Chao Fan

Chao Fan
Clemson University | CU · Department of Civil Engineering

Doctor of Philosophy
Looking for Postdoc, PhD students, and grad/undergrad research assistants

About

52
Publications
9,181
Reads
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864
Citations
Additional affiliations
December 2020 - August 2022
Texas A&M University
Position
  • Postdoc Researcher
August 2017 - December 2020
Texas A&M University
Position
  • Research Assistant
January 2017 - June 2017
University of California, Davis
Position
  • Reader
Education
August 2017 - December 2020
Texas A&M University
Field of study
  • Civil Engineering
September 2016 - June 2017
University of California, Davis
Field of study
  • Civil Engineering
September 2012 - June 2016
China University of Mining and Technology
Field of study
  • Civil Engineering

Publications

Publications (52)
Article
Damages in critical infrastructure occur abruptly, and disruptions evolve with time dynamically. Understanding the situation of critical infrastructure disruptions is essential to effective disaster response and recovery of communities. Although the potential of social media data for situation awareness during disasters has been investigated in rec...
Article
This paper presents a vision for a Disaster City Digital Twin paradigm that can: (i) enable interdisciplinary convergence in the field of crisis informatics and information and communication technology (ICT) in disaster management; (ii) integrate artificial intelligence (AI) algorithms and approaches to improve situation assessment, decision making...
Article
Full-text available
In this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of floodwaters in urban road networks. A network of urban roads resilient to flooding events is essential for the provision of public services and for emergency response. The...
Article
Full-text available
While conceptual definitions have provided a foundation for measuring inequality of access and resilience in urban facilities, the challenge for researchers and practitioners alike has been to develop analytical support for urban system development that reduces inequality and improves resilience. Using 30 million large-scale anonymized smartphone-l...
Preprint
Ambient exposure to fine particulate matters of diameters smaller than 2.5{\mu}m (PM2.5) has been identified as one critical cause for respiratory disease. Disparities in exposure to PM2.5 among income groups at individual residences are known to exist and are easy to calculate. Existing approaches for exposure assessment, however, do not capture t...
Preprint
The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their spe...
Article
While several non-pharmacological measures have been implemented for a few months in an effort to slow the coronavirus disease (COVID-19) pandemic in the United States, the disease remains a danger in a number of counties as restrictions are lifted to revive the economy. Making a trade-off between economic recovery and infection control is a major...
Article
Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and...
Preprint
Full-text available
The objective of this study is to propose a system-level framework with quantitative measures to assess the resilience of road networks. The framework proposed in this paper can help transportation agencies incorporate resilience considerations into project development proactively and to understand the resilience performance of current road network...
Article
Full-text available
Twitter is an increasingly popular platform for understanding and analyzing human sociobehavioral dynamics, information diffusion, and public sentiment largely because of its high volume of content and relative ease of access. This has been particularly true in the field of crisis informatics, in which tweet content and social networks are utilized...
Conference Paper
Full-text available
This work aims to generalize demographic parity to continuous sensitive attributes while preserving tractable computation. Current fairness metrics for continuous sensitive attributes largely rely on intractable statistical independence between variables, such as Hirschfeld-Gebelein-Renyi (HGR) and mutual information. Statistical fairness metrics e...
Article
The objective of this paper is to examine practices for incorporating resilience by transportation agencies. This paper presents findings from statewide interviews and survey of personnel in transportation organizations throughout Texas. This study is focused on resilience planning and practices (not emergency management and evacuation planning). T...
Preprint
Full-text available
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i.e., message passing) behind GNNs inducing unfairness issue remains unknown. In this work, we theoretically and experimentally demonstrate that rep...
Article
The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes. Understanding and evaluating human mobility patterns, such as destination and traje...
Preprint
Full-text available
Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and...
Preprint
Full-text available
In this study, we propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas and their interactions captured by human mobility network to obtain vector representations of these areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobil...
Preprint
Full-text available
This study identifies the limitations and underlying characteristics of urban mobility networks that influence the performance of the gravity model. The gravity model is a widely-used approach for estimating and predicting population flows in urban mobility networks, assuming the scale-free property. Prior studies have reported good performance res...
Article
Embeddings of textual data containing location names (e.g., social media posts) have essential applications in various contexts such as marketing and disaster management. In these downstream implementations, social biases behind location names are highly prone to introduce unfair results through their embeddings; for example, emergent text messages...
Article
Full-text available
Deriving effective mobility control measures is critical for the control of COVID-19 spreading. In response to the COVID-19 pandemic, many countries and regions implemented travel restrictions and quarantines to reduce human mobility and thus reduce virus transmission. But since human mobility decreased heterogeneously, we lack empirical evidence o...
Article
Full-text available
The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infect...
Preprint
Full-text available
While several non-pharmacological measures have been implemented for a few months in an effort to slow the coronavirus disease (COVID-19) pandemic in the United States, the disease remains a danger in a number of counties as restrictions are lifted to revive the economy. Making a trade-off between economic recovery and infection control is a major...
Article
Extensive spread of situational information is important for communities in response to crises/ disasters. Among various mechanisms affecting the spread of information on social media, influential users play a critical role in enhancing information spread. This study examines the attributes and activities of local influential users as well as their...
Article
Full-text available
The objective of this study is to examine the transmission risk of COVID-19 based on cross-county population co-location data from Facebook. The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social d...
Article
Full-text available
The spread of pandemics such as COVID-19 is strongly linked to human activities. The objective of this article is to specify and examine early indicators of disease spread risk in cities during the initial stages of outbreak based on patterns of human activities obtained from digital trace data. In this study, the Venables distance (D v) and the ac...
