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Buildings
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Artificial Intelligence in Material and Structural Optimization for Sustainable Infrastructure
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Artificial Intelligence in Material and Structural Optimization for Sustainable Infrastructure

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 4017

Special Issue Editors

Dr. Satish Paudel

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA
Interests: 3D concrete printing; machine learning; FEA; performance-based design; composite structures
Prof. Dr. Panagiotis G. Asteris

E-Mail Website
Guest Editor
Computational Mechanics Laboratory, Department of Civil Engineering, School of Pedagogical and Technological Education, GR15122 Athens, Greece
Interests: computational intelligence; computational mechanics; machine learning; masonry structures; structural engineering
Special Issues, Collections and Topics in MDPI journals
Dr. T. Jeyakaran

E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Engineering, South Eastern University of Sri Lanka, Oluvil 32360, Sri Lanka
Interests: finite element modelling; shear retrofitting of concrete structures; concrete chemistry; concrete durability
Dr. Hakas Prayuda

E-Mail Website
Guest Editor
Department of Civil Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia
Interests: monitoring and maintenance for reincorced concrete structure; concrete technology

Special Issue Information

Dear Colleagues,

The necessity for innovative methods in material selection, structural design, and infrastructure development has increased due to the global push for sustainability, resilience, and efficiency in the built environment. Smarter decision-making, automated performance improvement, and notable decreases in material, energy, and construction waste are all made possible by the integration of artificial intelligence (AI) with optimization approaches in engineering.

This Special Issue aims to explore the frontiers of AI-driven material and structural optimization with a focus on advancing sustainable infrastructure. Recent developments in metaheuristic algorithms, machine learning, deep learning, and multi-objective optimization are changing the way materials scientists and civil engineers approach design, fabrication, and performance evaluation.

We invite researchers, practitioners, and innovators to contribute original research articles, review papers, and case studies that demonstrate novel applications of AI and computational methods in the optimization of structural systems and materials for sustainable development.

This Special Issue will offer a platform for disseminating interdisciplinary advancements and facilitating dialogs among stakeholders in structural engineering, materials science, computational design, and sustainability.

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

  • AI-based structural design optimization for performance, cost, and sustainability.
  • Data-driven materials design, selection, and multi-scale modeling.
  • Topology and shape optimization of structural systems using machine learning and evolutionary algorithms.
  • AI-assisted life-cycle assessment and embodied carbon minimization.
  • Application of generative design and parametric tools for sustainable architecture and infrastructure.
  • Integration of AI with digital fabrication and 3D concrete printing.
  • Reinforcement learning and surrogate modeling in real-time structural optimization.
  • Computational approaches for climate-resilient and adaptive infrastructure design.
  • Case studies demonstrating AI-augmented optimization in bridges, buildings, pavements, or marine structures.
  • Emerging trends, challenges, and ethical considerations in AI-driven engineering.

We welcome your valuable contributions to this Special Issue and look forward to showcasing pioneering work that pushes the boundaries of sustainable engineering through artificial intelligence and computational intelligence.

Dr. Satish Paudel
Prof. Dr. Panagiotis G. Asteris
Dr. Hakas Prayuda
Dr. T. Jeyakaran
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. Buildings 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

  • artificial intelligence (AI)
  • generative design
  • surrogate modeling
  • additive manufacturing in construction
  • resilient infrastructure
  • life-cycle assessment (LCA)
  • sustainable construction materials

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

Published Papers (2 papers)

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Research

27 pages, 3445 KB  
Article
Artificial Neural Network-Based Prediction of Compressive Strength for Mix Design Evaluation in Sustainable Expanded Polystyrene-Infused Concrete
by Kavin John O. Castillanes and Gilford B. Estores
Buildings 2026, 16(6), 1252; https://doi.org/10.3390/buildings16061252 - 21 Mar 2026
Abstract
Lightweight concrete incorporating expanded polystyrene (EPS) remains an active area of research due to its potential to produce more sustainable resource-efficient construction materials. However, identifying the optimal mix design for EPS-infused concrete typically requires extensive experimental trials, resulting in significant time, cost, and [...] Read more.
Lightweight concrete incorporating expanded polystyrene (EPS) remains an active area of research due to its potential to produce more sustainable resource-efficient construction materials. However, identifying the optimal mix design for EPS-infused concrete typically requires extensive experimental trials, resulting in significant time, cost, and material consumption. To address this challenge, this study proposes an artificial neural network (ANN) predictive model with 5-fold cross-validation to estimate compressive strength performance and to develop mix design recommendations based on actual and predicted results. A total of 55 experimental samples were prepared and grouped into 11 batches, with the EPS volume replacement levels ranging from 0% to 50% at 5% increments. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2), and scatter index (SI), with graphical representations like predicted vs. actual plots, response plots, and residual plots, and the results were benchmarked against a multiple linear regression (MLR) model. Among the tested configurations, the 4-5-1 ANN model demonstrated the highest predictive accuracy. Furthermore, a Shapley (SHAP) analysis was conducted to interpret the model behavior and determine the relative importance of the input variables. The findings reveal that EPS content had the greatest influence on compressive strength prediction, followed by slump value, then gravel content, and finally concrete density. Full article
(This article belongs to the Special Issue Artificial Intelligence in Material and Structural Optimization for Sustainable Infrastructure)
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16 pages, 7804 KB  
Article
Linear Seismic Analysis and Structural Optimization of Reinforced Concrete Frames Using OpenSeesPy
by Diego Llanos and Rick M. Delgadillo
Buildings 2025, 15(23), 4388; https://doi.org/10.3390/buildings15234388 - 4 Dec 2025
Cited by 1 | Viewed by 763
Abstract
Seismic design of reinforced concrete buildings in highly active seismic regions is challenging, as structural members are often oversized due to conservative design practices, leading to inefficient use of materials. This study proposes an optimization methodology based on the Peruvian seismic code E.030, [...] Read more.
Seismic design of reinforced concrete buildings in highly active seismic regions is challenging, as structural members are often oversized due to conservative design practices, leading to inefficient use of materials. This study proposes an optimization methodology based on the Peruvian seismic code E.030, implemented with the OpenSeesPy library for modeling and numerical analysis. The methodology automates the linear analysis of frame structures through the parametrization of member dimensions, span lengths, and material properties. Optimization is carried out using the Hill Climbing algorithm, which iteratively explores design alternatives and verifies compliance with code requirements for interstory drift and base shear. Results show material savings of up to 20% in beams and columns. Although interstory drifts increased by 60–85% compared to the initial configuration, they remained within code limits. The methodology establishes a framework for integrating optimization techniques into the seismic design of reinforced concrete frame buildings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Material and Structural Optimization for Sustainable Infrastructure)
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