Cities are becoming the preferred choice of populations to reside in due to the opportunities they offer. While the concentration of populations is increasing in the cities, there is an immediate need to equip the cities for efficient functioning and providing safety and security. The unplanned urbanization of cities is adding vulnerability, especially to the spatially relevant hazards such as earthquakes and floods. Initiatives such as the Smart City Mission support cities with investments to improve the quality of life for people and enhance the efficiency of the civic systems by integrating infrastructure and technology. However, the top down approach to decision making, especially in spatial planning, leaves out the perspective of citizens. This study, hence, attempts to gather the perception of citizens on smart city initiatives and disaster risk reduction (DRR) through a questionnaire survey in the smart city of Pune, India. The objective of this study is to understand how smart city initiatives influence the key spatial planning components for DRR. The study reveals smart city initiatives proposed for the city impacts each spatial planning component. Smart city initiatives may further stress these components, increasing the exposure to disaster risks. Therefore, there is a need for holistic integration in spatial planning for DRR. This study can help in modulating the smart city initiatives for enhancing the safety of the citizens.
由于城市提供的机会，城市正成为人们居住的首选。在城市人口日益集中的同时，迫切需要使城市具备有效运作和提供安全保障的能力。城市无计划的城市化增加了脆弱性，特别是对地震和洪水等空间相关灾害的脆弱性。智慧城市使命(Smart City Mission)等倡议通过投资支持城市，通过整合基础设施和技术，改善人们的生活质量，提高公民系统的效率。然而，自上而下的决策方法，特别是在空间规划中，忽略了公民的观点。因此，本研究试图通过在印度浦那的智慧城市进行问卷调查，收集市民对智慧城市倡议和减少灾害风险(DRR)的看法。本研究的目的是了解智慧城市倡议如何影响DRR的关键空间规划组成部分。该研究揭示了为城市提出的智慧城市倡议对每个空间规划组成部分的影响。智慧城市计划可能会进一步强调这些组成部分，增加灾害风险。因此，需要在减灾空间规划中进行整体整合。这项研究可以帮助调整智慧城市的举措，以提高市民的安全。
{"title":"Smart cities - spatial planning and disaster risk reduction of Pune city, India","authors":"Sujata Kodag, Abhishek Kodag","doi":"10.20517/dpr.2023.11","DOIUrl":"https://doi.org/10.20517/dpr.2023.11","url":null,"abstract":"Cities are becoming the preferred choice of populations to reside in due to the opportunities they offer. While the concentration of populations is increasing in the cities, there is an immediate need to equip the cities for efficient functioning and providing safety and security. The unplanned urbanization of cities is adding vulnerability, especially to the spatially relevant hazards such as earthquakes and floods. Initiatives such as the Smart City Mission support cities with investments to improve the quality of life for people and enhance the efficiency of the civic systems by integrating infrastructure and technology. However, the top down approach to decision making, especially in spatial planning, leaves out the perspective of citizens. This study, hence, attempts to gather the perception of citizens on smart city initiatives and disaster risk reduction (DRR) through a questionnaire survey in the smart city of Pune, India. The objective of this study is to understand how smart city initiatives influence the key spatial planning components for DRR. The study reveals smart city initiatives proposed for the city impacts each spatial planning component. Smart city initiatives may further stress these components, increasing the exposure to disaster risks. Therefore, there is a need for holistic integration in spatial planning for DRR. This study can help in modulating the smart city initiatives for enhancing the safety of the citizens.","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131571115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Earthquakes are among the most devastating natural disasters, posing a significant threat to human life and property. With the rapid pace of urbanization, urban risk against earthquakes has increased, making them an increasingly pressing concern for human society. Urban infrastructure systems (UISs), such as electric power, water supply, and gas systems, are essential to the smooth functioning of modern society but are highly vulnerable to ground shaking, resulting in service interruptions to customers and triggering negative impacts on society. This article focuses on the seismic retrofit problem, which intends to enhance the resilience of UISs against seismic hazards. First, a two-stage stochastic programming model is developed for the seismic retrofit problem, where the first stage seeks an optimal seismic retrofit strategy under a limited budget, and the second stage attempts to identify a repair sequence to maximize the system resilience under the given retrofit strategy. Then, this article introduces a heuristic algorithm based on the scenario reduction method and integer L-shaped method to solve the formulated model. Finally, numerical experiments on the Qujing power transmission system are conducted to validate the proposed algorithm. Results show that they can be applied to the resilience-based seismic retrofit problem of large-scale UISs.
