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Infrastructures
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Sustainable Bridge Engineering
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Sustainable Bridge Engineering

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 5594

Special Issue Editors

Dr. Xiaodong Song

E-Mail Website
Guest Editor
School of Transportation, Southeast University, No.2 Southeast University Road, Nanjing 211189, China
Interests: vibration and noise radiation from bridge and tunnel structures; intelligent operation and maintenance of bridge structures; BIM technology for bridge engineering
Prof. Dr. Qi Li

E-Mail Website
Guest Editor
Department of Bridge Engineering, School of Civil Engineering, Tongji University, Shanghai, China
Interests: vibration and noise control; intelligent monitoring and maintenance for rail transit structures
Dr. Chao Zou

E-Mail Website
Guest Editor
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, China
Interests: vibration prediction of building structure; vibration and noise evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable bridge engineering is a critical field that addresses the growing need for infrastructure that is not only resilient and efficient, but also environmentally responsible. Bridges, as vital components of transportation networks, must be designed, constructed, and maintained with a focus on minimizing environmental impact, enhancing durability, and ensuring safety. This Special Issue, entitled “Sustainable Bridge Engineering”, aims to explore innovative approaches, materials, and technologies that can contribute to the development of bridges that are sustainable, resilient, and adaptable to future challenges.

The scope of this Special Issue encompasses a wide range of topics related to sustainable practices in bridge engineering. We invite original research articles and reviews that highlight advancements in materials, design methodologies, construction techniques, and maintenance strategies. Key areas of interest include, but are not limited to, the following:

  • Sustainable materials: the utilization of recycled, renewable, and low-carbon materials to reduce environmental impact.
  • Innovative design and construction approaches: the integration of life-cycle assessment, modular construction, and adaptive design principles.
  • Intelligent monitoring and detection: IoT, AI, and sensor technology applications for bridge service performance assessments.
  • Resilience and durability: strategies to enhance the longevity and performance of bridges under various load conditions.
  • Extreme environment adaptation: design and retrofitting strategies to address the impacts of extreme weather events and rising environmental stresses.

This Special Issue seeks to provide a platform for researchers and practitioners to share their insights and innovations in sustainable bridge engineering. By fostering collaboration and knowledge exchange, we aim to contribute to the development of bridge infrastructure that meets the demands of modern transportation while preserving the environment for future generations.

We look forward to receiving your contributions.

Dr. Xiaodong Song
Prof. Dr. Qi Li
Dr. Chao Zou
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

  • sustainable bridge engineering
  • low-carbon materials
  • life-cycle assessment
  • adaptive design
  • resilient infrastructure
  • intelligent monitoring
  • extreme environments

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

Published Papers (5 papers)