Article
Full-text available
We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped census-block-group to point-of-interest (POI) mo...
Article
How populations use social media in disasters is essential for evaluating the representation of subpopulations while analyzing social media data for emergency response and disaster research. Existing machine learning models can extract, characterize and make sense of digital trace data from social media, but are unable to account for diversity in p...
Preprint
Full-text available
The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2,787 counties in the United States using a data-driven machine learning model. We trained random forest models using 23 features representing six key influencing factors affecting pand...
Article
Full-text available
Collection of information from crowdsourced and traditional sensing techniques during a disaster offers opportunities to exploit this new data source to enhance situational awareness, relief, and rescue coordination, and impact assessment. The evolution of disaster/crisis informatics affords the capability to process multi-modal data and to impleme...
Preprint
Full-text available
The spread of pandemics such as COVID-19 is strongly linked to human activities. The objective of this paper is to specify and examine early indicators of disease spread risk in cities during the initial stages of outbreak based on patterns of human activities obtained from digital trace data. In this study, the Venables distance (D_v), and the act...
Preprint
Full-text available
The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes. Understanding and predicting human mobility patterns, such as destination and traje...
Article
Full-text available
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the proposed model can both take in a large number of het...
Preprint
Full-text available
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the proposed model can both take in a large number of het...
Preprint
Full-text available
We examined the effect of social distancing on changes in visits to urban hotspot points of interest. Urban hotspots, such as central business districts, are gravity activity centers orchestrating movement and mobility patterns in cities. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can...
Article
Full-text available
Online social network has become a new form of infrastructure for communities in spreading situational information in disasters. Developing effective interventions to improve the network performance of information diffusion is essential for people to rapidly retrieve information in coping with disasters and subsequent disruptions. Existing studies...
Article
The objective of this study is to examine and quantify the relationships among sociodemographic factors, damage claims, and social media attention on areas during natural disasters. Social media has become an important communication channel for people to share and seek situational information to learn of risks, to cope with community disruptions, a...
Article
The objective of this paper is to investigate the role of different types of users in the diffusion of situational information through online social networks in disasters. In particular, this paper investigates the influence of two types of users: crowd (regular users) and hubs (users with a large number of followers) on the speed and magnitude of...
Preprint
Full-text available
The rapid spread of COVID-19 in the United States has imposed a major threat to public health, the real economy, and human well-being. With the absence of effective vaccines, the preventive actions of social distancing and travel reduction are recognized as essential non-pharmacologic approaches to control the spread of COVID-19. Prior studies demo...
Conference Paper
Full-text available
The objective of this study is to propose and test a methodological framework that integrates natural language processing and meta-network analysis to leverage social media data for situation assessment and action prioritization in disasters. Situation assessment based on evaluation of location-event-actor nexus (i.e., what event is occurring where...
Article
Social cohesion is an important determinant of community well-being, especially in times of distress such as disasters. This study investigates the phenomena of emergent social cohesion, which is characterized by abrupt, temporary and extensive social ties with the goal of sharing and receiving information regarding a particular event influencing a...
Article
This paper proposed and tested an integrated textual-visual-geo framework to enhance social sensing techniques in smart city digital twins in the context of disasters. Effective and efficient disaster response and recovery require reliable situational awareness regarding infrastructure disruptions and their societal impacts. Due to the rapid unfold...
Article
Full-text available
The objective of this study is to propose and test a hybrid machine learning pipeline to uncover the unfolding of disaster events corresponding to different locations from social media posts during disasters. Effective disaster response and recovery require a comprehensive understanding of disaster situations, i.e., unfolding of disaster events and...
Conference Paper
The objective of this study is to propose and test a theoretical framework which integrates the human sentiment reactions on social media in disasters into infrastructure resilience assessment. Infrastructure resilience assessment is important for reducing adverse consequences of infrastructure failures and promoting human well-being in natural dis...
Article
The objective of this paper is to create and test an in-tegrative framework for performance analysis of disaster management system-of-systems (DM-SoS). Effective disaster management systems and processes are critical for saving lives and protecting properties. However, the lack of integration between heterogeneous systems and entities involved in d...
Conference Paper
The objective of this study is to propose a seed-search algorithm and develop seeding strategies in online social networks for disseminating credible situational information regarding built environment disruptions in disasters. Rapid and extensive dissemination of credible situational information is important for disaster preparedness, response, an...
Conference Paper
The objective of this paper is to propose and test a graph-based approach for detection of critical infrastructure disruptions in social media data in disasters. Understanding the situation and disruptive events of critical infrastructure is essential to effective disaster response and recovery of communities. The potential of social media data for...
Conference Paper
The objective of this paper is to establish a meta-network framework to identify constituents in Disaster Management System-of-Systems (DM-SoS), conceptualize relationships and interactions among the constituents, and formulate quantitative measurements of DM-SoS performance for achieving network-centric operation and coordination in the context of...
Conference Paper
The objective of this paper is to propose a System-of-Systems (SoS) framework for disaster management systems and processes to better analyze, design and operate the heterogeneous, interconnected, and distributed systems involved in disasters. With increasing frequency and severity of disasters, improvement of efficiency and effectiveness of disast...
Article
In order to reveal the influence of coal gangue aggregate activity on the mechanical properties of the mortar, to promote coal gangue fine aggregate in concrete mortar application, this paper in view of the calcined coal gangue aggregate carried out ion dissolution test (ICP), construction of the coal gangue aggregate ion dissolution amount and its...

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