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Infrastructures
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Advances in Smart Infrastructures: Converging IoT, AI, and Digital Twins...
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Advances in Smart Infrastructures: Converging IoT, AI, and Digital Twins for Resilient Futures

A special issue of Infrastructures (ISSN 2412-3811). This special issue belongs to the section "Smart Infrastructures".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 2204

Special Issue Editors

Dr. Kinam Kim

E-Mail Website
Guest Editor
Department of Construction Management, University of Houston, Houston, TX 77004, USA
Interests: automation in construction; advanced data informatics in construction; human–robot collaboration; AR/VR applications in construction
Dr. Lu Gao

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77004, USA
Interests: big data in construction industry and asset management; artificial intelligence; machine learning; data science; natural language processing
Special Issues, Collections and Topics in MDPI journals
Dr. Somin Park

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76010, USA
Interests: construction automation; human–robot collaboration (HRC); human–robot interaction (HRI); virtual reality (VR); artificial intelligence (AI)
Dr. Zia Din

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77004, USA
Interests: construction safety; virtual and augmented reality; building information modeling (BIM); digital safety training
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancement of the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin technologies, the design, operation, and management of infrastructure systems have been transformed into more data-driven and resilient approaches. As infrastructure systems become more complex and the risk of climate change increases, the ability to create smart, adaptive, and resilient infrastructure systems has become a critical priority. These emerging technologies enable real-time monitoring, predictive analytics, and virtual replication of physical assets, offering unprecedented opportunities for enhancing resilience, sustainability, and efficiency.

This Special Issue aims to bring together high-quality research papers and comprehensive review articles that explore the latest advances at the intersection of IoT, AI, and Digital Twins for smart infrastructure. Contributions should highlight innovative methods, systems, and applications that strengthen resilience and sustainability in infrastructure while also addressing the challenges of scalability, interoperability, and long-term management.

I invite you to contribute to this Special Issue on “Advances in Smart Infrastructure: Converging IoT, AI, and Digital Twins for Resilient Futures”, for publication in MDPI’s journal Infrastructures. This Special Issue is both timely and impactful, including but not limited to the following emerging topics:

  • IoT-enabled infrastructure monitoring and management systems;
  • AI-driven predictive maintenance and decision support for infrastructure;
  • Digital Twins for lifecycle management, risk assessment, and resilience planning;
  • Integration of smart infrastructure with climate adaptation and mitigation strategies;
  • Cyber–physical systems for intelligent transportation, energy, and water networks;
  • Interoperability frameworks and standards for smart infrastructure technologies;
  • Case studies and real-world applications demonstrating resilient smart infrastructure.

We look forward to receiving your contributions.

