Numerical Analysis and Modeling in Structural Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1366

Special Issue Editor


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Guest Editor
School of Civil Engineering and Geomatics, Universidad del Valle, Cali 760032, Colombia
Interests: structural dynamics; earthquake engineering; human–structure interaction; seismic risk assessment; vibration control; smart structures

Special Issue Information

Dear Colleagues,

Numerical analysis and modeling are essential in structural engineering, as they offer powerful tools to simulate, analyze, and optimize the behavior of complex structures under diverse loading and environmental conditions. This Special Issue aims to present recent advances in computational modeling and numerical techniques that support innovation in structural design, performance assessment, and safety evaluation.

Topics of interest include the following: nonlinear structural behavior; substructuring in structural dynamics; damage and fracture mechanics; multiscale and multiphysics modeling; and computational mechanics. We welcome submissions focused on optimization strategies such as topology and shape optimization, sustainable design, and life-cycle analysis. Contributions addressing uncertainty quantification, stochastic modeling, reliability analysis, and robust or resilient structural design are also encouraged.

Further areas of interest include data-driven modeling and the integration of artificial intelligence, machine learning, or quantum computing into numerical frameworks. Studies involving high-performance computing and large-scale simulations are also within the scope of this Special Issue.

If you require an extension or have any questions regarding this Special Issue, please do not hesitate to contact me by replying to the provided email.

Prof. Dr. Daniel Gomez
Guest Editor

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Keywords

  • Numerical analysis
  • Computational modeling
  • Finite element methods
  • Nonlinear structural behavior
  • Substructuring in structural dynamics
  • Multiscale modeling
  • Multiphysics simulation
  • Fracture mechanics
  • Topology optimization
  • Reliability analysis
  • Stochastic modeling
  • Uncertainty quantification
  • Robust design
  • Resilient structures
  • High-performance computing
  • Large-scale simulation
  • Machine learning
  • Data-driven modeling
  • Quantum computing in engineering
  • Sustainable structural design
  • Life-cycle assessment

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Published Papers (1 paper)

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Research

18 pages, 5589 KB  
Article
Efficient Meshless Phase-Field Modeling of Crack Propagation by Using Adaptive Load Increments and Variable Node Densities
by Izaz Ali, Božidar Šarler and Boštjan Mavrič
Mathematics 2025, 13(23), 3795; https://doi.org/10.3390/math13233795 - 26 Nov 2025
Cited by 1 | Viewed by 911
Abstract
This study employs the fourth-order phase-field method (PFM) to investigate crack propagation. The PFM incurs significant computational costs due to its need for a highly dense node arrangement for accurate crack propagation. This study proposes an adaptive loading step size strategy combined with [...] Read more.
This study employs the fourth-order phase-field method (PFM) to investigate crack propagation. The PFM incurs significant computational costs due to its need for a highly dense node arrangement for accurate crack propagation. This study proposes an adaptive loading step size strategy combined with a scattered node (SCNvar) arrangement with variable spacings. The mechanical and phase-field models are solved using the strong-form meshless local radial basis function collocation method in a staggered approach. The method’s performance is evaluated based on accuracy and computational cost, using regular nodes (RGN) and scattered nodes (SCNuni) with uniform spacing, as well as SCNvar with variable node spacing. Two benchmark tests are used to analyze the proposed method: a symmetric double-notch tension and a single-edge notch shear test. The analysis shows that the adaptive step size strategy improves numerical stability while the SCNvar significantly reduces computational cost. Using SCNvar, the CPU time is decreased by about thirty times compared to uniform nodes in the tensile case and by approximately three times in the shear case, without sacrificing accuracy. This confirms that directing computational resources to critical regions can significantly reduce CPU time, suggesting that adaptive node redistribution could further enhance computational performance. Full article
(This article belongs to the Special Issue Numerical Analysis and Modeling in Structural Engineering)
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