相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article
Energy & Fuels
Dawei Qiu et al.
Summary: Extreme events have a significant impact on the normal operations of microgrids, resulting in severe outages and high restoration costs. To address this issue, a decentralized decision-making model for the dispatch of repair crews in a multi-energy microgrid is formulated. A hierarchical multi-agent reinforcement learning algorithm is proposed to solve the dispatch problem.
Article
Engineering, Industrial
Pegah Rokhforoz et al.
Summary: This paper proposes a safe reinforcement learning algorithm for generating bidding decisions and scheduling maintenance in a competitive electricity market. By combining reinforcement learning and a predicted safety filter, the proposed approach can handle the challenges of incomplete information and critical safety constraints, while achieving a higher profit and satisfying system safety requirements.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Seyedvahid Najafi et al.
Summary: This paper proposes a modified deep reinforcement learning algorithm for optimizing condition-based maintenance (CBM) policy for a multi-unit system. A novel environment simulator is also suggested to evaluate the system's reliability characteristics. The proposed algorithm is applied to a hydroelectric power system to demonstrate its effectiveness.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Chuanzhou Jia et al.
Summary: This paper proposes a new two-stage stochastic optimization model to increase the resilience of critical infrastructure through pre-disaster planning and post-disaster scheduling. The model simultaneously determines the locations for building restoration team stations before disasters and the routing of restoration tasks after disasters. Two layers of interdependent infrastructures are considered: the flow transmission layer to meet customers' demands and the restoration crew transportation layer. The goal is to minimize the total cost, including station building cost and penalty cost due to performance loss. A column-generation-based Lagrangian Relaxation algorithm with a dynamic programming algorithm is proposed to solve the computational complexity of the problem, and real-world infrastructures (French natural gas network) are used for extensive computational experiments to illustrate the merits of the approach.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Ao Du et al.
Summary: Given the potential risks posed by earthquakes and the need to make informed decisions to enhance seismic resilience of built environments, there is a growing focus on regional seismic risk and resilience assessment. However, there is currently a lack of comprehensive review studies summarizing the advancements in this field. This study fills this gap by conducting a systematic review, covering various aspects of regional seismic risk assessment and regional seismic resilience assessment, providing insights into existing models and tools, and highlighting challenges and future directions.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Haipeng Xie et al.
Summary: This paper proposes a robustness assessment and enhancement method for load restoration in distribution systems using deep reinforcement learning. The method improves the system's robustness against adversarial examples and enhances the stability of load restoration decision-making through adversarial training and retraining.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Zachary Hamida et al.
Summary: This paper introduces a hierarchical RL formulation for maintenance planning on bridges, which addresses the challenges of complexity and scale. By utilizing state and temporal abstraction, the formulation decomposes large state and action spaces into smaller ones. Additionally, an open-source RL environment is developed to describe the propagation of deterioration condition over time, and demonstrated by solving maintenance planning problems at different levels.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Dongkyu Lee et al.
Summary: A multiagent deep reinforcement learning framework called parallelized multi-agent deep Q-network (PM-DQN) is proposed to overcome the curse of dimensionality in complex systems. The method divides the system into multiple subsystems, with each agent learning the operation and maintenance policy of the corresponding subsystem. The learning processes occur simultaneously in parallel units, and the trained policies are periodically synchronized to improve the master policy. Numerical examples demonstrate that the proposed method outperforms baseline policies.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Interdisciplinary Applications
Xudong Fan et al.
Summary: This paper introduces a graph convolutional neural network-integrated deep reinforcement learning model to support optimal repair decisions for water distribution networks (WDNs) after earthquakes. The model incorporates the dynamic evolution of WDN performance indicators during the post-earthquake recovery process and uses a GCN-DRL framework to select the optimal repair sequence for achieving the highest system resilience. Experimental results show that the model's repair decisions achieved the highest system resilience index values and the fastest system performance recovery.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Review
Geosciences, Multidisciplinary
Amanda Melendez et al.
Summary: This paper reviews the latest computational methods for modeling community resilience in the past 10 years, discussing static and dynamic computational models, including rating models for community resilience assessment and probabilistic modeling in uncertain environments.
