²,

Rodrigues

et al. 2019

Communications in Computer and Information Science

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The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.

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“…Every year, the number of disasters around the world increases, causing more damage and deaths. Earthquakes, floods, landslides, and other natural disasters cause irreparable damage, often endangering the lives of people, cultures, material resources, and the environment [1,2]. Recently, the number of natural disasters has been on the rise, with earthquakes, in particular.…”

Section: Introductionmentioning

confidence: 99%

“…According to the National Protection and Rescue Directorate of Croatia (2013), 56.22% of Croatian territory, inhabited by more than 30% (1,633,529) of the total current population, is characterized as a zone with a high risk of earthquakes occurrence.…”

Section: Introductionmentioning

confidence: 99%

A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek

2020

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Due to increases in the number of inhabitants and their concentrations in densely populated areas, there is a growing need in modern society to be cautious towards the impact of catastrophic natural events. An earthquake is a particularly major example of this. Knowledge of the seismic vulnerability of buildings in Europe and around the world has deepened and expanded over the last 20 years, as a result of the many devastating earthquakes. In this study, a review of seismic risk assessment methods in Croatia was presented with respect to the hazard, exposure, and vulnerability of buildings in the fourth largest city (Osijek) in Croatia. The proposed algorithm for a detailed risk assessment was applied to a database and is currently in its initial stage.Sustainability 2020, 12, 1796 2 of 24 to estimate the losses. These losses can be assessed in a material form, through the damage of the building stock or non-structural elements of buildings, or in the form of casualties or injuries during the earthquake [10,11]. The last step is to analyze uncertainties, costs, decision-making criteria, etc. The ultimate goal of studying earthquakes and their impact on people and buildings is to create a safer environment, in case an earthquake occurs.The basic elements of the earthquake risk assessment process are shown in Figure 1.Sustainability 2020, 12, 1796 2 of 25 probable intensity in a given geographical area). Then, based on the available data from the exposure model, evaluate the damage using one of the existing vulnerability assessment methods in order to estimate the losses. These losses can be assessed in a material form, through the damage of the building stock or non-structural elements of buildings, or in the form of casualties or injuries during the earthquake [10,11]. The last step is to analyze uncertainties, costs, decision-making criteria, etc. The ultimate goal of studying earthquakes and their impact on people and buildings is to create a safer environment, in case an earthquake occurs. The basic elements of the earthquake risk assessment process are shown in Figure 1.

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Flood-routing modeling with neural network optimized by social-based algorithm

Cited by 55 publications

References 27 publications

Development of Seismic Vulnerability and Exposure Models—A Case Study of Croatia

Development of Seismic Vulnerability and Exposure Models—A Case Study of Croatia

Masonry Compressive Strength Prediction Using Artificial Neural Networks

A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek

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