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Artificial Intelligence (AI)-Driven Full Lifecycle Management of Infrastructures: From Advanced Cementitious Materials to Durable Structures

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 3002

Special Issue Editors

Dr. Peng Wang

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Interests: high-performance fiber-reinforced concrete (UHPFRC); sustainable and green concrete; durability of fiber-reinforced polymer (FRP) and seawater sea-sand concrete (SSC); static and seismic performance of high-speed railway bridges (HRBs)
Special Issues, Collections and Topics in MDPI journals
Dr. Zhangliang Hu

E-Mail Website
Guest Editor
College of Civil Engineering, Hefei University of Technology, Heifei, China
Interests: earthquake engineering; technology of isolation for bridges; seismic vulnerability and risk assessment of bridges
Dr. Nailiang Xiang

E-Mail Website
Guest Editor
School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Interests: bridge engineering; earthquake engineering; seismic design and retrofit of bridges; seismic resilience; smart materials and their engineering application
Special Issues, Collections and Topics in MDPI journals
Dr. Chenguang Wang

E-Mail Website
Guest Editor Assistant
College of Civil Engineering, Lanzhou Jiaotong University, Lanzhou, China
Interests: analysis of spatial effects and finite element simulation of thin-walled box girders; high-toughness cementitious materials
Dr. Dongliang Meng

E-Mail Website
Guest Editor Assistant
School of Civil Engineering, Central South University, Changsha, China
Interests: seismic isolation and reduction; impact dynamics
Dr. Bo Wu

E-Mail Website
Guest Editor Assistant
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong
Interests: MgO-based binder; cement carbonation; self-healing; FRCC; fiber–matrix interface
Dr. Tianyu Wang

E-Mail Website
Guest Editor Assistant
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong
Interests: sustainability; pipeline; rehabilitation; biochar; CO2 sequestration
Dr. Shangtao Hu

E-Mail Website
Guest Editor Assistant
Earthquake Engineering Research and Test Center, Guangzhou University, Guangzhou, China
Interests: seismic isolation and reduction; vibration control

Special Issue Information

Dear Colleagues,

In order to alleviate the resource crisis and accelerate the realization of carbon neutrality, advanced cementitious materials for durable structures have been applied in infrastructure. Driven by the tremendous progress of artificial intelligence (AI), construction is gradually transitioning from traditional manufacturing to intelligent manufacturing. However, how to organically integrate AI with the entire chain of "design–construction–operation–maintenance" to achieve intelligent construction of the entire lifecycle of infrastructure needs further exploration.

This Special Issue aims to explore how to achieve intelligent infrastructure construction based on AI through advanced cementitious materials for long-lifespan structures. Research that investigates advanced cementitious materials, structures, AI algorithms, machine learning, and corresponding digital twin technologies is welcome. Literature reviews and state-of-the-art articles are highly appreciated.

The subtopics for submissions include, but are not limited to, the following:

  1. Intelligent construction and operation of infrastructure.
  2. Structural intelligence health inspection/monitoring and evaluation.
  3. Dual carbon and intelligent, healthy infrastructures.
  4. High-performance new cementitious materials.
  5. Key technologies and applications, such as BIM and digital twins.
  6. Structural intelligence for disaster prevention and mitigation.

Dr. Peng Wang
Dr. Zhangliang Hu
Dr. Nailiang Xiang
Guest Editors

Dr. Chenguang Wang
Dr. Dongliang Meng
Dr. Bo Wu
Dr. Tianyu Wang
Dr. Shangtao Hu
Guest Editor Assistants

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Materials is an international peer-reviewed open access semimonthly 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 2600 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

  • AI-driven management
  • intelligent analysis
  • machine learning
  • digital twin
  • low-carbon cement materials
  • low-carbon, sustainable, and durable concrete structures

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Published Papers (4 papers)

