Application of Symmetry in Civil Infrastructure Asset Management

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 7372

Special Issue Editors


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Guest Editor
Risk-Informed Life Cycle Infrastructure Centre (RILCIE) Lab, Toronto Metropolitan University, Toronto, ON, Canada
Interests: multi-objective optimization; large-scale network-level; decision support system; infrastructure asset management; pavement management; bridge maintenance and rehabilitation; climate change mitigation and adaptation; degradation modeling; sustainability assessment; multi-objective

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Guest Editor
College of Civil Engineering and Architecture, Xinjiang University, Urumqi 830023, China
Interests: steel structure and composite structure; prefabricated building structure system

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Guest Editor
School of Transportation Engineering, Chang'an University, Xi'an 710064, China
Interests: asset management and other aspects of research

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Guest Editor
Department of Civil Engineering and Management, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands
Interests: operational research methods applied to life cycle sustainability and resilience assessment; combinatorial optimization; metaheuristics; multicriteria decision analysis (mcda); data mining and statistical modelling; climate change and extreme events impacts on the new and existing civil infrastructure systems
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Special Issue Information

Dear Colleagues,

Civil infrastructure asset management involves the systematic planning, maintenance, and optimization of physical assets to ensure their longevity and efficiency. Symmetry/asymmetry plays a crucial role in various aspects of infrastructure management, from structural health monitoring to deterioration modelling and maintenance optimization. The presence of symmetry in asset conditions, degradation patterns, and decision-making processes can significantly improve computational efficiency, enhance predictive modelling accuracy, and optimize resource allocation. Conversely, understanding asymmetry can help address uncertainties and variabilities in asset performance, leading to more robust management strategies.

The aim of this Special Issue is to explore the applications of symmetry/asymmetry in infrastructure asset management, which can be highly beneficial for engineering, data-driven decision-making, and policy optimization. For instance, symmetry-based modelling approaches can improve the efficiency of maintenance scheduling, while recognizing asymmetry in degradation patterns can lead to more adaptive and cost-effective asset management strategies.

We invite contributions (both research and review articles) that cover a broad range of topics related to symmetry/asymmetry in infrastructure asset management, including (but not limited to) the following areas:

  • Symmetry-based optimization in asset management decision-making;
  • Applications of symmetry in structural health monitoring and sensor placement;
  • Asymmetry in deterioration modelling and predictive maintenance strategies;
  • Leveraging symmetry/asymmetry to enhance probabilistic modelling and risk assessment;
  • Computational methods exploiting symmetry in infrastructure simulations;
  • Dynamic programming and reinforcement learning approaches utilizing symmetry in maintenance planning;
  • The role of symmetry in infrastructure network resilience and redundancy analysis.

This Special Issue aims to foster interdisciplinary research by bringing together experts from civil engineering, computational modelling, artificial intelligence, and infrastructure management to explore the theoretical and practical implications of symmetry in asset management. We look forward to receiving your contributions and advancing knowledge in this important field.

Dr. Wang Chen
Dr. Jianhong Han
Dr. Lin Chen
Dr. João Santos
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. Symmetry 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 2400 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

  • symmetry/asymmetry
  • infrastructure asset management
  • structural health monitoring
  • deterioration modelling
  • predictive maintenance
  • decision-making
  • probabilistic modelling
  • reinforcement learning
  • dynamic programming

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Published Papers (3 papers)