{"title":"Resilience-based seismic retrofit of urban infrastructure systems","authors":"Chuang Liu, Min Xu, Shenglan Hu, M. Ouyang","doi":"10.20517/dpr.2023.07","DOIUrl":"https://doi.org/10.20517/dpr.2023.07","url":null,"abstract":"Earthquakes are among the most devastating natural disasters, posing a significant threat to human life and property. With the rapid pace of urbanization, urban risk against earthquakes has increased, making them an increasingly pressing concern for human society. Urban infrastructure systems (UISs), such as electric power, water supply, and gas systems, are essential to the smooth functioning of modern society but are highly vulnerable to ground shaking, resulting in service interruptions to customers and triggering negative impacts on society. This article focuses on the seismic retrofit problem, which intends to enhance the resilience of UISs against seismic hazards. First, a two-stage stochastic programming model is developed for the seismic retrofit problem, where the first stage seeks an optimal seismic retrofit strategy under a limited budget, and the second stage attempts to identify a repair sequence to maximize the system resilience under the given retrofit strategy. Then, this article introduces a heuristic algorithm based on the scenario reduction method and integer L-shaped method to solve the formulated model. Finally, numerical experiments on the Qujing power transmission system are conducted to validate the proposed algorithm. Results show that they can be applied to the resilience-based seismic retrofit problem of large-scale UISs.","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131095926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Welcome to the new journal of Disaster Prevention and Resilience","authors":"Jie Li","doi":"10.20517/dpr.2021.01","DOIUrl":"https://doi.org/10.20517/dpr.2021.01","url":null,"abstract":"","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132598539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
No country will escape the ravages of climate change, but some, like small island developing states (SIDS), will be less able to withstand them. Their fundamental characteristics in essential domains pose existential threats for them. The paper borrows from Lukka and Vinnari’s work on domain and method theories as a lens to conceptually explore the question, “Is it possible for SIDS to become disaster resilient?” It turns out that SIDS might be too small, too isolated, too economically and institutionally weak, and too exposed to become disaster resilient. Their developmental state, economic, institutional, and community attributes are causes of significant vulnerabilities and undermine disaster resilience efforts. The challenges from climate change alone highlight the herculean task ahead for these small and tiny developing islands without transformative actions. The advantage for SIDS is their solid social system. Their populations are resourceful, and they can pivot if they need to. However, the lingering question remains whether that will be enough to mitigate the weaknesses in other critical resilience domains?
{"title":"Compounding challenges for disaster resilience in small island developing states","authors":"Denise D. P. Thompson","doi":"10.20517/dpr.2021.04","DOIUrl":"https://doi.org/10.20517/dpr.2021.04","url":null,"abstract":"No country will escape the ravages of climate change, but some, like small island developing states (SIDS), will be less able to withstand them. Their fundamental characteristics in essential domains pose existential threats for them. The paper borrows from Lukka and Vinnari’s work on domain and method theories as a lens to conceptually explore the question, “Is it possible for SIDS to become disaster resilient?” It turns out that SIDS might be too small, too isolated, too economically and institutionally weak, and too exposed to become disaster resilient. Their developmental state, economic, institutional, and community attributes are causes of significant vulnerabilities and undermine disaster resilience efforts. The challenges from climate change alone highlight the herculean task ahead for these small and tiny developing islands without transformative actions. The advantage for SIDS is their solid social system. Their populations are resourceful, and they can pivot if they need to. However, the lingering question remains whether that will be enough to mitigate the weaknesses in other critical resilience domains?","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123948317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Randomness in earthquake ground motions is prevalent in real engineering practices. Therefore, it is of paramount significance to utilize an appropriate model to simulate random ground motions. In this paper, a physical random function model of ground motions, which considers the source-path-site mechanisms of earthquakes, is employed for the seismic analysis. The probability density evolution method is adopted to quantify the extreme value distribution of structural responses. Then, the sensitivity analysis of the extreme value distribution with respect to basic model parameters is conducted via a newly developed Fréchet-derivative-based approach. A 10-story reinforced concrete frame structure, with nominal deterministic structural parameters and subjected to random ground motions, is studied. The results indicate that when the structure is still in a linear or weakly nonlinear stage in the situation of frequent earthquakes, the model parameter called the equivalent predominate circular frequency is of the most significance, with an importance measure (IM) greater than 0.8. Nonetheless, if the structure exhibits strong nonlinearity, such as in the case of a rare earthquake, the equivalent predominate circular frequency remains highly influential, but the Brune source parameter, which describes the decay process of the fault rupture, becomes important as well, with an IM increased from around 0.2 to around 0.4. These findings indicate that the IMs of basic model parameters are closely related to the embedded physical mechanisms of the structure, and the change in the physical state of the structure may provoke the change of IMs of basic inputs. Furthermore, some other issues are also outlined.
