Applications of artificial intelligence for disaster management
Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …
socioeconomic loss. The actual damage and loss observed in the recent decades has …
A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems
Cyber–physical systems (CPSs) are confronted with major problems, such as high
proportions of renewable energy penetration and frequent extreme events, which severely …
proportions of renewable energy penetration and frequent extreme events, which severely …
Automated identification of substantial changes in construction projects of airport improvement program: Machine learning and natural language processing …
Contractual changes—mainly substantial changes—within airport improvement program
(AIP) projects represent a critical risk that could result in severe negative time and cost …
(AIP) projects represent a critical risk that could result in severe negative time and cost …
Multiagent reinforcement learning for project-level intervention planning under multiple uncertainties
Reinforcement learning (RL) has recently been adopted by infrastructure asset management
(IAM) researchers for adding flexibility regarding uncertainties in preventive actions decision …
(IAM) researchers for adding flexibility regarding uncertainties in preventive actions decision …
Reinforcement learning in construction engineering and management: A review
The construction engineering and management (CEM) domain frequently meets complex
tasks due to the unavoidable complicated operation environments and the involvement of …
tasks due to the unavoidable complicated operation environments and the involvement of …
Analyzing multisector stakeholder collaboration and engagement in housing resilience planning in greater Miami and the beaches through social network analysis
Housing resilience planning is a challenging process that requires active participation of
multisector stakeholders, including public agencies, private industries, nongovernmental …
multisector stakeholders, including public agencies, private industries, nongovernmental …
Data Mining for Community Resilience: Understanding Stakeholder Value Systems across Communities in the State of Florida
Achieving community resilience is a complex task, as there is no 'one-size-fits-all'approach
that can be applied uniformly across all communities. Communities with unique …
that can be applied uniformly across all communities. Communities with unique …
Road-reconstruction after multi-locational flooding in multi-agent deep RL with the consideration of human mobility-Case study: Western Japan flooding in 2018
Record-breaking heavy rain occurred in Western Japan from June 28 to July 8, 2018. Many
roads in Hiroshima and Okayama Prefecture were disrupted simultaneously. The …
roads in Hiroshima and Okayama Prefecture were disrupted simultaneously. The …
[HTML][HTML] Collaborative modelling for goal-oriented scenario planning: A resilience planning case study in the context of greater Sydney
Worldwide, natural hazards have an increasing impact on ever expanding urbanised areas.
Therefore, authorities need more sophisticated planning support systems (PSS) to enhance …
Therefore, authorities need more sophisticated planning support systems (PSS) to enhance …
Generating Diverse Optimal Road Management Plans in Post-Disaster by Applying Envelope Multi-Objective Deep Reinforcement Learning
The authors used a data-driven reinforcement learning model for the post-disaster rapid
recovery of human mobility, considering human-mobility recovery rate, road connectivity …
recovery of human mobility, considering human-mobility recovery rate, road connectivity …