The special collection on Managing Infrastructure in a Data-Rich Era is available in the ASCE Library at https://ascelibrary.org/jmenea/infrastructure_data_rich_era.This special collection intends to establish the scholarly foundation for developing and managing infrastructure leveraging a wide range of cutting-edge data-driven theories, methodologies, and applications. Thirteen papers have been accepted for inclusion in this special collection and are grouped in four categories based on the phase of infrastructure life cycle to which they apply: project initiation and planning, project implementation and construction, operation and maintenance, and disruption management.Concerning project initiation and planning, Tong et al. (2021) proposed a gray relation analysis–least absolute shrinkage and selection operator (GRA-LASSO) hybrid method to establish a highway engineering budgetary estimate prediction model. The study aims to examine the rationality of estimated investment at the initial stage of construction and can contribute to the rapid auditing, forecasting, forewarning, monitoring, and management of highway engineering cost.With regard to project implementation and construction, Chen et al. (2021) developed a novel Bayesian Monte Carlo simulation model for construction schedule risk inference of infrastructures. Results of a case study demonstrated the reliability, convenience, and flexibility of the approach, and thus the study contributes to improving the accuracy in predicting risk occurrence probability. Xu et al. (2021b) developed a rule-based natural language processing (NLP) approach to extracting domain knowledge elements (DKEs) from Chinese text documents in the domain of construction safety management. The study can facilitate the establishment of knowledge-based safety management systems and guide safety training for construction safety management. Su et al. (2021) proposed a data-driven approach based on convolutional neural network (CNN) to recognize real-time fires in various construction environments. The approach attempts to solve the problem of construction fire safety management and contributes to guiding project teams in the timely detection of fires, improving safety management efficiency, and reducing fire-related losses. Xue et al. (2020) proposed a dynamic stakeholder-related modeling approach to identify, evaluate, and manage public concerns using data from large quantities of unstructured project documents. The dynamics of 16 critical public concerns in the projects are revealed and verified by real data, providing useful knowledge for future megainfrastructure projects in view of stakeholder management. Zhou et al. (2021) developed an analytical framework for the modeling analysis of public sentiment for megainfrastructure based on two dimensions of stage and region. The study contributes to public opinion analysis of megainfrastructures within the context of sufficient social media data, which provides new opportunities for data-driven infrastructure management and governance.Addressing the challenges of infrastructure operation and maintenance, Xu et al. (2021a) investigated the effectiveness of a virtual reality (VR)–integrated community-scale eco-feedback system for increasing public engagement toward energy saving and sustainable energy consumption behavior. Results demonstrated that displaying energy feedback that meets users’ diverse preferences can effectively encourage energy-saving behavior and contribute to meeting the sustainability targets set by city governments. Karimzadeh et al. (2021) developed a clustering method that incorporates a wide mixture of categorical and continuous contributors to pavement deterioration for the prediction of pavement conditions. Study results revealed an improvement in the accuracy of the condition predictions compared to past studies, thus contributing to efficient maintenance planning of pavements. Salem et al. (2021) attempted to map a process used by the California Department of Transportation to identify high collision concentration locations (HCCLs) across California. The findings helped identify opportunities to enhance, modify, and update priority safety programs to better meet the needs of state transportation agencies.For better disruption management, Chen et al. (2020) presented a social media–based approach to assessing disaster impacts on highways. The approach leverages the near-real-time and human-centered nature of social media information to achieve a timely and reliable assessment of the severity and location of disaster impacts on highways, thus contributing to the efficient planning of evacuations, emergency services, and recovery activities. Using publicly available big data, Pradhan and Arneson (2021) conducted a quantitative study to measure labor demand surge in highways, roads, and bridges construction following disasters, and then identify when such postdisaster surge in demand occurs. The study results have the potential to improve predisaster planning and postdisaster decision making. Zhou et al. (2020) proposed a synthetic approach that exploits media news to delineate the patterns of infrastructure failure interdependencies and stakeholders associated with infrastructure failures. The study complements the lack of empirical evidence in existing approaches and the research results provide valuable insights on effective emergency response. Rahimi-Golkhandan et al. (2021) assessed the impact of transportation diversity on mobility in urban communities after Hurricane Sandy using system-wide geographic information system (GIS) data, and reported that the distance, radius, and entropy of all individuals significantly decreased after the disaster. The results contribute to the mobility resilience literature by deepening the understanding of the underlying drivers of changes in human mobility following extreme events.On a final note, we would like to extend our gratitude to all the contributors to this special collection. We thank the authors for their effort and valuable knowledge manifested in the quality of the articles and appreciate the support and selfless involvement of all the guest editors and reviewers.References Karimzadeh, A., S. Sabeti, and O. Shoghli. 2021. “Optimal clustering of pavement segments using K-prototype algorithm in a high-dimensional mixed feature space.” J. Manage. Eng. 37 (4): 04021022. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000910. Xu, L., A. Francisco, J. E. Taylor, and N. Mohammadi. 2021a. “Urban energy data visualization and management: Evaluating community-scale eco-feedback approaches.” J. Manage. Eng. 37 (2): 04020111. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000879. Xu, N., L. Ma, L. Wang, Y. Deng, and G. Ni. 2021b. “Extracting domain knowledge elements of construction safety management: Rule-based approach using Chinese natural language processing.” J. Manage. Eng. 37 (2): 04021001. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000870. Xue, J., G. Q. Shen, Y. Li, J. Wang, and I. Zafar. 2020. “Dynamic stakeholder-associated topic modeling on public concerns in megainfrastructure projects: Case of Hong Kong–Zhuhai–Macao bridge.” J. Manage. Eng. 36 (6): 04020078. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000845. Zhou, S., S. T. Ng, Y. Yang, and J. F. Xu. 2020. “Delineating infrastructure failure interdependencies and associated stakeholders through news mining: The case of Hong Kong’s water pipe bursts.” J. Manage. Eng. 36 (5): 04020060. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000821.