papers.bib

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@phdthesis{lee2023development,
  title={Development of seismic reliability analysis methods for large-scale infrastructure networks},
  author={Lee, Dongkyu},
  year={2023},
  school={Seoul National University},
  doi={10.23170/snu.000000179598.11032.0000217},
  html={https://s-space.snu.ac.kr/handle/10371/196249},
  abbr={Ph.D. Thesis},
  preview={Thesis.png},
  bibtex_show={true}
}

@article{lee2021multi,
  author={Lee, Dongkyu and Song*, Junho},
  title={Multi-scale seismic reliability assessment of networks by centrality-based selective recursive decomposition algorithm},
  journal={Earthquake Engineering \& Structural Dynamics},
  volume={50},
  number={8},
  pages={2174--2194},
  keywords={betweenness centrality, clustering, modularity, multi-scale approach, network reliability, non-simulation-based approach, recursive decomposition algorithm, seismic reliability},
  doi={10.1002/eqe.3447},
  html={https://snu.elsevierpure.com/en/publications/multi-scale-seismic-reliability-assessment-of-networks-by-central},
  abstract={As lifeline networks such as transportation or electricity networks in modern societies are intricately interlocked, a small number of components damaged by natural or man-made disasters can have a great impact on network performance. For this reason, it is essential to assure the capability of rapid assessment of network reliability to make prompt follow-up measures. Despite the rapid development of various algorithms and computing power, the capability is still limited due to computational cost for analyzing the connectivity of a single origin and destination (O/D) node pair in large-scale networks. Therefore, this paper introduces a new algorithm utilizing network centrality, termed “centrality-based selective recursive decomposition algorithm” (CS-RDA). By preferentially decomposing the node which is most likely to belong to the min-cut identified based on the betweenness centrality, the convergence of the bounds on the O/D connectivity can be expedited significantly. This paper also introduces a new multi-scale analysis approach termed “edge-betweenness algorithm.” The algorithm groups components such that its modularity is maximized, by sequentially removing edges that have the highest level of betweenness centrality. As a result, the reliability of large-scale networks can be accurately evaluated in a short time owing to the reduced complexity of the simplified network. The proposed methods are successfully demonstrated by a hypothetical network example, the highway bridge networks in San Jose and San Diego in California, USA.},
  year={2021},
  abbr={EESD},
  publisher={Wiley},
  preview={Paper1.png},
  bibtex_show={true},
  selected={true},
  google_scholar_id={qjMakFHDy7sC}
}
@article{lee2023risk,
  author={Lee, Dongkyu and Song*, Junho},
  title={Risk-informed operation and maintenance of complex lifeline systems using parallelized multi-agent deep Q-network},
  journal={Reliability Engineering \& System Safety},
  volume={239},
  pages={109512},
  keywords={deep reinforcement learning, lifeline systems, life-cycle cost, Markov decision process, operation & maintenance, parallel processing},
  doi={10.1016/j.ress.2023.109512},
  html={https://snu.elsevierpure.com/en/publications/risk-informed-operation-and-maintenance-of-complex-lifeline-syste},
  abstract={Lifeline systems such as transportation and water distribution networks may deteriorate with age, raising the risk of system failure or degradation. Thus, system-level sequential decision-making is essential to address the problem cost-effectively while minimizing the potential loss. Researchers have proposed to assess the risk of lifeline systems using Markov decision processes (MDPs) to identify a risk-informed operation and maintenance (O&M) policy. In complex systems with many components, however, it is potentially intractable to find MDP solutions because the numbers of states and action spaces increase exponentially. This paper proposes a multi-agent deep reinforcement learning framework, termed parallelized multi-agent deep Q-network (PM-DQN), to overcome the curse of dimensionality. The proposed method takes a divide-and-conquer strategy, in which multiple subsystems are identified by community detection, and each agent learns to achieve the O&M policy of the corresponding subsystem. The agents establish policies to minimize the decentralized cost of the cluster unit, including the factorized cost. Such learning processes occur simultaneously in several parallel units, and the trained policies are periodically synchronized with the best ones, thereby improving the master policy. Numerical examples demonstrate that the proposed method outperforms baseline policies, including conventional maintenance schemes and the subsystem-level optimal policy.},
  year={2023},
  abbr={RESS},
  publisher={Elsevier},
  bibtex_show={true},
  selected={true},
  google_scholar_id={UeHWp8X0CEIC},
  pdf={Lee_2023_RESS.pdf},
  preview={Paper2.jpg}
}
@article{kim2024seismic,
  author={Kim*, Dongjoo and Song, Junho and Lee, Young-Joo and Lee, Dongkyu and Yoon, Sungsik and Yoon, Dong Keun and Lee, Yong Kang and Kwon, Youngjun and Choi, Yeon-Woo},
  title={Seismic performance management of aging road facilities in Korea: Part 2- Decision-making support technology and its application},
  journal={KSCE Journal of Civil Engineering},
  volume={28},
  number={5},
  pages={1889--1902},
  keywords={seismic performance, road facilities, aging, fragility curve, limit states},
  doi={10.1007/s12205-023-0601-3},
  html={https://snu.elsevierpure.com/en/publications/seismic-performance-management-of-aging-road-facilities-in-korea-},
  abstract={This study aims to develop a decision-making support system for managing aged road facilities in a target road network based on seismic performance evaluation. For this purpose, the seismic fragility considering aging effect is analyzed for bridges, tunnels, retaining walls, and slopes to assess the direct damage to individual road facilities, as described in the companion paper. In this paper, based on the seismic fragilities of road facilities, the degradation of the road network's seismic performance and social and economic resilience is evaluated. The decision support system is then developed based on the seismic risk assessment method (SRA) for the seismic management of old road facilities suitable for domestic conditions. The SRA method includes the calculation of direct and indirect damage of road networks, the assessment of socio-economic resilience to disaster in South Korea, and the basis for decision-making. In addition, a geospatial information-based software for repair and reinforcement decisions is developed. The developed decision-making support software is verified by using Pohang city located in the East part of Korea as a test-bed example.},
  year={2024},
  abbr={KSCE},
  publisher={Elsevier},
  bibtex_show={true},
  google_scholar_id={b0M2c_1WBrUC},
  pdf={Kim_2024_KSCE.pdf},
  preview={Paper3.png}
}
@article{lee2025efficient,
  author={Lee, Dongkyu and Wang*, Ziqi and Song*, Junho},
  title={Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation},
  journal={Reliability Engineering \& System Safety},
  volume={260},
  pages={110947},
  keywords={fragility, lifeline networks, network reliability, seismic reliability, subset simulation},
  arxiv={2310.10232},
  doi={10.1016/j.ress.2025.110947},
  html={https://snu.elsevierpure.com/en/publications/efficient-seismic-reliability-and-fragility-analysis-of-lifeline-},
  abstract={Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, the seismic safety and resilience of a community are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic reliability analyses of networks often encounter significant challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes to overcome these challenges by a variance-reduction method for network fragility analysis using subset simulation. The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by identifying an implicit relationship between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.},
  year={2025},
  abbr={RESS},
  publisher={Elsevier},
  bibtex_show={true},
  selected={true},
  google_scholar_id={ufrVoPGSRksC},
  preview={Paper4.gif},
  annotation={*Corresponding Authors}
}