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Research

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32 pages, 24136 KB  
Article
A Study on the Deterioration of Atmospheric Conditions in Road Areas Based on the Equal-Pollution Model and Fluid Dynamics Simulations
by Chuan Lu, Lin Teng, Xueqi Wang, Chuanwei Du, Wenke Yan and Yan Wang
Symmetry 2025, 17(12), 2182; https://doi.org/10.3390/sym17122182 - 18 Dec 2025
Viewed by 590
Abstract
This study investigates the impact of roadside building development and vehicle exhaust emissions on atmospheric deterioration in urban highway areas. By integrating satellite-based building coverage data with an equal-pollution vehicle conversion method (based on human toxicity potential), we establish a computational fluid dynamics [...] Read more.
This study investigates the impact of roadside building development and vehicle exhaust emissions on atmospheric deterioration in urban highway areas. By integrating satellite-based building coverage data with an equal-pollution vehicle conversion method (based on human toxicity potential), we establish a computational fluid dynamics framework to simulate pollutant dispersion. Key results reveal the following: (1) Street canyon morphology, particularly its geometric symmetry, dominates diffusion patterns. Wide canyons (aspect ratio = 3.3) reduce CO accumulation by over 30% compared to deep canyons (aspect ratio = 0.3), highlighting the role of built form in regulating pollution distribution. (2) Under idealized conditions, photocatalytic pavement mitigates pollutant concentrations at human breathing height by 28.7–56.7%, demonstrating the potential of uniformly applied material solutions. These findings provide a validated theoretical basis for optimizing urban road design and evaluating environmental policies, with considerations for spatial layout and material treatment. Full article
(This article belongs to the Special Issue Application of Symmetry in Civil Infrastructure Asset Management)
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42 pages, 3902 KB  
Article
Uncovering Symmetric and Asymmetric Deterioration Patterns in Maryland’s Steel Bridges Through Time-Series Clustering and Principal Component Analysis
by Soroush Piri, Zeinab Bandpey, Mehdi Shokouhian and Ruel Sabellano
Symmetry 2025, 17(12), 2074; https://doi.org/10.3390/sym17122074 - 3 Dec 2025
Viewed by 908
Abstract
This study analyzes long-term deterioration patterns in 1378 Maryland steel bridges using annual Bridge Health Index (BHI) records from 1995–2021. Missing observations were addressed through linear interpolation combined with forward/backward filling, after which feature-wise z-score standardization was applied to ensure comparability across annual [...] Read more.
This study analyzes long-term deterioration patterns in 1378 Maryland steel bridges using annual Bridge Health Index (BHI) records from 1995–2021. Missing observations were addressed through linear interpolation combined with forward/backward filling, after which feature-wise z-score standardization was applied to ensure comparability across annual trajectories. Euclidean K-means clustering (k-means++ initialization, 10 restarts) was implemented to identify deterioration archetypes, with K = 6 selected using the elbow method and the silhouette coefficient. Cluster-internal stability was evaluated using bridge-level Root Mean Squared Error (RMSE), and uncertainty in median deterioration profiles was quantified using 2000-iteration percentile-based bootstrap confidence intervals. To interpret structural and contextual drivers within each group, Principal Component Analysis (PCA) was performed on screened and standardized geometric, structural, and traffic-related attributes. Results revealed strong imbalance in cluster membership (757, 503, 35, 33, 44, and 6 bridges), reflecting substantial diversity in long-term BHI behavior. Cluster-median RMSE values ranged from 2.69 to 22.66, while wide confidence bands in smaller clusters highlighted elevated uncertainty due to limited sample size. PCA indicated that span length, deck width, truck percentage, and projected future ADT were the most influential differentiators of deteriorating clusters, while stable clusters were distinguished by consistently high BHI component values and limited geometric complexity. Missing rehabilitation records prevented definitive attribution of U-shaped or recovering trajectories to specific intervention events. Overall, this study establishes a scalable, statistically supported framework for deterioration-trajectory profiling and provides actionable insight for proactive inspection scheduling, rehabilitation prioritization, and long-term asset management planning for state-level bridge networks. Full article
(This article belongs to the Special Issue Application of Symmetry in Civil Infrastructure Asset Management)
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Review

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50 pages, 1742 KB  
Review
A Review of Pavement Performance Deterioration Modeling: Influencing Factors and Techniques
by Benjamin G. Famewo and Mehdi Shokouhian
Symmetry 2025, 17(11), 1992; https://doi.org/10.3390/sym17111992 - 18 Nov 2025
Cited by 6 | Viewed by 5209
Abstract
Accurate modeling of pavement performance is vital to maintaining safe, reliable, and sustainable transportation infrastructure. This review synthesizes current approaches to pavement deterioration modeling, with emphasis on key influencing factors, performance indicators, and methodologies employed within Pavement Management Systems (PMS). Primary deterioration drivers, [...] Read more.
Accurate modeling of pavement performance is vital to maintaining safe, reliable, and sustainable transportation infrastructure. This review synthesizes current approaches to pavement deterioration modeling, with emphasis on key influencing factors, performance indicators, and methodologies employed within Pavement Management Systems (PMS). Primary deterioration drivers, including traffic loading and environmental stressors, are analyzed for their impact on degradation patterns. Performance indicators such as the Pavement Surface Evaluation and Rating (PASER), Pavement Condition Index (PCI), and International Roughness Index (IRI) are evaluated for their effectiveness in capturing pavement condition and guiding maintenance decisions. Modeling techniques are broadly categorized into deterministic, probabilistic, and intelligent (machine learning–based) frameworks to illustrate the evolution of predictive approaches. Across these approaches, the notion of symmetry can be interpreted as the balance and consistency achieved between model assumptions, input variables, and predicted pavement behavior, while asymmetry represents deviations caused by uncertainty, variability, and nonlinearity inherent in real-world conditions. Recognizing these symmetrical and asymmetrical relationships helps unify different modeling paradigms and provides insight into how each framework handles equilibrium between accuracy, complexity, and interpretability. The review also highlights persistent challenges in data availability, quality, and standardization. Notably, the increasing adoption of machine learning reflects its capacity to handle high-dimensional and spatiotemporal datasets. Recommendations are proposed to improve the robustness, scalability, and transparency of future deterioration models, thereby enhancing their role in data-driven, resilient, and cost-effective pavement management strategies. Full article
(This article belongs to the Special Issue Application of Symmetry in Civil Infrastructure Asset Management)
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