Article
Management
Basem A. Alkhaleel et al.
Summary: This paper introduces two stochastic optimization models for scheduling repair activities in disrupted critical infrastructure networks to enhance system resilience. An improved fast forward algorithm is proposed to reduce the number of chosen scenarios by considering uncertainties. Furthermore, a conditional value-at-risk metric is incorporated to assess the risks associated with post-disruption scheduling plans.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Honglan Huang et al.
Summary: This study presents a methodology for dynamic seismic damage assessment of distributed infrastructure systems using graph neural networks and semi-supervised machine learning. The approach combines traditional fragility-based models or machine learning classification algorithms with actual damage observations to update pre-event damage assessments. The evaluation results show that the methodology provides better damage estimates than existing models and can quantify damage accurately based on partial information.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Engineering, Industrial
Irene Alisjahbana et al.
Summary: In this paper, we propose an optimization approach to the post-disaster reconstruction of school systems. We develop a greedy algorithm to find a reasonable order for school reconstruction, considering multiple factors. Through empirical evaluation using real data from the 2018 Lombok Earthquakes, we show that our greedy algorithm outperforms traditional policies in post-disaster decision-making.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Min Xu et al.
Summary: This paper proposes a two-stage stochastic repair model that considers uncertainty, and solves the recovery decision problem under large-scale and small-scale disruptions using scenario generation and reduction method and an efficient enumeration algorithm. Numerical experiments demonstrate the efficiency of the proposed algorithm and indicate that the stochastic solution might not be as good as the deterministic solution when a large number of repair time scenarios are considered.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Review
Engineering, Civil
Mahdi Shadabfar et al.
Summary: The search for how safe is safe enough has led to the evolution of design philosophies, from ensuring system capacity exceeds demand to performance-based design and resilience-based design. These changes aim to balance safety and recovery capability, especially under extreme loads.
ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING
(2022)
Article
Engineering, Civil
Izuchukwu A. Ugwu et al.
Summary: Critical facilities such as hospitals heavily rely on complex infrastructure systems, but their functionality is impaired after a disaster. Existing infrastructure recovery planning approaches lack a systematic method for prioritizing the recovery of critical facilities. This paper presents a framework that focuses on the recovery planning of healthcare facilities using a simulation method.
JOURNAL OF INFRASTRUCTURE SYSTEMS
(2022)
Article
Construction & Building Technology
Nebiyu Siraj Kedir et al.
Summary: Decision-making in construction planning and scheduling is complex due to various constraints and uncertainties. This paper proposes a hybrid model that combines reinforcement learning and simulation to optimize project duration, support decision-making, and address the knowledge gap in activity sequencing and work breakdown under uncertainty. The model is demonstrated through case studies of practical construction scheduling problems.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Engineering, Industrial
Reza Mohammadi et al.
Summary: Developing optimal rail renewal and maintenance planning is crucial for the railroad industry. This study proposes a Deep Reinforcement Learning (DRL) based approach that optimizes the planning over time, considering cost-effectiveness and risk reduction. The proposed approach is demonstrated to reduce budget consumption and improve network reliability and safety when applied to a railroad network.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Civil
Shaopeng Li et al.
Summary: The uncertain weather and traffic conditions caused by hurricanes make managing transportation infrastructure a stochastic decision problem. This study proposes a deep reinforcement learning-based decision support system to minimize network-level losses caused by hurricanes.
Article
Construction & Building Technology
Sebastiano Marasco et al.
Summary: The paper introduces an integrated platform that implements a community hybrid model with real-time simulation capabilities to assess seismic resilience and vulnerability of critical infrastructures at the urban level. Different seismic scenarios have been applied to a large-scale virtual city model, proving the platform's effectiveness in analyzing emergencies and implementing countermeasures to improve community response and overall resilience.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Engineering, Industrial
Li Sun et al.