@article{lee2025dual,
  author={Lee, Dongkyu and Byun*, Ji-Eun and Song*, Junho},
  title={Dual graph-based Bayesian network modeling with Rao-Blackwellization for seismic reliability and complexity quantification of network connectivity},
  journal={Earthquake Engineering \& Structural Dynamics},
  volume={54},
  number={10},
  pages={2387--2402},
  keywords={adaptive importance sampling, Bayesian network, complexity analysis, connectivity reliability, junction tree algorithm, Rao-Blackwellization},
  doi={10.1002/eqe.4362},
  html={https://snu.elsevierpure.com/en/publications/dual-graph-based-bayesian-network-modeling-with-rao-blackwellizat},
  abstract={Modern societies depend on various lifeline networks such as transportation, electricity, and gas distribution systems, which are vulnerable to seismic events. Although numerous analytical and simulation-based methods have been developed for efficient seismic system reliability analysis (SRA), dealing with high-dimensional events arising from large-scale infrastructure networks remains challenging. To address this challenge, this paper proposes a system reliability method that efficiently computes the connectivity of directed graphs. The method employs the dual graph representation of a target system to automate the construction of a Bayesian network (BN). This enables the application of the junction tree algorithm, a well-established BN inference method, to perform reliability analysis and quantify complexity based on a network topology. The paper further tackles SRA challenges associated with fully correlated seismic uncertainties, which typically lead to a significant increase in computational complexity. To this end, we propose to combine a cross entropy-based adaptive importance sampling technique with Rao-Blackwellization. Thereby, sampling methods and exact analytical inference can be effectively combined to improve computational efficiency for seismic SRA of lifeline networks. The proposed methods are demonstrated through three numerical examples.},
  year={2025},
  abbr={EESD},
  publisher={Wiley},
  bibtex_show={true},
  selected={true},
  pdf={Lee_2025_EESD.pdf},
  preview={Paper5.png},
  google_scholar_id={tkaPQYYpVKoC},
  annotation={*Corresponding Authors}
}

