Skip to main content

Call for Papers - Machine Learning for Infrastructure Durability and Resilience

Guest Editors:

Research Assistant Professor Yong Deng, PhD, Civil and Environmental Engineering, Washington State University, USA
Professor Qiao Dong, PhD, School of Transportation, Southeast University, China

Submission Status: Open   |   Submission Deadline: Closed


Journal of Infrastructure Preservation and Resilience is calling for submissions to our Collection on Machine learning for infrastructure durability and resilience. 

For infrastructure systems, durability refers to the ability to withstand anticipated service conditions including environmental stresses and traffic loads, while resilience relates to the capacity to recover from unexpected disturbances including natural disasters and disruptions caused by human activities. Durability and resilience are fundamental considerations throughout the stages of planning, design, construction, and management of infrastructure systems, with the goal of ensuring the sustained integrity, functionality, and operational performance.

Durability and resilience have significant importance to modern infrastructure systems, considering the variety of factors they influence including safety, economy, environment and society. Moreover, the escalating uncertainty and complexity in infrastructure systems and their operational environment pose additional challenges in conducting comprehensive analysis and achieving effective management of durability and resilience.

Machine learning has demonstrated exceptional capabilities in data analysis, system identification, and optimized decision-making processes, highlighting its substantial potential as a valuable tool for analyzing and enhancing infrastructure durability and resilience throughout the entire life cycle. It is capable of making infrastructure more sustainable and safer, in a smart manner. The purpose of this collection is to present the latest advancements in machine learning techniques that contribute to the evaluation, prediction, and improvement of durability and resilience in diverse infrastructure domains, including but not limited to bridges, buildings, roads, dams, and utility networks.

About the collection

This collection welcomes original research, case studies and reviews of machine learning applications in the following areas (but are not limited to):

• Interpretation and understanding of infrastructure durability and resilience
• Durability characterization and modeling of infrastructure materials and structures
• Health monitoring of infrastructure systems
• Predictive modelling of infrastructure deterioration and degradation
• Risk assessment and prediction for infrastructure systems
• Optimized design, maintenance and repair strategies for infrastructure systems
• Decision support systems for infrastructure resilience planning and management
• Climate change adaptation and natural disaster mitigation
• Life cycle assessment of infrastructure systems

  1. Accurate crack detection is crucial for maintaining pavement integrity, yet manual inspections remain labor-intensive and prone to errors, underscoring the need for automated solutions. This study proposes a n...

    Authors: R Rakshitha, S Srinath, N Vinay Kumar, S Rashmi and B V Poornima
    Citation: Journal of Infrastructure Preservation and Resilience 2024 5:11
    Content type: Research Published on:

  2. Structural elements undergo multiple levels of damage at various locations due to environments and critical loading conditions. The level of damage and its location can be predicted using acoustic emission (AE...

    Authors: Mohamed Barbosh, Liangfu Ge and Ayan Sadhu
    Citation: Journal of Infrastructure Preservation and Resilience 2024 5:10
    Content type: Research Published on:

  3. Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of t...

    Authors: Karthikeyan Loganathan, Mohammad Najafi, Sharareh Kermanshachi, Praveen Kumar Maduri and Apurva Pamidimukkala
    Citation: Journal of Infrastructure Preservation and Resilience 2024 5:9
    Content type: Research Published on:

  4. We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I &M) planning. Unlike other DRL algorithms ...

    Authors: Daniel Koutas, Elizabeth Bismut and Daniel Straub
    Citation: Journal of Infrastructure Preservation and Resilience 2024 5:6
    Content type: Research Published on:

  5. The operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will have a significant impact on freight mobility, ...

    Authors: Mohamed T. Elshazli, Dina Hussein, Ganapati Bhat, Ahmed Abdel-Rahim and Ahmed Ibrahim
    Citation: Journal of Infrastructure Preservation and Resilience 2024 5:5
    Content type: Research Published on:

  6. Retroreflectivity is the primary metric that controls the visibility of pavement markings during nighttime and in adverse weather conditions. Maintaining the minimum level of retroreflectivity as specified by ...

    Authors: Ipshit Ibne Idris, Momen Mousa and Marwa Hassan
    Citation: Journal of Infrastructure Preservation and Resilience 2024 5:3
    Content type: Research Published on:

Submission Guidelines

Back to top

This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system SNAPP. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Machine learning for infrastructure durability and resilience" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.