Landslide Risk: Economic Valuation in the North-Eastern Zone of Medellin City

Natural disasters of a geodynamic nature can cause enormous economic and human losses. The economic costs of a landslide disaster include relocation of communities and physical repair of urban infrastructure. However, when performing a quantitative risk analysis, generally, the indirect economic consequences of such an event are not taken into account. A probabilistic approach methodology that considers several scenarios of hazard and vulnerability to measure the magnitude of the landslide and to quantify the economic costs is proposed. With this approach, it is possible to carry out a quantitative evaluation of the risk by landslides, allowing the calculation of the economic losses before a potential disaster in an objective, standardized and reproducible way, taking into account the uncertainty of the building costs in the study zone. The possibility of comparing different scenarios facilitates the urban planning process, the optimization of interventions to reduce risk to acceptable levels and an assessment of economic losses according to the magnitude of the damage. For the development and explanation of the proposed methodology, a simple case study is presented, located in north-eastern zone of the city of Medellín. This area has particular geomorphological characteristics, and it is also characterized by the presence of several buildings in bad structural conditions. The proposed methodology permits to obtain an estimative of the probable economic losses by earthquake-induced landslides, taking into account the uncertainty of the building costs in the study zone. The obtained estimative shows that the structural intervention of the buildings produces a reduction the order of 21 % in the total landslide risk. © Published under licence by IOP Publishing Ltd

Prediction of seismic-induced structural damage using artificial neural networks

Peer reviewedPostprin

Application of association rules to determine building typological classes for seismic damage predictions at regional scale. The case study of Basel

Assessing seismic vulnerability at large scales requires accurate attribution of individual buildings to more general typological classes that are representative of the seismic behavior of the buildings sharing same attributes. One-by-one evaluation of all buildings is a time-and-money demanding process. Detailed individual evaluations are only suitable for strategic buildings, such as hospitals and other buildings with a central role in the emergency post-earthquake phase. For other buildings simplified approaches are needed. The definition of a taxonomy that contains the most widespread typological classes as well as performing the attribution of the appropriate class to each building are central issues for reliable seismic assessment at large scales. A fast, yet accurate, survey process is needed to attribute a correct class to each building composing the urban system. Even surveying buildings with the goal to determine classes is not as time demanding as detailed evaluations of each building, this process still requires large amounts of time and qualified personnel. However, nowadays several databases are available and provide useful information. In this paper, attributes that are available in such public databases are used to perform class attribution at large scales based on previous data-mining on a small subset of an entire city. The association-rule learning (ARL) is used to find links between building attributes and typological classes. Accuracy of wide spreading these links learned on <250 buildings of a specific district is evaluated in terms of class attribution and seismic vulnerability prediction. By considering only three attributes available on public databases (i.e., period of construction, number of floors, and shape of the roof) the time needed to provide seismic vulnerability scenarios at city scale is significantly reduced, while accuracy is reduced by <5%

Selection of ground motion prediction equations for probabilistic seismic hazard analysis based on an improved fuzzy logic

The fuzzy logic method has been used widely in civil and earthquake engineering, but there is no comprehensive point of view for utilizing fuzzy approach in order to obtain ground motion prediction equations (GMPEs) for probabilistic seismic hazard analysis (PSHA). Hence, fuzzy magnitude–distance method as a new approach for choosing GMPEs in the process of PSHA, is developed in this research through the selection of the ruling peak ground acceleration (PGA) of each common cell (the combined cell of earthquake intensity and site to source distance). The presented method reduces the need for engineering judgments in seismic analysis based on a newly developed benchmark. It enables designers to not only determine the range of acceptable fuzzy results but also introduces a concept which ensures the selection of initial well-suited GMPEs for the analysis

Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs

Aplicações da inteligência artificial na engenharia sísmica

Alguns métodos do domínio da inteligência artificial têm vindo a ser utilizados na engenharia civil, nomeadamente na engenharia sísmica, pelo que se faz, neste artigo, um resumo das diversas aplicações sugeridas por um elevado número de investigadores. Os estudos realizados no âmbito da engenharia sísmica, apresentam grande complexidade, face à elevada incerteza que os caracterizam e em virtude de serem não lineares. Para tentar solucionar alguns desses problemas, vários investigadores têm proposto, nas duas últimas décadas, que se recorra ao desenvolvimento de sistemas periciais, sistemas fuzzy, redes neuronais e redes fuzzy neuronais, designadamente nas avaliações do risco sísmico (análise da casualidade e da vulnerabilidade sísmica), para modelar o comportamento não linear

Measuring and improving community resilience: a Fuzzy Logic approach

Due to the increasing frequency of natural and man-made disasters worldwide, the scientific community has paid considerable attention to the concept of resilience engineering in recent years. Authorities and decision-makers, on the other hand, have been focusing their efforts to develop strategies that can help increase community resilience to different types of extreme events. Since it is often impossible to prevent every risk, the focus is on adapting and managing risks in ways that minimize impacts to communities (e.g., humans and other systems). Several resilience strategies have been proposed in the literature to reduce disaster risk and improve community resilience. Generally, resilience assessment is challenging due to uncertainty and unavailability of data necessary for the estimation process. This paper proposes a Fuzzy Logic method for quantifying community resilience. The methodology is based on the PEOPLES framework, an indicator-based hierarchical framework that defines all aspects of the community. A fuzzy-based approach is implemented to quantify the PEOPLES indicators using descriptive knowledge instead of hard data, accounting also for the uncertainties involved in the analysis. To demonstrate the applicability of the methodology, data regarding the functionality of the city San Francisco before and after the Loma Prieta earthquake are used to obtain a resilience index of the Physical Infrastructure dimension of the PEOPLES framework. The results show that the methodology can provide good estimates of community resilience despite the uncertainty of the indicators. Hence, it serves as a decision-support tool to help decision-makers and stakeholders assess and improve the resilience of their communities

Rapid Natech Risk Assessment and Mapping Tool for Earthquakes: RAPID-N

Natural-hazard triggered accidents at industrial facilities (natechs) are recognized as an emerging risk with possibly serious consequences. Risk maps are helpful to identify natech hot spots. However, recent surveys showed that hardly any natech risk maps exist in the OECD and EU. A probabilistic natech risk mapping methodology for earthquakes was developed to fill this gap. It was implemented as a web-based software tool called RAPID-N. This tool allows rapid natech risk assessment and mapping by using fragility curves for damage estimation and simple models for consequence assessment with minimum data input. The tool includes a property estimation framework that can be used to calculate hazard parameters and site, process equipment, and substance properties. RAPID-N comes with a basic set of fragility curves for the damage assessment. If needed, custom damage states and fragility curves can also be defined for different process equipment types. Conditional and probabilistic relationships can be specified between damage states and probable natech event scenarios. The consequences of the natech events are assessed using the Risk Management Program (RMP) methodology of the U.S. EPA and the results are presented as summary reports and interactive risk maps. The tool can be used for land-use and emergency planning.JRC.G.6-Security technology assessmen