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Research

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17 pages, 820 KB  
Article
AI-Driven Optimization of Cu2O Modified Bitumen: A Multi-Scale Evaluation of Rheological, Aging, and Moisture Susceptibility Performance
by Sebnem Karahancer
Materials 2025, 18(17), 4201; https://doi.org/10.3390/ma18174201 - 8 Sep 2025
Viewed by 567
Abstract
This study explores the integration of copper oxide (Cu2O) into bitumen and leverages Artificial Intelligence (AI) to evaluate and optimize the binder’s performance across multiple scales. Comprehensive laboratory tests, including conventional binder properties, rheological analysis, aging simulations, low-temperature cracking, and moisture [...] Read more.
This study explores the integration of copper oxide (Cu2O) into bitumen and leverages Artificial Intelligence (AI) to evaluate and optimize the binder’s performance across multiple scales. Comprehensive laboratory tests, including conventional binder properties, rheological analysis, aging simulations, low-temperature cracking, and moisture susceptibility, were conducted on base and Cu2O modified asphalt binders. The results were used to train predictive models using gradient boosting regressors for each performance category. Optimization identified ideal Cu2O ratios for different engineering goals, offering practical recommendations. Based on this integrated cost-performance analysis, a Cu2O concentration of 2.3% was recommended as the most efficient trade-off point. AI modeling using Gradient Boosting Regressor (GBR) achieved high predictive performance, with R2 values reaching 0.98 for BBR prediction and 0.78 for rheology, and mean absolute error (MAE) values as low as 4.21. This demonstrates the model’s robustness in capturing complex nonlinear binder behaviors. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Driven Full Lifecycle Management of Infrastructures: From Advanced Cementitious Materials to Durable Structures)
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21 pages, 5609 KB  
Article
Carbonation and Corrosion Durability Assessment of Reinforced Concrete Beam in Heavy-Haul Railways by Multi-Physics Coupling-Based Analytical Method
by Wu-Tong Yan, Lei Yuan, Yong-Hua Su, Long-Biao Yan and Zi-Wei Song
Materials 2025, 18(15), 3622; https://doi.org/10.3390/ma18153622 - 1 Aug 2025
Viewed by 466
Abstract
The operation of heavy-haul railway trains with large loads results in significant cracking issues in reinforced concrete beams. Atmospheric carbon dioxide, oxygen, and moisture from the atmosphere penetrate into the beam interior through these cracks, accelerating the carbonation of the concrete and the [...] Read more.
The operation of heavy-haul railway trains with large loads results in significant cracking issues in reinforced concrete beams. Atmospheric carbon dioxide, oxygen, and moisture from the atmosphere penetrate into the beam interior through these cracks, accelerating the carbonation of the concrete and the corrosion of the steel bars. The rust-induced expansion of steel bars further exacerbates the cracking of the beam. The interaction between environmental factors and beam cracks leads to a rapid decline in the durability of the beam. To address this issue, a multi-physics field coupling durability assessment method was proposed, considering concrete beam cracking, concrete carbonation, and steel bar corrosion. The interaction among these three factors is achieved through sequential coupling, using crack width, carbonation passivation time, and steel bar corrosion rate as interaction parameters. Using this method, the deterioration morphology and stiffness degradation laws of 8 m reinforced concrete beams under different load conditions, including those of heavy and light trains in heavy-haul railways, are compared and assessed. The analysis reveals that within a 100-year service cycle, the maximum relative stiffness reduction for beams on the heavy train line is 20.0%, whereas for the light train line, it is only 7.4%. The degree of structural stiffness degradation is closely related to operational load levels, and beam cracking plays a critical role in this difference. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Driven Full Lifecycle Management of Infrastructures: From Advanced Cementitious Materials to Durable Structures)
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20 pages, 1666 KB  
Article
Optimized Design of Low-Carbon Fly Ash–Slag Composite Concrete Considering Carbonation Durability and CO2 Concentration Rising Impacts
by Kang-Jia Wang, Seung-Jun Kwon and Xiao-Yong Wang
Materials 2025, 18(14), 3418; https://doi.org/10.3390/ma18143418 - 21 Jul 2025
Viewed by 510
Abstract
Fly ash and slag are widely used as mineral admixtures to partially replace cement in low-carbon concrete. However, such composite concretes often exhibit a greater carbonation depth than plain Portland concrete with the same 28-day strength, increasing the risk of steel reinforcement corrosion. [...] Read more.
Fly ash and slag are widely used as mineral admixtures to partially replace cement in low-carbon concrete. However, such composite concretes often exhibit a greater carbonation depth than plain Portland concrete with the same 28-day strength, increasing the risk of steel reinforcement corrosion. Previous mix design methods have overlooked this issue. This study proposes an optimized design method for fly ash–slag composite concrete, considering carbonation exposure classes and CO2 concentrations. Four exposure classes are addressed—XC1 (completely dry or permanently wet environments such as indoor floors or submerged concrete), XC2 (wet but rarely dry, e.g., inside water tanks), XC3 (moderate humidity, e.g., sheltered outdoor environments), and XC4 (cyclic wet and dry, e.g., bridge decks and exterior walls exposed to rain). Two CO2 levels—0.04% (ambient) and 0.05% (elevated)—were also considered. In Scenario 1 (no durability constraint), the optimized designs for all exposure classes were identical, with 60% slag and 75% total fly ash–slag replacement. In Scenario 2 (0.04% CO2 with durability), the designs for XC1 and XC2 remained the same, but for XC3 and XC4, the carbonation depth became the controlling factor, requiring a higher binder content and leading to compressive strengths exceeding the target. In Scenario 3 (0.05% CO2), despite the increased carbonation depth, the XC1 and XC2 designs were unchanged. However, XC3 and XC4 required further increases in binder content and actual strength to meet durability limits. Overall, compressive strength governs the design for XC1 and XC2, while carbonation durability is critical for XC3 and XC4. Increasing the water-to-binder ratio reduces strength, while higher-strength mixes emit more CO2 per cubic meter, confirming the proposed method’s engineering validity. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Driven Full Lifecycle Management of Infrastructures: From Advanced Cementitious Materials to Durable Structures)
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Review