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Research

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17 pages, 4116 KB  
Article
Degradation Mechanism, Performance Impact, and Maintenance Strategies for Expansion Devices of Large-Span Railway Bridges
by Yunchao Ye, Aiguo Yan, Pengcheng Yin, Jinbao Liang and Zhiqiang Zhu
Infrastructures 2026, 11(1), 30; https://doi.org/10.3390/infrastructures11010030 - 19 Jan 2026
Viewed by 308
Abstract
To ensure the coordinated interaction between the beam and track of large-span bridges and achieve smooth rail transition at beam joints, rail expansion regulators and beam-end expansion devices are essential at bridge ends. However, these devices are structurally fragile, making them a weak [...] Read more.
To ensure the coordinated interaction between the beam and track of large-span bridges and achieve smooth rail transition at beam joints, rail expansion regulators and beam-end expansion devices are essential at bridge ends. However, these devices are structurally fragile, making them a weak link in the seamless track system. This study selected a long-span railway bridge and its expansion devices as research objects, summarized typical in-service diseases of beam-end expansion devices (e.g., adjustable sleeper offset, sleeper skewing, and expansion device jamming), and constructed a train–track–bridge coupled model incorporating these devices. The model was used to analyze the structural performance and train operation safety under defective conditions. Based on the analysis findings, a maintenance evaluation method for the beam-end region was proposed. Criteria include adjustable sleeper offset, lateral displacement difference between adjacent beam-ends, horizontal rotation angle of adjacent beams, vertical rotation angle of beam-ends, and longitudinal expansion amount of beam-end expansion devices in order to address the corresponding issues and achieve sustainable maintenance and operation of bridge structures. Full article
(This article belongs to the Special Issue Sustainable Bridge Engineering)
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18 pages, 1145 KB  
Article
A Systematic Approach for Selection of Fit-for-Purpose Low-Carbon Concrete for Various Bridge Elements to Reduce the Net Embodied Carbon of a Bridge Project
by Harish Kumar Srivastava, Vanissorn Vimonsatit and Simon Martin Clark
Infrastructures 2025, 10(10), 274; https://doi.org/10.3390/infrastructures10100274 - 13 Oct 2025
Viewed by 1210
Abstract
Australia consumes approximately 29 million m3 of concrete each year with an estimated embodied carbon (EC) of 12 Mt CO2e. High consumption of concrete makes it critical for successful decarbonization to support the achievement of ‘Net Zero 2050’ objectives of [...] Read more.
Australia consumes approximately 29 million m3 of concrete each year with an estimated embodied carbon (EC) of 12 Mt CO2e. High consumption of concrete makes it critical for successful decarbonization to support the achievement of ‘Net Zero 2050’ objectives of the Australian construction industry. Portland cement (PC) constitutes only 12–15% of the concrete mix but is responsible for approximately 90% of concrete’s EC. This necessitates reducing the PC in concrete with supplementary cementitious materials (SCMs) or using alternative binders such as geopolymer concrete. Concrete mixes including a combination of PC and SCMs as a binder have lower embodied carbon (EC) than those with only PC and are termed as low-carbon concrete (LCC). SCM addition to a concrete mix not only reduces EC but also enhances its mechanical and durability properties. Fly ash (FA) and granulated ground blast furnace slag (GGBFS) are the most used SCMs in Australia. It is noted that other SCMs such as limestone, metakaolin or calcinated clay, Delithiated Beta Spodumene (DBS) or lithium slag, etc., are being trialed. This technical paper presents a methodology that enables selecting LCCs with various degrees of SCMs for various elements of bridge structure without compromising their functional performance. The proposed methodology includes controls that need to be applied during the design/selection process of LCC, from material quality control to concrete mix design to EC evaluation for every element of a bridge, to minimize the overall carbon footprint of a bridge. Typical properties of LCC with FA and GGBFS as binary and ternary blends are also included for preliminary design of a fit-for-purpose LCC. An example for a bridge located in the B2 exposure classification zone (exposed to both carbonation on chloride ingress deterioration mechanisms) has also been included to test the methodology, which demonstrates that EC of the bridge may be reduced by up to 53% by use of the proposed methodology. Full article
(This article belongs to the Special Issue Sustainable Bridge Engineering)
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25 pages, 3896 KB  
Article
Bridge Risk Index for Freight Corridor Resilience: A Non-Parametric Machine Learning and Threat Modeling Approach
by Raj Bridgelall
Infrastructures 2025, 10(10), 264; https://doi.org/10.3390/infrastructures10100264 - 7 Oct 2025
Viewed by 929
Abstract
Bridges are critical nodes in freight networks, yet limited funding prevents agencies from maintaining all structures in good condition. This creates the need for a transparent and scalable method to identify which bridges pose the greatest risk to supply chain continuity. This study [...] Read more.
Bridges are critical nodes in freight networks, yet limited funding prevents agencies from maintaining all structures in good condition. This creates the need for a transparent and scalable method to identify which bridges pose the greatest risk to supply chain continuity. This study develops a bridge risk index using the threat–vulnerability–consequence (TVC) framework and validates its components with machine learning. Threat is defined as per-lane average daily traffic, vulnerability as effective bridge age (epoch), and consequence as detour distance, with traffic also contributing to disruption magnitude. The methodology applies log transformation and normalization to construct an interpretable multiplicative index, then classifies risk using Jenks natural breaks. The results show that epoch dominates vulnerability, detour distance amplifies consequence, and their interaction explains most of the risk variation. Specifically, effective age explains over three times more variation in bridge condition than any other attribute. The vulnerability–consequence interaction dominates with an R2 = 0.98. The highest-risk bridges are concentrated in rural areas and near major freight gateways where detour options are limited. The proposed TVC index provides a transparent, data-driven decision-support tool that agencies can apply nationwide to prioritize investments, safeguard freight corridors, and strengthen supply chain resilience. Full article
(This article belongs to the Special Issue Sustainable Bridge Engineering)
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17 pages, 5462 KB  
Article
Degradation and Sustainability: Analysis of Structural Issues in the Eduardo Caldeira Bridge, Machico
by Raul Alves, Sérgio Lousada, José Manuel Naranjo Gómez and José Cabezas
Infrastructures 2025, 10(9), 224; https://doi.org/10.3390/infrastructures10090224 - 22 Aug 2025
Viewed by 1557
Abstract
This paper presents a detailed analysis of the severe structural anomalies that led to the urgent rehabilitation of the Eduardo Caldeira Bridge in Machico, Madeira. Situated in a challenging coastal environment with complex volcanic geology, the bridge exhibited a critical failure of its [...] Read more.
This paper presents a detailed analysis of the severe structural anomalies that led to the urgent rehabilitation of the Eduardo Caldeira Bridge in Machico, Madeira. Situated in a challenging coastal environment with complex volcanic geology, the bridge exhibited a critical failure of its bearing devices, which were assigned the highest defect severity rating (Grade 5). A multidisciplinary diagnostic methodology, combining visual inspection data, non-destructive testing, and geotechnical analysis, was employed to identify the root causes of this degradation. The investigation concluded that the bearing failure was not due to widespread material deterioration but was directly linked to significant lateral structural displacements, exacerbated by localized geotechnical instabilities. This paper details the data-driven rehabilitation strategy that was subsequently implemented, including the complete replacement of the bearings and substructure stabilization measures. The study provides a valuable case study of a complex, mechanics-driven failure mode and demonstrates that for such critical infrastructure, a proactive management model integrating advanced technologies like Structural Health Monitoring (SHM) and Building Information Modelling (BIM) is essential for ensuring long-term safety and resilience. Full article
(This article belongs to the Special Issue Sustainable Bridge Engineering)
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Review

