Cloud-IDA-MSA Conversion of Fragility Curves for Efficient and High-Fidelity Resilience Assessment

Pang, Yutao; Wang, Xiaowei

doi:10.1061/(asce)st.1943-541x.0002998

Cited by 110 publications

(31 citation statements)

References 53 publications

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“…Seismic fragility curves are used for road system resilience assessment [44]. The fragility curve developed by HAZUS [45] is utilized to estimate road reliability after the earthquake.…”

Section: Initial Damage Situation Under Earthquake Hazardmentioning

confidence: 99%

A Novel Deep Reinforcement Learning Model for Resilient Road Network Recovery from Multiple Hazards

Fan

Zhang

Wang

et al. 2022

Preprint

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As the backbone and the ‘blood vessel’ of modern cities, road networks provide critical support for community activities and economic growth, with their roles even more crucial due to the dramatic progress in urbanization. The service of road networks is subjected to the increasing frequency of high-consequence natural hazards such as earthquakes, floods, hurricanes, etc. Identifying resilient restoration sequences is essential to mitigate the disruption of such important infrastructure networks. This paper investigates a novel decision-support model to optimize post-disaster road network repair sequence. The model, named as GCN-DRL model, integrates the advantages of deep reinforced learning (DRL) with graph convolutional neural network (GCN), two emerging artificial intelligence (AI) techniques to achieve efficient recovery of road network service. The model is applied to analyze two cases of community road networks in the US that are subjected to different types of hazards, i.e., earthquakes and flooding. The performance of repair sequence by the GCN-DRL model is compared with two commonly used methods, i.e., repair sequence by the genetic algorithm and by prioritization based on graph importance with betweenness centrality. The results showed the decision sequence by GCN-DRL model consistently achieved superior performance in road network restoration than the conventional methods. The AI-based decision model also features high computational efficiency since the GCN-DRL model can be trained before the hazard. With a pre-trained GCN-DRL model, a close to optimal decision-making process can be made available rapidly for different types of new hazards, which is advantageous in efficiently responding to hazards when they happen. This study demonstrates the promise of a new AI-based decision support model to improve the resilience of road networks by enabling efficient post-hazards recovery.

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“…Seismic fragility curves are used for road system resilience assessment [44]. The fragility curve developed by HAZUS [45] is utilized to estimate road reliability after the earthquake.…”

Section: Initial Damage Situation Under Earthquake Hazardmentioning

confidence: 99%

A Novel Deep Reinforcement Learning Model for Resilient Road Network Recovery from Multiple Hazards

Fan

Zhang

Wang

et al. 2022

Preprint

Self Cite

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“…To carry out the wind fragility analysis of the main structure model in Figure 7(b), the 10-minute mean wind speed is selected as the IM of the wind hazard, the ISDR max is selected as the EDP, and two typical wind directions (0°and 90°) are considered for the wind load, as shown in Figure 13. It is assumed that the EDP of the wind hazard demand of the main structure is related to the IM of the wind hazard through the following equation [39][40][41]:…”

Section: Wind Fragility Analysis Of the Main Structurementioning

confidence: 99%

Fragility Comprehensive Assessment of Low‐Rise Cold‐Formed Steel Framed Wall Structure Subjected to Wind Load

Zhang

Hou

Ding

et al. 2021

Shock and Vibration

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This paper presents a comprehensive assessment method of the fragility of low-rise cold-formed steel (CFS) framed wall structures subjected to wind hazards considering the fragility of both the main structure and the cladding system. The effects of wind directions on the fragility of CFS framed wall structures were also studied. For the main structure, the fragility curve is established using the maximum interstory drift ratio (ISDRmax) as the performance index for assessing the wind fragility of the structure. For the cladding system, the probabilistic models of the wind load and the cladding component resistance are established based on Monte Carlo simulation, and then methods for the fragility assessment of single cladding components and the cladding system under wind hazards considering the influence of the number and arrangement of the cladding components are proposed. The results indicated that, under strong wind, the cladding system may be damaged before the required wind resistance capacity of the main structure is exhausted. In particular, the roof sheathing is the most prone to damage, followed by the stud wall. That is, before the main structure is severely damaged or collapses, the cladding systems may be severely damaged, rendering the structure unusable. Therefore, the comprehensive assessment of the fragility of this type of structure subjected to wind hazard considering the fragility of both the main structure and the cladding system is more accurate. This study is of great significance for the improvement of the wind resistance performance of CFS structures and the popularization of this type of structure.

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“…Due to the simplicity of its underlying formulations, Cloud analysis only requires fewer number of unscaled ground motions with uniformly distributed intensity levels of IM to develop the fragility curves (Billah and Alam, 2015;Chen et al, 2022;Cornell et al, 2002;Jalayer et al, 2007Jalayer et al, , 2011Pang et al, 2014Pang et al, , 2022Riddell, 2007;Xiang et al, 2020). Thus, compared to MSA and IDA, Cloud is more efficient since it only needs several nonlinear dynamic analyses based on the set of unscaled ground motions (Pang et al, 2021a;Pang and Wang, 2021). Meanwhile, Cloud analysis is based on a few simplifying assumptions (e.g.…”

Section: Introductionmentioning

confidence: 99%

E-Cloud: Efficient seismic fragility assessment of structures based on enhanced cloud analysis

2022

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Cloud analysis is based on linear regression in the logarithmic space using least squares, in which a large number of nonlinear dynamic analyses are required to ensure the regression accuracy. Thus, it needs significant computational effort to derive fragility curves especially for the complicated structures. This article proposed the Enhanced Cloud approach (E-Cloud) to enhance the efficiency but maintain the accuracy of Cloud analysis. The basic concept of the E-Cloud method aims to utilize both maximum and additional seismic responses with corresponding intensity measures (IMs) from ground motions for the logarithmic linear regression in Cloud analysis. The additional seismic responses can be obtained from the engineering demand parameter (EDP)-IM curve, which is generated at the specific time duration when structures are subjected to intensifying dynamic excitations in nonlinear time–history analysis. The required number of nonlinear time–history analyses in Cloud analysis is reduced since additional seismic responses (with their corresponding IM value) are used as additional Cloud points for the Cloud regression. The proposed E-Cloud method is applied for the case study of a typical reinforced concrete (RC) frame structure. By comparison of probabilistic seismic demand models and fragility curves from the E-Cloud method to Cloud analysis, it is demonstrated that the proposed E-Cloud method can significantly improve the computational efficiency of the Cloud analysis, which also leads to accurate fragility estimates of the structures.

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Cloud-IDA-MSA Conversion of Fragility Curves for Efficient and High-Fidelity Resilience Assessment

Cited by 110 publications

References 53 publications

A Novel Deep Reinforcement Learning Model for Resilient Road Network Recovery from Multiple Hazards

A Novel Deep Reinforcement Learning Model for Resilient Road Network Recovery from Multiple Hazards

Fragility Comprehensive Assessment of Low‐Rise Cold‐Formed Steel Framed Wall Structure Subjected to Wind Load

E-Cloud: Efficient seismic fragility assessment of structures based on enhanced cloud analysis

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