@inproceedings{lee2020centrality,
  title={Centrality-based selective recursive decomposition algorithm for efficient network reliability analysis},
  author={Lee, Dongkyu and Song, Junho},
  booktitle={Proc. 7th International Symposium on Reliability Engineering and Risk Management},
  year={2020},
  month={June},
  html={https://www.researchgate.net/profile/Dongkyu-Lee-20/publication/374754971_Centrality-based_selective_recursive_decomposition_algorithm_for_efficient_network_reliability_analysis/links/652e17fa7d0cf66a67346765/Centrality-based-selective-recursive-decomposition-algorithm-for-efficient-network-reliability-analysis.pdf},
  location={Beijing, China},
  abbr={ISRERM 2020},
  bibtex_show={true},
  pdf={ISRERM2020.pdf},
  preview={ISRERM2020.png}
}

@inproceedings{lee2022maintenance,
  title={Maintenance Decision-making for Infrastructure Systems Using Clustering-based Cooperative Multi-Agent Deep Q-Network},
  author={Lee, Dongkyu and Song, Junho},
  booktitle={Proc. 8th International Symposium on Reliability Engineering and Risk Management},
  year={2022},
  month={September},
  doi={10.3850/978-981-18-5184-1_MS-08-062-cd},
  html={https://www.researchgate.net/publication/371271917_Maintenance_decision-making_for_infrastructure_systems_using_clustering-based_cooperative_multi-agent_deep_Q-network},
  location={Hannover, Germany},
  isbn = "9789811851841",
  abbr={ISRERM 2022},
  bibtex_show={true},
  pdf={ISRERM2022.pdf},
  preview={ISRERM2022.png}
}

@inproceedings{lee2022parallelized,
  title={Parallelized multi-agent deep reinforcement learning for optimal maintenance of large-scale infrastructure systems},
  author={Lee, Dongkyu and Song, Junho},
  booktitle={Proc. ASCE Engineering Mechanics Institute Conference},
  location={Maryland, USA},
  year={2022},
  month={May},
  abbr={EMI 2022},
  preview={EMI2022.png}
}

@inproceedings{lee2022development,
  title={Development of centrality-based selective recursive decomposition algorithm using network simplification},
  author={Lee, Dongkyu and Song, Junho},
  booktitle={Proc. 13th International Conference on Structural Safety and Reliability},
  year={2022},
  month={September},
  html={https://www.researchgate.net/publication/383413637_Development_of_centrality-based_selective_recursive_decomposition_algorithm_using_network_simplification},
  location={Shanghai, China},
  abbr={ICOSSAR 2021-2022},
  bibtex_show={true},
  pdf={ICOSSAR2021.pdf},
  preview={ICOSSAR2021.png}
}