{"title":"Fréchet-derivative-based global sensitivity analysis of the physical random function model of ground motions","authors":"Z. Wan, Wei-Feng Tao, Yanqiong Ding, Lifeng Xin","doi":"10.20517/dpr.2023.13","DOIUrl":"https://doi.org/10.20517/dpr.2023.13","url":null,"abstract":"Randomness in earthquake ground motions is prevalent in real engineering practices. Therefore, it is of paramount significance to utilize an appropriate model to simulate random ground motions. In this paper, a physical random function model of ground motions, which considers the source-path-site mechanisms of earthquakes, is employed for the seismic analysis. The probability density evolution method is adopted to quantify the extreme value distribution of structural responses. Then, the sensitivity analysis of the extreme value distribution with respect to basic model parameters is conducted via a newly developed Fréchet-derivative-based approach. A 10-story reinforced concrete frame structure, with nominal deterministic structural parameters and subjected to random ground motions, is studied. The results indicate that when the structure is still in a linear or weakly nonlinear stage in the situation of frequent earthquakes, the model parameter called the equivalent predominate circular frequency is of the most significance, with an importance measure (IM) greater than 0.8. Nonetheless, if the structure exhibits strong nonlinearity, such as in the case of a rare earthquake, the equivalent predominate circular frequency remains highly influential, but the Brune source parameter, which describes the decay process of the fault rupture, becomes important as well, with an IM increased from around 0.2 to around 0.4. These findings indicate that the IMs of basic model parameters are closely related to the embedded physical mechanisms of the structure, and the change in the physical state of the structure may provoke the change of IMs of basic inputs. Furthermore, some other issues are also outlined.","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125673754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Critical infrastructure such as the transportation, power generation, water supply, telecommunications, security and health services/systems, etc. are essential for providing a reliable flow of goods and services, crucial to the functioning of the economy and society. These infrastructures are closely linked and dependent on one another, and these interdependencies need to be modelled in order to analyse the disruptions and vulnerabilities of critical infrastructure networks as a whole. With increased, investment and complexity in the coupling of gas and electricity network, limitations and vulnerabilities of the coupled networks are becoming increasingly relevant to the operational planning of the critical infrastructures. Current modelling of a coupled gas and electricity network will be used in conjunction with nation input-output interdependency model to model physical critical infrastructures and critical infrastructure interdependencies, respectively. This research work will tackle two possible scenarios that might happen in the gas network while evaluating the cascading impact both in the physical model perspective and input-output interdependency model perspective. The results will provide insights on how disruption in the gas network affects the electricity grid and its corresponding economic impact on all economic sectors in a nation.
