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Artificial Intelligence in the Design and Innovation of High-Performance Concrete Materials

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Materials Science and Engineering".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1584

Special Issue Editors

Dr. Chuanqi Li

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: high-performance concrete; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiancheng Mei

E-Mail Website
Guest Editor Assistant
Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
Interests: tunnel engineering; seismic materials; stable analysis; rubber sand concrete; AI

Special Issue Information

Dear Colleagues,

With the rapid development of artificial intelligence (AI), its applications across various fields are deepening, showing tremendous potential, particularly in materials science and engineering. This Special Issue focuses on the cutting-edge application of AI in the design and development of high-performance concrete materials, aiming to explore how AI, through intelligent algorithms and data analysis, can enhance the performance and sustainability of concrete materials. High-performance concrete, known for its superior strength, durability, and potential for extreme environment applications, has gained significant attention in modern construction and infrastructure projects. AI technologies such as machine learning, deep learning, and optimization algorithms enable researchers to predict material performance more accurately, accelerate the design of novel concrete formulations, and achieve customized, intelligent production. Furthermore, AI offers innovative solutions for quality control during the concrete production process, optimizing waste material utilization, and conducting lifecycle analysis. This Special Issue will gather the latest research from top global scholars and engineers, providing an interdisciplinary platform for experts in the concrete field. The aim is to promote intelligent design and green development of high-performance concrete materials, offering innovative ideas and technical support for the future of building materials.

The suggested themes for this Special Issue, titled “Artificial Intelligence in the Design and Innovation of High-Performance Concrete Materials”, as are follows:

  • AI-driven concrete mix design optimization;
  • Intelligent performance prediction and modeling of concrete materials;
  • Quality control and process optimization in concrete production using AI;
  • AI applications in concrete waste management and recycling;
  • AI in durability analysis of concrete materials;
  • Intelligent and customized concrete material design.

You may choose our Joint Special Issue in Materials.

Dr. Chuanqi Li
Guest Editor

Dr. Xiancheng Mei
Guest Editor Assistant

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. Applied Sciences 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 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

  • artificial intelligence
  • high-performance concrete
  • material design
  • machine learning
  • data analysis
  • optimization algorithms
  • sustainability
  • intelligent production

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Published Papers (3 papers)