Dr. Kinam Kim
Dr. Lu Gao
Dr. Somin Park
Dr. Zia Din
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IoT-enabled monitoring systems
  • data-driven decision support
  • artificial intelligence (AI) applications in infrastructure systems
  • digital twins for infrastructure management
  • adaptive infrastructure
  • cyber–physical system (CPS) in infrastructure systems
  • smart construction
  • multi-sensor fusion
  • augmented reality (AR) and virtual reality (VR)
  • case studies and practical applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 1661 KB  
Article
Design of a Quantitative Evaluation Framework for Highway Landscape Quality Based on Panoramic Image Segmentation
by Hanwen Zhang and Myun Kim
Infrastructures 2026, 11(4), 132; https://doi.org/10.3390/infrastructures11040132 - 8 Apr 2026
Viewed by 239
Abstract
Highway landscape quality is important for visual comfort, environmental coordination, and infrastructure management. However, conventional assessment methods rely heavily on manual inspection and qualitative judgment, which are subjective and inefficient for large-scale applications. To address this issue, this study proposes an AI-based quantitative [...] Read more.
Highway landscape quality is important for visual comfort, environmental coordination, and infrastructure management. However, conventional assessment methods rely heavily on manual inspection and qualitative judgment, which are subjective and inefficient for large-scale applications. To address this issue, this study proposes an AI-based quantitative evaluation framework for highway landscape quality using an improved Panoptic-DeepLab model for panoramic image segmentation. The model identifies major landscape elements in highway scenes, including vegetation, sky, roads, buildings, and billboards. Based on the segmentation results, the proportions of natural elements, spatial openness, and artificial interference are integrated into a landscape quality score (LQS) model for quantitative assessment. Experimental results demonstrate that the proposed method achieves reliable segmentation performance and stable convergence in complex highway environments. Comparative analysis further shows that the method provides competitive accuracy with good computational efficiency. The proposed framework offers an effective tool for highway landscape evaluation and can support highway planning, landscape optimization, and visual environment management. Full article
(This article belongs to the Special Issue Advances in Smart Infrastructures: Converging IoT, AI, and Digital Twins for Resilient Futures)
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25 pages, 2714 KB  
Article
From Prediction to Explanation: Explainable Machine Learning for Motor Vehicle–Involved Pedestrian and Cyclist Crash Risk
by Ahmed Elsayed, Ahmed Abdel-Rahim and Logan Prescott
Infrastructures 2026, 11(3), 77; https://doi.org/10.3390/infrastructures11030077 - 26 Feb 2026
Viewed by 553
Abstract
Pedestrian and cyclist safety at urban intersections remains a critical challenge for transportation agencies, as vulnerable road users are significantly exposed to crash risks in complex traffic environments. Identifying high-risk locations and factors that contribute to crashes is essential for improving road safety. [...] Read more.
Pedestrian and cyclist safety at urban intersections remains a critical challenge for transportation agencies, as vulnerable road users are significantly exposed to crash risks in complex traffic environments. Identifying high-risk locations and factors that contribute to crashes is essential for improving road safety. This study developed an explainable machine learning framework to predict motor vehicle-involved pedestrian and cyclist crash occurrence at urban intersections using five years of crash, geometric, operational, and socioeconomic data from a large set of urban intersections. Five supervised machine learning algorithms were trained and evaluated, including Binary Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest. The evaluated models demonstrated strong predictive performance overall, with accuracies approaching 91% and high discriminative capability. In particular, the Binary Logistic Regression and Random Forest models achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.961 and 0.964, respectively. To enhance transparency, SHAP values were used to quantify the contribution of predictors and examine feature effects at both the global and local levels. The results indicate that roadway hierarchy, intersection markings, and total entering volume are among the most influential determinants of crash likelihood, while socioeconomic variables exhibit weaker but interpretable effects. Full article
(This article belongs to the Special Issue Advances in Smart Infrastructures: Converging IoT, AI, and Digital Twins for Resilient Futures)
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19 pages, 4207 KB  
Article
Study on Distress Characteristics of Asphalt Pavement Under Heavy-Duty Traffic Based on Lightweight Road Inspection Equipment
by Hong Zhang, Yuanshuai Dong, Yun Hou, Xinlong Tong, Xiangjun Cheng and Keming Di
Infrastructures 2025, 10(11), 299; https://doi.org/10.3390/infrastructures10110299 - 7 Nov 2025
Viewed by 891
Abstract
This study, based on the maintenance engineering of regular national and provincial highways in Shanxi Province, aims to achieve refined maintenance of aging asphalt pavements under heavy-duty traffic conditions. Lightweight inspection equipment was used to perform frequent distress collection on the study sections, [...] Read more.
This study, based on the maintenance engineering of regular national and provincial highways in Shanxi Province, aims to achieve refined maintenance of aging asphalt pavements under heavy-duty traffic conditions. Lightweight inspection equipment was used to perform frequent distress collection on the study sections, and for the first time, the EPCI (Economic Pavement Surface Condition Index, which can quickly improve the overall condition level of the pavement by identifying simple two-dimensional diseases such as transverse and longitudinal joints and tortoise net cracks, and low-cost maintenance measures can be carried out through the detection data, which does not include diseases such as subsidence, which are more complex and costly.) is proposed to assess pavement distress conditions. The study conducted six high-frequency data collections over one year on the designated road sections. EPCI evaluations were carried out on each lane in different driving directions, summarizing eight types of pavement distress, including alligator cracking, block cracking, longitudinal and transverse cracking, potholes, longitudinal and transverse crack repairs, and block repairs. The development trends of EPCI and the distribution of pavement distress were analyzed. By comparing EPCI data, it was found that EPCI values in the driving lane fluctuated more stably than those in the overtaking and slow lanes, which was attributed to differences in maintenance intensity. The overall PCI data of the pavement during the COVID-19 pandemic showed that reduced maintenance activities are more conducive to analyzing the pavement’s deterioration patterns. By examining the distressed area in each lane over time, it was observed that the slow lane had the highest distribution of alligator and block cracking, while longitudinal and transverse cracking were most prevalent in the overtaking and driving lanes. Further analysis of the relationship between distressed area and EPCI suggests that controlling the distressed area to around 500 square meters per kilometer per lane can maintain the EPCI score at approximately 80. This level of maintenance is considered the most economical while ensuring satisfactory pavement performance. Full article
(This article belongs to the Special Issue Advances in Smart Infrastructures: Converging IoT, AI, and Digital Twins for Resilient Futures)
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