{"title":"Modelling of multi-sectoral critical infrastructure interdependencies for vulnerability analysis","authors":"Jiwei Lin, T. Pan","doi":"10.20517/dpr.2021.05","DOIUrl":"https://doi.org/10.20517/dpr.2021.05","url":null,"abstract":"Critical infrastructure such as the transportation, power generation, water supply, telecommunications, security and health services/systems, etc. are essential for providing a reliable flow of goods and services, crucial to the functioning of the economy and society. These infrastructures are closely linked and dependent on one another, and these interdependencies need to be modelled in order to analyse the disruptions and vulnerabilities of critical infrastructure networks as a whole. With increased, investment and complexity in the coupling of gas and electricity network, limitations and vulnerabilities of the coupled networks are becoming increasingly relevant to the operational planning of the critical infrastructures. Current modelling of a coupled gas and electricity network will be used in conjunction with nation input-output interdependency model to model physical critical infrastructures and critical infrastructure interdependencies, respectively. This research work will tackle two possible scenarios that might happen in the gas network while evaluating the cascading impact both in the physical model perspective and input-output interdependency model perspective. The results will provide insights on how disruption in the gas network affects the electricity grid and its corresponding economic impact on all economic sectors in a nation.","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130753145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper aims to propose run-out distance predictive models for clay slopes using the material point method (MPM), which can simulate the progressive failure process of slopes considering the strain softening effect of soils. A suite of 100 ground motions is selected from the NGA-West2 database and then scaled for conducting the dynamic analysis of slopes. The permanent slope displacements (D) can be classified into two categories, namely the “un-failure” category with D smaller than 0.4 m and the “failure” category with D in the range of 10 m to 15 m. It is found that peak ground velocity (PGV) exhibits the highest correlation with D for the “un-failure” category, whereas all ground-motion intensity measures (e.g., PGV, peak ground acceleration) are less correlated with D for the “failure” category. Therefore, the run-out distance of collapsed clay slopes is more related to the failure model rather than the triggering shaking intensities. Moreover, thousands of slope models with various slope angles, slope heights (H), soil densities, and peak and residual strength parameters are developed based on MPM. The run-out distances for the slopes being collapsed are then collected. Predictive models for different slope angles are proposed, which predict the run-out distance as a function of H, unit weight, residual cohesion, and residual friction angle. The proposed models are applicable for clay slopes with slope angles in the range of 30° to 45° and H in the range of 10 m to 30 m.
利用物质点法(substance point method, MPM)建立了黏土边坡的跳动距离预测模型，该模型可以模拟考虑土体应变软化效应的边坡渐进破坏过程。从NGA-West2数据库中选择了一组100个地面运动，然后进行缩放以进行斜坡的动力分析。边坡永久位移(D)可分为两类，即D小于0.4 m的“未破坏”类和D在10 ~ 15 m范围内的“破坏”类。研究发现，在“未失效”类别中，峰值地面速度(PGV)与D的相关性最高，而在“失效”类别中，所有地面运动强度测量(如PGV、峰值地面加速度)与D的相关性较低。因此，粘土边坡崩塌的跳动距离更多地与破坏模式有关，而不是与触发震动强度有关。在此基础上，建立了数千个具有不同坡角、坡高(H)、土密度、峰值和残余强度参数的边坡模型。然后收集斜坡坍塌的跳动距离。提出了不同坡角的预测模型，该模型预测了以H、单位重量、剩余黏聚力和剩余摩擦角为函数的跳动距离。所建模型适用于坡角为30°~ 45°、H为10 m ~ 30 m的粘土边坡。
{"title":"Predictive models for the run-out distance of clay slopes based on material point method","authors":"Yu-Han Zhao, Qiang Wu, W. Du","doi":"10.20517/dpr.2023.14","DOIUrl":"https://doi.org/10.20517/dpr.2023.14","url":null,"abstract":"This paper aims to propose run-out distance predictive models for clay slopes using the material point method (MPM), which can simulate the progressive failure process of slopes considering the strain softening effect of soils. A suite of 100 ground motions is selected from the NGA-West2 database and then scaled for conducting the dynamic analysis of slopes. The permanent slope displacements (D) can be classified into two categories, namely the “un-failure” category with D smaller than 0.4 m and the “failure” category with D in the range of 10 m to 15 m. It is found that peak ground velocity (PGV) exhibits the highest correlation with D for the “un-failure” category, whereas all ground-motion intensity measures (e.g., PGV, peak ground acceleration) are less correlated with D for the “failure” category. Therefore, the run-out distance of collapsed clay slopes is more related to the failure model rather than the triggering shaking intensities. Moreover, thousands of slope models with various slope angles, slope heights (H), soil densities, and peak and residual strength parameters are developed based on MPM. The run-out distances for the slopes being collapsed are then collected. Predictive models for different slope angles are proposed, which predict the run-out distance as a function of H, unit weight, residual cohesion, and residual friction angle. The proposed models are applicable for clay slopes with slope angles in the range of 30° to 45° and H in the range of 10 m to 30 m.","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127405730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Social institutions such as hospitals and schools are among the main pillars of community stability. A drop in the functionality of hospitals and schools is likely to have short-term and long-term effects on a community, including a reduction in medical interventions, an increase in unschooled children, and population outmigration in search of essential social services. However, comprehensive community resilience models that consider the role played by social institutions in community stability following natural disasters are scarce at the present time. This paper provides a literature review and critical appraisal of previous studies on the resilience of hospital and school systems and their impact on community well-being. The review encompasses existing resilience models for single hospitals and schools, their role when connected with other hospitals and schools in a network, their reliance on each other as interdependent systems, and their role in community resilience and stability. Different mitigation strategies and policies to enhance hospital and school systems’ resilience after extreme natural hazards are also summarized. The paper concludes with a series of recommendations to improve current models for social institutions, enhance the connection between existing hospital and school resilience models and community resilience frameworks, and develop social stability indices that policymakers can use in preparing and mitigating future extreme events.
