Advanced Geotechnics: Optimization, Reliability, and Intelligent Methods for Underground, Foundation, and Earth Structures

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 410

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, 2000 Maribor, Slovenia
Interests: geotechnical design and analysis; construction materials; construction; reliability based design; optimization; neural networks and artificial intelligence

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, 2000 Maribor, Slovenia
Interests: civil engineering structures; optimization, steel structures; intelligence; sustainability; LCA
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor Assistant
Department of Geotechnics, University of Maribor, 2000 Maribor, Slovenia
Interests: geotechnical design and analysis; reliability-based design; optimization; artificial Intelligence; energy geostructures

Special Issue Information

Dear Colleagues,

This Special Issue aims to bring together cutting-edge research that advances geotechnical engineering through optimization, reliability concepts, and intelligent data-driven methods. We welcome contributions addressing underground structures, foundations, embankments, pavements, slopes, earth-retaining systems, and other geotechnical works, both in research and in practice.

Topics of interest include, but are not limited to, optimization-based design (single and multiobjective) of geotechnical and soil–structure systems; reliability-based and performance-based design frameworks; probabilistic modeling of soil and rock parameters; and the integration of geotechnical and structural limit states in unified design procedures. We particularly encourage studies that utilize artificial intelligence and machine learning tools such as neural networks, ANFIS, hybrid, and surrogate models for prediction, decision support, and design-space exploration.

We also welcome contributions on site investigation planning and interpretation, model calibration and validation, field monitoring, and digital workflows (including numerical modeling, model updating, and digital twins). Both methodological papers and application-oriented case studies are encouraged, especially those demonstrating clear implications for safer, more economical, and more sustainable geotechnical design and construction.

Dr. Primož Jelušič
Dr. Tomaž Žula
Guest Editors

Dr. Rok Varga
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. 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

  • geotechnical engineering
  • underground structures
  • foundations
  • earth structures
  • optimization
  • multi-objective design
  • reliability-based design
  • sustainability
  • machine learning
  • numerical modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

27 pages, 9176 KB  
Article
Multi-Objective Topological Optimization of 3D Multi-Material Structures Using the SESO Method with FORM
by Márcio Maciel da Silva, Hélio Luiz Simonetti, Francisco de Assis das Neves and Marcílio Sousa da Rocha Freitas
Buildings 2026, 16(5), 981; https://doi.org/10.3390/buildings16050981 - 2 Mar 2026
Viewed by 202
Abstract
Topological optimization has established itself as an efficient tool for the design of highly complex structures and the rational use of materials, especially in problems involving multiple constraints and conflicting objectives. This work presents a new multi-material topological optimization approach based on the [...] Read more.
Topological optimization has established itself as an efficient tool for the design of highly complex structures and the rational use of materials, especially in problems involving multiple constraints and conflicting objectives. This work presents a new multi-material topological optimization approach based on the ESO smoothing method (SESO), formulated as a multi-objective optimization problem in a MATLAB R2021a environment. The multi-objective formulation simultaneously considers the minimization of the maximum von Mises equivalent stress (or minimum principal stress) and the maximum displacement, which are fundamental criteria for structural engineering design. The proposed methodology also incorporates a reliability analysis using the First-Order Reliability Method (FORM), modeling uncertainties associated with the applied force, volume fraction, and modulus of elasticity through normal and lognormal probability distributions, with a target reliability index of βtarget=3.0. The consistency of the reliability analysis was evaluated using Monte Carlo simulations, validating the reliability indices obtained via FORM. The approach was applied to two classical three-dimensional numerical examples: a cantilever beam under base and center loads and an MBB beam, considering two widely used engineering materials, steel and concrete. The results indicate improved multi-material distribution in the design domain and greater structural robustness against unfavorable loading planes, variations in the modulus of elasticity, and volume constraints imposed by FORM. Furthermore, the minimum yield stress of steel (σymin) and the compressive strength of concrete (fckmin) were calibrated, representing the minimum material strengths required to resist the maximum von Mises stress in steel and the minimum principal stress (σ3) in concrete, ensuring the target reliability index is achieved. This method, thus, highlights the integration of SESO with multi-material, multi-objective, and reliability-based optimization as a consistent, robust, and practically relevant strategy with potential for future applications in structural engineering projects. Full article
Show Figures

Figure 1

Back to TopTop