Summary: This paper explores a new approach to minimize functionality losses of Road Networks (RNs) under earthquake hazards by leveraging post-shock rapid responses. An agent-based modelling (ABM) framework is developed for nuanced examination on resilience of earthquake-damaged RNs. Through a case-study in Luchon, France, post-shock rapid responses are shown to be an effective strategy to increase the recovery rate of RNs' functionality in the aftermath of damaging earthquakes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Civil
Saeed Nozhati
Summary: The functioning of infrastructure and buildings after a disaster is crucial for modern urban centers, requiring an effective emergency management plan. Decision-making framework at the community level needs to address large-scale stochastic scheduling problems, managing complex infrastructure systems while avoiding the curse of dimensionality. The proposed approach can consider and improve company recovery policies, providing optimal decisions for post-disaster recovery.
Article
Geosciences, Multidisciplinary
Donglian Gu et al.
Summary: This study proposes a high-efficiency simulation framework by coupling computational fluid dynamics and mechanistic tree damage modeling to realize the near real-time prediction of wind damage to urban trees. The methods used in each step are validated, applied in a case study at Tsinghua University campus in Beijing, and a user-friendly program is developed for evaluating wind-induced damage risk. Hypothetical strong wind events are used to exemplify the practicality of the proposed framework.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2021)
Article
Computer Science, Information Systems
Shuliang Wang et al.
Summary: This paper presents a methodological framework for resilience analysis of interdependent critical infrastructure systems using artificial power and gas networks as an example. Deep learning is used to identify network topology attributes and vulnerability processes. Recovery strategies are proposed and an optimal resilience improvement strategy is obtained through a resilience triangle analysis.
INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURE PROTECTION
(2021)
Article
Engineering, Geological
Tian You et al.
Summary: This paper introduces a framework to link the seismic performance of buildings with community resilience, and proposes a novel long-term resilience indicator that integrates different magnitude earthquake probabilities. The framework is effective in assessing whether the desired community resilience goals can be achieved by evaluating the seismic performance of buildings and highlights the importance of considering the interdependence between infrastructure systems.
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
(2021)
Article
Geosciences, Multidisciplinary
Taiyi Zhao et al.
Summary: This paper proposes an agent-based modeling framework to examine the impact of looped interdependencies among Critical Infrastructure Systems on their seismic resilience. By quantifying and tracking the functionality losses and post-shock recovery processes of the coupled Transportation System and Electric Power Supply System, the study shows that the recovery of coupled infrastructure systems is influenced by the degree of interdependence between them.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2021)
Article
Computer Science, Interdisciplinary Applications
Chen Xiong et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2020)
Article
Engineering, Industrial
Saeed Nozhati et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2020)
Article
Computer Science, Interdisciplinary Applications
Neetesh Sharma et al.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2020)
Article
Engineering, Civil
Agam Tomar et al.
JOURNAL OF INFRASTRUCTURE SYSTEMS
(2020)
Article
Geosciences, Multidisciplinary
Fei Wang et al.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2020)
Article
Engineering, Industrial
Sabarethinam Kameshwar et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2019)
Article
Geosciences, Multidisciplinary
Deniz Berfin Karakoc et al.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2019)
Article
Engineering, Industrial
Saeed Nozhati et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2019)
Article
Engineering, Civil
Xinzheng Lu et al.
EARTHQUAKE SPECTRA
(2018)
Article
Engineering, Civil
Peihui Lin et al.
Article
Construction & Building Technology
Dorothy Reed et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2016)
Article
Construction & Building Technology
Henry V. Burton et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2016)
Article
Construction & Building Technology
G. P. Cimellaro et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2016)
Article
Construction & Building Technology
Gian Paolo Cimellaro et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2016)
Article
Construction & Building Technology
Dorothy Reed et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2016)
Article
Construction & Building Technology
Henry V. Burton et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2016)
Article
Construction & Building Technology
G. P. Cimellaro et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2016)
Article
Construction & Building Technology
Gian Paolo Cimellaro et al.
JOURNAL OF STRUCTURAL ENGINEERING
(2016)
Article
Engineering, Civil
Kenneth W. Campbell et al.
EARTHQUAKE SPECTRA
(2014)
Article
Engineering, Civil
Gian Paolo Cimellaro et al.
ENGINEERING STRUCTURES
(2010)
Article
Engineering, Civil
M Bruneau et al.
EARTHQUAKE SPECTRA
(2003)