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CivilEng
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Machine Learning and AI-Driven Innovations in Concrete Technology and Construction...
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Machine Learning and AI-Driven Innovations in Concrete Technology and Construction Materials

A special issue of CivilEng (ISSN 2673-4109). This special issue belongs to the section "Construction and Material Engineering".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2177

Special Issue Editor

Dr. Radu Muntean

E-Mail Website
Guest Editor
Department of Civil Engineering, Transilvania University of Brasov, 500036 Brasov, Romania
Interests: sustainable building materials and technologies; management; timber and concrete structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of machine learning (ML) and artificial intelligence (AI) is transforming the field of civil engineering, especially in terms of concrete technology and construction materials. These technologies offer innovative solutions addressing long-standing challenges related to sustainability, durability, cost-effectiveness, and performance optimization. By leveraging big data, smart sensors, and predictive modeling, AI-driven approaches enable researchers and practitioners to optimize concrete mix designs, forecast structural behavior, and ensure long-term material resilience under diverse environmental and loading conditions.

This Special Issue, entitled “Machine Learning and AI-Driven Innovations in Concrete Technology and Construction Materials”, seeks to gather original research, case studies, and reviews that showcase how AI and ML can enhance civil engineering practices. It emphasizes both theoretical developments and practical applications, providing a comprehensive overview of the digital revolution shaping the future of sustainable construction.

Areas of interest include, but are not limited to, the following topics:

  • AI-based optimization of concrete mix design and performance prediction;
  • Application of ML in strength, durability, and sustainability assessment of construction materials;
  • Real-time structural health monitoring using AI, IoT, and smart sensors;
  • Digital twins for construction materials and structural systems;
  • Big data analytics in material science and construction engineering;
  • Predictive modeling of life-cycle performance and sustainability;
  • Integration of ML/AI with nanotechnology and advanced material design;
  • AI-assisted quality control and defect detection in manufacturing processes;
  • Automated construction processes and robotics in civil engineering;
  • Case studies on AI-driven approaches in sustainable buildings and infrastructure;
  • Policy, standardization, and education challenges in adopting AI in construction.

By presenting interdisciplinary research from around the globe, this Special Issue aims to highlight the pivotal role of digital intelligence in advancing civil engineering materials and practices, bridging the gap between innovation, sustainability, and real-world implementation.

Dr. Radu Muntean
Guest Editor

Moutaman M. Abbas
Guest Editor Assistant
Email: moutaman.abbas@unitbv.ro
Affiliation: Department of Civil Engineering, Transilvania University of Brasov, 500036 Brasov, Romania
Website: https://scholar.google.com/citations?user=rtbOhwwAAAAJ&hl=en&oi=ao
Interests: sustainability; supplementary cementitious materials; concrete; AI-driven concrete technology

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. CivilEng is an international peer-reviewed open access quarterly 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 1400 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

  • machine learning in concrete technology
  • artificial intelligence in construction materials
  • sustainable building materials
  • durability and life-cycle performance
  • digital twins and smart sensors
  • structural health monitoring
  • big data analytics in civil engineering
  • mix design optimization
  • predictive modeling
  • AI-driven sustainability

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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