@inproceedings{lee2023network,
  title={Network reliability analysis and complexity quantification using Bayesian network and dual representation},
  author={Lee, Dongkyu and Byun, Ji-Eun and Song, Junho and Sadeghi, Kayvan},
  booktitle={Proc. 14th International Conference on Applications of Statistics and Probability in Civil Engineering},
  year={2023},
  month={July},
  html={http://hdl.handle.net/2262/103359},
  location={Dublin, Ireland},
  abbr={ICASP14},
  bibtex_show={true},
  google_scholar_id={Y5dfb0dijaUC},
  pdf={ICASP14.pdf},
  preview={ICASP14.png}
}

@inproceedings{lee2023efficient,
  title={Efficient Monte Carlo simulation for seismic reliability analysis of lifeline networks},
  author={Lee, Dongkyu and Wang, Ziqi and Song, Junho},
  booktitle={Proc. ASCE INSPIRE 2023},
  location={Virginia, USA},
  year={2023},
  month={November},
  abbr={INSPIRE 2023},
  preview={INSPIRE.png}
}

@inproceedings{lee2024seismic1,
  title={Seismic fragility analysis of lifeline networks using subset simulation},
  author={Lee, Dongkyu and Wang, Ziqi and Song, Junho},
  booktitle={Proc. SIAM Conference on Uncertainty Quantification},
  year={2024},
  month={Feb},
  location={Trieste, Italy},
  abbr={UQ24}
}

@inproceedings{seok2024hierarchical,
  title={Hierarchical matrix-based system reliability method for large-scale systems},
  author={Seok, Uichan and Lee, Dongkyu and Song, Junho},
  booktitle={Proc. SIAM Conference on Uncertainty Quantification},
  year={2024},
  month={Feb},
  location={Trieste, Italy},
  abbr={UQ24},
  preview={UQ24_2.png}
}

@inproceedings{lee2024efficient,
  title={Efficient assessment of network seismic fragility curves using subset simulation},
  author={Lee, Dongkyu and Wang, Ziqi and Song, Junho},
  booktitle={Proc. ASCE Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference},
  year={2024},
  month={May},
  location={Illinois, USA},
  abbr={EMI/PMC 2024}
}

@inproceedings{lee2024combining,
  title={Combining Rao-Blackwellization and importance sampling for connectivity reliability analysis of networks with dependent components},
  author={Lee, Dongkyu and Byun, Ji-Eun and Song, Junho},
  booktitle={Proc. 9th European Congress on Computational Methods in Applied Sciences and Engineering},
  year={2024},
  month={June},
  location={Lisbon, Portugal},
  abbr={ECCOMAS 2024},
  preview={ECCOMAS2024.png}
}

@inproceedings{lee2024seismic2,
  title={Seismic fragility curves of lifeline networks based on subset simulation},
  author={Lee, Dongkyu and Wang, Ziqi and Song, Junho},
  booktitle={Proc. 18th World Conference on Earthquake Engineering},
  html={https://proceedings-wcee.org/view.html?id=23774&conference=18WCEE},
  year={2024},
  month={June},
  location={Milan, Italy},
  abbr={WCEE2024},
  bibtex_show={true},
  pdf={WCEE2024.pdf},
  preview={WCEE2024.png},
}

@inproceedings{lee2026redundancy,
  title={Redundancy factors for structural systems via Hamiltonian Monte Carlo-based subset simulation},
  author={Lee, Dongkyu and Broccardo, Marco},
  booktitle={Proc. 17th World Congress on Computational Mechanics & 10th European Congress on Computational Methods in Applied Sciences and Engineering},
  Xhtml={https://wccm-eccomas2026.org},
  year={2026},
  month={July},
  location={Munich, Germany},
  abbr={WCCM-ECCOMAS 2026},
  Xbibtex_show={true},
  Xpdf={WCCM_ECCOMAS.pdf}
}