A5082373340- Infrastructure Resilience and Vulnerability Analysis
- Facility Location and Emergency Management
- Smart Grid Security and Resilience
- Supply Chain Resilience and Risk Management
- Risk and Safety Analysis
- Evacuation and Crowd Dynamics
- Network Security and Intrusion Detection
- Disaster Management and Resilience
- Complex Network Analysis Techniques
- Sustainable Supply Chain Management
- Economic and Environmental Valuation
- Distributed Sensor Networks and Detection Algorithms
- Probabilistic and Robust Engineering Design
- COVID-19 epidemiological studies
- Advanced Queuing Theory Analysis
- Quality and Supply Management
- Oil and Gas Production Techniques
- Complex Systems and Decision Making
- Transportation Planning and Optimization
- Misinformation and Its Impacts
- Hydropower, Displacement, Environmental Impact
- Occupational Health and Safety Research
- COVID-19 Pandemic Impacts
- Efficiency Analysis Using DEA
- Climate Change, Adaptation, Migration
University of Oklahoma
2017-2025
Louisiana State University
2024
Rice University
2014-2022
University of Chieti-Pescara
2021
Universidad de Los Andes
2014-2017
Universidad de Valladolid
2015
Abstract This study introduces the Interdependent Network Design Problem (INDP), concerned with defining minimum‐cost reconstruction strategy of a partially destroyed system infrastructure networks, subject to budget, resources, and operational constraints, while considering interdependencies between them. To solve INDP, authors develop an efficient Mixed Integer Programming (MIP) model, which considers different types interdependency exploiting efficiencies from joint restoration due...
Abstract Coastal communities are increasingly vulnerable to hurricanes, which cause billions of dollars in damage annually through wind, storm surge, and flooding. Mitigation efforts essential reduce these impacts but face significant challenges, including uncertainties hazard prediction, estimation, recovery costs. Resource constraints the disproportionate burden borne by socioeconomically groups further complicate retrofitting strategies. This study presents a probabilistic methodology...
Abstract Infrastructure systems are critical for society's resilience, government operation, and overall defense. Thereby, it is imperative to develop informative computationally efficient analysis methods infrastructure systems, which reveal system vulnerabilities recoverability. To capture practical constraints in analyses, various layers of complexity play a role, including limited element capacities, restoration resources, the presence interdependence among systems. High‐fidelity...
Abstract Recovery of interdependent infrastructure networks in the presence catastrophic failure is crucial to economy and welfare society. Recently, centralized methods have been developed address optimal resource allocation postdisaster recovery scenarios systems that minimize total cost. In real‐world systems, however, multiple independent, possibly noncooperative, utility network controllers are responsible for making decisions, resulting suboptimal decentralized processes. With goal...
Abstract Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships the natural societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tend be address broader policy issues management. We propose an optimization‐based framework for resilience analysis that incorporates organizational socioeconomic aspects into problems, allowing understand...
Restoring operation of critical infrastructure systems after catastrophic events is an important issue, inspiring work in multiple fields, including network science, civil engineering, and operations research. We consider the problem finding optimal order repairing elements power grids similar infrastructure. Most existing methods either only system structure, potentially ignoring features, or incorporate component level details leading to complex optimization problems with limited...
Disruptions in multimodal transportation networks can lead to significant damage and loss, affecting not only the networks’ efficiency but also their sustainability. Given size, dynamics, complex nature of these networks, it is essential understand enhance resilience against disruptions. This ensures functionality performance supports sustainable development by maintaining equitable service across various communities economic sectors. Therefore, developing efficient techniques increase...
Summary Machine learning (ML) has become a robust method for modeling field operations based on measurements. For example, wellbore cleanout is critical operation that needs to be optimized enhance the removal of solids reduce problems associated with poor hole cleaning. However, as geometry becomes more complicated, predicting cleaning performance fluids challenging. As result, optimization often difficult. Therefore, this research focuses developing data-driven model in deviated wells...