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Research

16 pages, 6942 KB  
Article
Experimental Study on Pore Structure, Mechanical Behavior and Permeability Characteristics of Weakly Cemented Sandstone
by Ahu Zhao, Yinping Li, Xilin Shi, Shefeng Hao, Zengguang Che, Wenrui Feng, Hanzhao Zhang, Hongling Ma and Mingnan Xu
Appl. Sci. 2026, 16(7), 3432; https://doi.org/10.3390/app16073432 - 1 Apr 2026
Viewed by 509
Abstract
To investigate the seepage and mechanical behavior of the overlying strata during solution mining in salt deposits, porous sandstones with different grain sizes were selected for study. First, a series of microscopic tests, including SEM, MIP, and NMR, was conducted to characterize the [...] Read more.
To investigate the seepage and mechanical behavior of the overlying strata during solution mining in salt deposits, porous sandstones with different grain sizes were selected for study. First, a series of microscopic tests, including SEM, MIP, and NMR, was conducted to characterize the pore structure of the rocks. Subsequently, using a servo-controlled triaxial rock testing system, permeability tests covering the complete stress–strain process were performed under different confining pressures and seepage pressures based on the steady-state method, in order to analyze the seepage and mechanical characteristics of the sandstones during deformation and failure. The results indicate that the investigated aquifer sandstones are characterized by weak cementation, high porosity, large pore size, good pore connectivity, and relatively high permeability. High confining pressure enhances the mechanical strength of the sandstone while reducing its permeability, whereas increasing seepage pressure decreases mechanical strength and enhances permeability during triaxial compression under pore water pressure conditions. Throughout the complete stress–strain process, the evolution of permeability is jointly controlled by the intrinsic pore structure of the rock, the stress loading path, and the failure mode. Under high confining pressure, localized compaction bands may develop, and the formation of such localized structures suppresses any increase in permeability. Acoustic emission shows good correlations with both the stress–strain response and permeability evolution. This study provides new insights into the pore structure of loose, highly permeable sandstones and their hydromechanical coupling behavior throughout the complete stress–strain process. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Design and Innovation of High-Performance Concrete Materials)
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28 pages, 12486 KB  
Article
Sustainability-Focused Evaluation of Self-Compacting Concrete: Integrating Explainable Machine Learning and Mix Design Optimization
by Abdulaziz Aldawish and Sivakumar Kulasegaram
Appl. Sci. 2026, 16(3), 1460; https://doi.org/10.3390/app16031460 - 31 Jan 2026
Viewed by 412
Abstract
Self-compacting concrete (SCC) offers significant advantages in construction due to its superior workability; however, optimizing SCC mixture design remains challenging because of complex nonlinear material interactions and increasing sustainability requirements. This study proposes an integrated, sustainability-oriented computational framework that combines machine learning (ML), [...] Read more.
Self-compacting concrete (SCC) offers significant advantages in construction due to its superior workability; however, optimizing SCC mixture design remains challenging because of complex nonlinear material interactions and increasing sustainability requirements. This study proposes an integrated, sustainability-oriented computational framework that combines machine learning (ML), SHapley Additive exPlanations (SHAP), and multi-objective optimization to improve SCC mixture design. A large and heterogeneous publicly available global SCC dataset, originally compiled from 156 independent peer-reviewed studies and further enhanced through a structured three-stage data augmentation strategy, was used to develop robust predictive models for key fresh-state properties. An optimized XGBoost model demonstrated strong predictive accuracy and generalization capability, achieving coefficients of determination of R2=0.835 for slump flow and R2=0.828 for T50 time, with reliable performance on independent industrial SCC datasets. SHAP-based interpretability analysis identified the water-to-binder ratio and superplasticizer dosage as the dominant factors governing fresh-state behavior, providing physically meaningful insights into mixture performance. A cradle-to-gate life cycle assessment was integrated within a multi-objective genetic algorithm to simultaneously minimize embodied CO2 emissions and material costs while satisfying workability constraints. The resulting Pareto-optimal mixtures achieved up to 3.9% reduction in embodied CO2 emissions compared to conventional SCC designs without compromising performance. External validation using independent industrial data confirms the practical reliability and transferability of the proposed framework. Overall, this study presents an interpretable and scalable AI-driven approach for the sustainable optimization of SCC mixture design. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Design and Innovation of High-Performance Concrete Materials)
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22 pages, 6307 KB  
Article
Study on Failure Mechanisms and Mechanical Properties of Rock Masses with Discontinuous Joints Based on 3D Printing Technology
by Yanshuang Yang, Junjie Zeng, Zhen Cui and Jinghan Yin
Appl. Sci. 2026, 16(2), 863; https://doi.org/10.3390/app16020863 - 14 Jan 2026
Viewed by 359
Abstract
Within natural rock masses, discontinuous joints are more prevalent than continuous joints. Discontinuous joints refer to non-persistent structural planes separated by intact rock bridges and can be quantified by the continuity coefficient KA. They significantly affect the macroscopic mechanical properties of [...] Read more.
Within natural rock masses, discontinuous joints are more prevalent than continuous joints. Discontinuous joints refer to non-persistent structural planes separated by intact rock bridges and can be quantified by the continuity coefficient KA. They significantly affect the macroscopic mechanical properties of rock masses. Therefore, investigating discontinuous jointed rock masses with diverse morphologies carries considerable theoretical and engineering significance. Using 3D printing technology, resin-based specimens with discontinuous joints were subjected to laboratory mechanical tests to explore the evolution of failure mechanisms and mechanical properties of discontinuous jointed rock masses with different inclinations, undulation amplitudes, and structural plane continuity. Results show that under compression, discontinuous jointed rock masses consistently undergo combined tensile and shear stresses, with joint undulation amplitude and continuity governing coplanar crack initiation. As the joint inclination angle ranges from 0° to 90°, the peak compressive strength first decreases and then increases: specimens with continuous joints or discontinuous joints (continuity coefficient KA < 0.25) follow a “V”-shaped trend, while those with KA > 0.25 exhibit a “U”-shaped trend. Joint continuity is a key factor governing rock mass strength: at the same rock column radius, higher continuity results in lower strength, and vice versa. Joint morphology also influences strength, with specimens with regular zigzag joints and rectangular corrugated joints exhibiting 6.7% and 11.2% higher strength than smooth-jointed specimens, respectively. These results clarify the effects of joint continuity and undulation on rock mass strength, providing a theoretical foundation for the rapid determination of KA via borehole imaging and laser scanning in engineering practice, and enabling direct prediction of rock mass strength trends. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Design and Innovation of High-Performance Concrete Materials)
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