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22 pages, 5533 KB  
Review
The Fusion Mechanism and Prospective Application of Physics-Informed Machine Learning in Bridge Lifecycle Health Monitoring
by Jiaren Sun, Jiangjiang He, Guangbing Zhou, Jun Yang, Xiaoli Sun and Shuai Teng
Infrastructures 2026, 11(1), 16; https://doi.org/10.3390/infrastructures11010016 - 8 Jan 2026
Viewed by 676
Abstract
Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine learning methods are limited by data scarcity and physical inconsistency. [...] Read more.
Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine learning methods are limited by data scarcity and physical inconsistency. Physics-informed machine learning, as an emerging “gray box” paradigm, effectively integrates the advantages of both by embedding physical laws (such as control equations) into machine learning models in the form of constraints, priors, or residuals. This article systematically elaborates on the core fusion mechanism of physics-informed machine learning (PIML) in bridge engineering, innovative applications throughout the entire lifecycle of design, construction, operation, and maintenance, as well as its unique data augmentation strategy. Research has shown that PIML can significantly improve the accuracy and robustness of damage identification, load inversion, and performance prediction, and is the core engine for constructing dynamic and predictive digital twin systems. Despite facing challenges in complex physical modeling, loss function balancing, and engineering interpretability, PIML represents a fundamental shift in bridge health monitoring towards intelligent and predictive maintenance by combining advanced strategies such as active learning and meta learning with IoT technology. Full article
(This article belongs to the Special Issue Sustainable Bridge Engineering)
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