{"title":"The role of social institutions in community resilience following extreme natural hazard events","authors":"E. Hassan, H. Mahmoud, B. Ellingwood","doi":"10.20517/dpr.2023.01","DOIUrl":"https://doi.org/10.20517/dpr.2023.01","url":null,"abstract":"Social institutions such as hospitals and schools are among the main pillars of community stability. A drop in the functionality of hospitals and schools is likely to have short-term and long-term effects on a community, including a reduction in medical interventions, an increase in unschooled children, and population outmigration in search of essential social services. However, comprehensive community resilience models that consider the role played by social institutions in community stability following natural disasters are scarce at the present time. This paper provides a literature review and critical appraisal of previous studies on the resilience of hospital and school systems and their impact on community well-being. The review encompasses existing resilience models for single hospitals and schools, their role when connected with other hospitals and schools in a network, their reliance on each other as interdependent systems, and their role in community resilience and stability. Different mitigation strategies and policies to enhance hospital and school systems’ resilience after extreme natural hazards are also summarized. The paper concludes with a series of recommendations to improve current models for social institutions, enhance the connection between existing hospital and school resilience models and community resilience frameworks, and develop social stability indices that policymakers can use in preparing and mitigating future extreme events.","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125054753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Non-emergency responses in the 311 system during the early stage of the COVID-19 pandemic: a case study of Kansas city","authors":"","doi":"10.20517/dpr.2022.08","DOIUrl":"https://doi.org/10.20517/dpr.2022.08","url":null,"abstract":"","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132797883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In many probabilistic analysis problems, the homogeneous/nonhomogeneous non-Gaussian field is represented as a mapped Gaussian field based on the Nataf translation system. We propose a new sample-based iterative procedure to estimate the underlying Gaussian correlation for homogeneous/nonhomogeneous non-Gaussian vector or field. The numerical procedure takes advantage that the range of feasible correlation coefficients for non-Gaussian random variables is bounded if the translation system is adopted. The estimated underlying Gaussian correlation is then employed for unconditional as well as conditional simulation of the non-Gaussian vector or field according to the theory of the translation process. We then present the steps for augmenting the simulated non-Gaussian field through the Karhunen-Loeve expansion for a refined discretized grid of the field. In addition, the steps to extend the procedure described in the previous section to the multi-dimensional field are highlighted. The application of the proposed algorithms is presented through numerical examples.
{"title":"Unconditional and conditional simulation of nonstationary and non-Gaussian vector and field with prescribed marginal and correlation by using iteratively matched correlation","authors":"M. Y. Xiao, H. Hong","doi":"10.20517/dpr.2022.01","DOIUrl":"https://doi.org/10.20517/dpr.2022.01","url":null,"abstract":"In many probabilistic analysis problems, the homogeneous/nonhomogeneous non-Gaussian field is represented as a mapped Gaussian field based on the Nataf translation system. We propose a new sample-based iterative procedure to estimate the underlying Gaussian correlation for homogeneous/nonhomogeneous non-Gaussian vector or field. The numerical procedure takes advantage that the range of feasible correlation coefficients for non-Gaussian random variables is bounded if the translation system is adopted. The estimated underlying Gaussian correlation is then employed for unconditional as well as conditional simulation of the non-Gaussian vector or field according to the theory of the translation process. We then present the steps for augmenting the simulated non-Gaussian field through the Karhunen-Loeve expansion for a refined discretized grid of the field. In addition, the steps to extend the procedure described in the previous section to the multi-dimensional field are highlighted. The application of the proposed algorithms is presented through numerical examples.","PeriodicalId":265488,"journal":{"name":"Disaster Prevention and Resilience","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130885263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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