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36 pages, 3548 KB  
Review
Integrating Life-Cycle Assessment (LCA) and Artificial Neural Networks (ANNs) for Optimizing the Inclusion of Supplementary Cementitious Materials (SCMs) in Eco-Friendly Cementitious Composites: A Literature Review
by A. Arvizu-Montes, Oswaldo Guerrero-Bustamante, Rodrigo Polo-Mendoza and M.J. Martinez-Echevarria
Materials 2025, 18(18), 4307; https://doi.org/10.3390/ma18184307 - 14 Sep 2025
Viewed by 674
Abstract
The construction industry is a major contributor to global environmental impacts, particularly through the production and use of cement-based materials. In response to this challenge, this study provides a comprehensive synthesis of recent advances in the integration of Life-Cycle Assessment (LCA) and Artificial [...] Read more.
The construction industry is a major contributor to global environmental impacts, particularly through the production and use of cement-based materials. In response to this challenge, this study provides a comprehensive synthesis of recent advances in the integration of Life-Cycle Assessment (LCA) and Artificial Neural Networks (ANNs) for optimizing cementitious composites containing Supplementary Cementitious Materials (SCMs). A total of 14 case studies specifically addressing this topic were identified, reviewed, and analyzed, spanning various binder compositions, ANN architectures, and LCA frameworks. The findings highlight how hybrid ANN–LCA systems can accurately predict mechanical performance while minimizing environmental burdens, supporting the formulation of low-carbon, high-performance cementitious composites. The diverse SCMs explored, including fly ash, slag, silica fume, waste glass powder, and rice husk ash, demonstrate significant potential for reducing CO2 emissions, energy consumption, and raw material depletion. Furthermore, the systematic comparative matrix developed in this work offers a valuable reference for researchers and practitioners aiming to implement intelligent, eco-efficient mix designs. Overall, this study contributes to advancing digital sustainability tools and reinforces the viability of ANN–LCA integration as a scalable decision-support framework for green construction practices. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Driven Full Lifecycle Management of Infrastructures: From Advanced Cementitious Materials to Durable Structures)
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