20 pages, 1390 KB  
Article
Machine Learning-Based Compressive Strength Prediction in Pervious Concrete
by Hamed Abdul Baseer and G. G. Md. Nawaz Ali
CivilEng 2026, 7(1), 3; https://doi.org/10.3390/civileng7010003 - 31 Dec 2025
Viewed by 431
Abstract
The construction industry significantly contributes to global sustainability challenges, producing 30–40 percent of global carbon dioxide emissions and consuming large amounts of natural resources. Pervious concrete has emerged as a sustainable alternative to conventional pavements due to its ability to promote stormwater infiltration [...] Read more.
The construction industry significantly contributes to global sustainability challenges, producing 30–40 percent of global carbon dioxide emissions and consuming large amounts of natural resources. Pervious concrete has emerged as a sustainable alternative to conventional pavements due to its ability to promote stormwater infiltration and groundwater recharge. However, the absence of fine aggregates creates a highly porous structure that results in reduced compressive strength, limiting its broader structural use. Determining compressive strength traditionally requires destructive laboratory testing of concrete specimens, which demands considerable material, energy, and curing time, often up to 28 days—before results can be obtained. This makes iterative mix design and optimization both slow and resource intensive. To address this practical limitation, this study applies Machine Learning (ML) as a rapid, preliminary estimation tool capable of providing early predictions of compressive strength based on mix composition and curing parameters. Rather than replacing laboratory testing, the developed ML models serve as supportive decision-making tools, enabling engineers to assess potential strength outcomes before casting and curing physical specimens. This can reduce the number of trial batches produced, lower material consumption, and minimize the environmental footprint associated with repeated destructive testing. Multiple ML algorithms were trained and evaluated using data from existing literature and validated through laboratory testing. The results indicate that ML can provide reliable preliminary strength estimates, offering a faster and more resource-efficient approach to guiding mix design adjustments. By reducing the reliance on repeated 28-day test cycles, the integration of ML into previous concrete research supports more sustainable, cost-effective, and time-efficient material development practices. Full article
(This article belongs to the Special Issue Machine Learning and AI-Driven Innovations in Concrete Technology and Construction Materials)
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37 pages, 4063 KB  
Article
Data-Driven Optimization of Sustainable Asphalt Overlays Using Machine Learning and Life-Cycle Cost Evaluation
by Ghazi Jalal Kashesh, Hasan H. Joni, Anmar Dulaimi, Abbas Jalal Kaishesh, Adnan Adhab K. Al-Saeedi, Tiago Pinto Ribeiro and Luís Filipe Almeida Bernardo
CivilEng 2026, 7(1), 1; https://doi.org/10.3390/civileng7010001 - 26 Dec 2025
Viewed by 369
Abstract
The growing demand for sustainable pavement materials has driven increased interest in asphalt mixtures incorporating recycled crumb rubber (CR). While CR modification enhances mechanical performance and durability, its often increases initial production costs and energy demand. This study develops an integrated framework that [...] Read more.
The growing demand for sustainable pavement materials has driven increased interest in asphalt mixtures incorporating recycled crumb rubber (CR). While CR modification enhances mechanical performance and durability, its often increases initial production costs and energy demand. This study develops an integrated framework that combines machine learning (ML) and economic analysis to identify the optimal balance between performance and cost in CR-modified asphalt overlay mixtures. An experimental dataset of conventional and CR-modified mixtures was used to train and validate multiple ML algorithms, including Random Forest (RF), Gradient Boosting (GB), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR). The RF and ANN models exhibited superior predictive accuracy (R2 > 0.98) for key performance indicators such as Marshall stability, tensile strength ratio, rutting resistance, and resilient modulus. A Cost–Performance Index (CPI) integrating life-cycle cost analysis was developed to quantify trade-offs between performance and economic efficiency. Environmental life-cycle assessment indicated net greenhouse gas reductions of approximately 96 kg CO2-eq per ton of mixture despite higher production-phase emissions. Optimization results indicated that a CR content of approximately 15% and an asphalt binder content of 4.8–5.0% achieve the best performance–cost balance. The study demonstrates that ML-driven optimization provides a powerful, data-based approach for guiding sustainable pavement design and promoting the circular economy in road construction. Full article
(This article belongs to the Special Issue Machine Learning and AI-Driven Innovations in Concrete Technology and Construction Materials)
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Graphical abstract

20 pages, 3630 KB  
Article
Hybrid Topology Optimization of a Concrete Structure via Finite Element Analysis and Deep Learning Surrogates
by Mohamed Gindy, Moutaman M. Abbas, Radu Muntean and Silviu Butnariu
CivilEng 2025, 6(4), 68; https://doi.org/10.3390/civileng6040068 - 9 Dec 2025
Viewed by 668
Abstract
The cement industry significantly contributes to global CO2 emissions, making material efficiency in concrete structures a crucial sustainability goal. This study addresses the challenge of excessive cement usage in traditional concrete design by optimizing a cast-in-place concrete bench. A density-based topology optimization [...] Read more.
The cement industry significantly contributes to global CO2 emissions, making material efficiency in concrete structures a crucial sustainability goal. This study addresses the challenge of excessive cement usage in traditional concrete design by optimizing a cast-in-place concrete bench. A density-based topology optimization framework was implemented in ANSYS Mechanical and enhanced with a deep-learning surrogate model to accelerate computational performance. The optimization aimed to minimize the structural mass while satisfying serviceability and strength constraints, including limits on displacement and compressive stress under realistic public-use loading conditions. The topology optimization converged after 62 iterations, achieving a 46% reduction in mass (from 258.3 kg to 139.4 kg) while maintaining a maximum deflection below 2 mm and a maximum compressive stress of 15.5 MPa, within the allowable limit for C20/25 concrete. The deep-learning surrogate model achieved strong predictive accuracy (IoU = 0.75, Dice = 0.73) and reduced computation time by over 105× compared to the full finite element optimization. The optimized geometry was reconstructed and rendered using Blender for visualization. These results highlight the potential of combining topology optimization and machine learning to reduce material use, enhance structural efficiency, and support sustainable practices in concrete construction. Full article
(This article belongs to the Special Issue Machine Learning and AI-Driven Innovations in Concrete Technology and Construction Materials)
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