Special lssues
Table of Content

Meta-heuristic Algorithms in Materials Science and Engineering

Submission Deadline: 31 December 2024 Submit to Special Issue

Guest Editors

Prof. Dr. Panagiotis G. Asteris, School of Pedagogical and Technological Education, Greece
Dr. Ahmed Salih Mohammed, University of Sulaimani, Iraq

Summary

During the last three decades, nonconventional methods are becoming an important class of efficient tools providing solutions to complicated engineering problems. Among these methods, soft computing has to be mentioned as one of the most eminent approaches. Neural networks (NNs), fuzzy logic, and evolutionary and classifications algorithms are the most popular soft-computing techniques.


The focus of this Special Issue is on nondeterministic computational methods for the modeling of structural engineering and materials problems. Articles submitted to this Special Issue can also be concerned about the most significant recent developments in computational methods and their applications in structural engineering and materials problems. We invite researchers to contribute original research articles as well as review articles that will stimulate the continuing research effort on applications of the soft computing approaches to model structural engineering and materials problems.


Keywords

1.Artificial neural networks (ANNs)
2.Computational biology/bioinformatics
3.Computational science and engineering
4.Evolutionary multimodal optimization
5.Forecasting models
6.Fuzzy set theory and hybrid fuzzy models
7.Genetic algorithm and genetic programming
8.Heuristic models
9.Hybrid intelligent systems
10.Image processing and computer vision
11.Machine learning techniques
12.Multicriteria decision making (MCDM)
13.Multiexpression programming
14.Multivariate adaptive regression splines (MARS)
15.Neural networks and deep neural networks
16.Optimization algorithms [structural optimization; topology optimization]
17.Neural networks, support vector machines
18.Fuzzy logic and fuzzy systems
19.Structural design, diagnostics, and health monitoring
20.Modeling of mechanical properties of structural materials

Published Papers


  • Open Access

    ARTICLE

    An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

    Abidhan Bardhan, Raushan Kumar Singh, Mohammed Alatiyyah, Sulaiman Abdullah Alateyah
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1521-1555, 2024, DOI:10.32604/cmes.2023.044467
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results featuring five influencing factors was… More >

  • Open Access

    ARTICLE

    Predicting the International Roughness Index of JPCP and CRCP Rigid Pavement: A Random Forest (RF) Model Hybridized with Modified Beetle Antennae Search (MBAS) for Higher Accuracy

    Zhou Ji, Mengmeng Zhou, Qiang Wang, Jiandong Huang
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1557-1582, 2024, DOI:10.32604/cmes.2023.046025
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract To improve the prediction accuracy of the International Roughness Index (IRI) of Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP), a machine learning approach is developed in this study for the modelling, combining an improved Beetle Antennae Search (MBAS) algorithm and Random Forest (RF) model. The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study. The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well. The results by the comparative analysis showed the prediction accuracy of the IRI of the newly… More >

    Graphic Abstract

    Predicting the International Roughness Index of JPCP and CRCP Rigid Pavement: A Random Forest (RF) Model Hybridized with Modified Beetle Antennae Search (MBAS) for Higher Accuracy

  • Open Access

    ARTICLE

    A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

    Elif Varol Altay, Osman Altay, Yusuf Özçevik
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1039-1094, 2024, DOI:10.32604/cmes.2023.029404
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve. Such design problems are widely experienced in many engineering fields, such as industry, automotive, construction, machinery, and interdisciplinary research. However, there are established optimization techniques that have shown effectiveness in addressing these types of issues. This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues. The algorithms used in the study are listed as: transient search optimization (TSO), equilibrium optimizer (EO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale… More >

  • Open Access

    ARTICLE

    An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

    Yingui Qiu, Shuai Huang, Danial Jahed Armaghani, Biswajeet Pradhan, Annan Zhou, Jian Zhou
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2873-2897, 2024, DOI:10.32604/cmes.2023.029938
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field are mostly based on a… More >

  • Open Access

    ARTICLE

    Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

    Ayla Ocak, Ümit Işıkdağ, Gebrail Bekdaş, Sinan Melih Nigdeli, Sanghun Kim, Zong Woo Geem
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2899-2924, 2024, DOI:10.32604/cmes.2023.030418
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures. The base isolators may lose their damping capacity over time due to environmental or dynamic effects. This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-term isolator life. In this study, an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time. With the developed model, the required damping capacity of the isolator structure was estimated and compared… More >

  • Open Access

    ARTICLE

    Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

    Rabar H. Faraj, Hemn Unis Ahmed, Hardi Saadullah Fathullah, Alan Saeed Abdulrahman, Farid Abed
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2925-2954, 2024, DOI:10.32604/cmes.2023.029392
    (This article belongs to this Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract Plain concrete is strong in compression but brittle in tension, having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures, even when steel reinforcing is present. In order to address these challenges, short polymer fibers are randomly dispersed in a cement-based matrix to form a highly ductile engineered cementitious composite (ECC). This material exhibits high ductility under tensile forces, with its tensile strain being several hundred times greater than conventional concrete. Since concrete is inherently weak in tension, the tensile strain capacity (TSC) has become one of the most extensively researched properties. As a… More >

Share Link