Special Issue "Soft Computing Techniques in Materials Science and Engineering"

Submission Deadline: 31 December 2021 (closed)
Guest Editors
Prof. Dr. Panagiotis G. Asteris, School of Pedagogical and Technological Education, Greece
Dr. Danial Jahed Armaghani, University of Malaya, Malaysia
Prof. Dr. Liborio Cavaleri, University of Palermo, Italy
Dr. Hoang Nguyen, Hanoi University of Mining and Geology, Vietnam


Heuristic and computing techniques are technologies that are poised to transform the way humans will interact with machines, and the role that machines will play in all spheres of human life. On the one hand, there is the exhilaration and excitement of the immense potential of these technologies to enhance and enrich human life, and on the other hand, there is fear and apprehension of a dystopian future where machines have taken over.

These techniques are considered in the category of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modelling and optimizing complex structure systems require huge amounts of computing resources, and computing-based solutions can often provide valuable alternatives for efficiently solving problems in engineering. Such techniques due to making non-linear and complex relationships between dependent and independent variables can be performed in the field of engineering with a high degree of accuracy. In this way, many new intelligence models can be introduced for different applications of engineering.

The focus of this Special Issue is on the development of computational methods for solving problems in fields of engineering. Articles submitted to this Special Issue can also be concerned with the most significant recent soft computing, optimization algorithms, hybrid intelligent systems and their applications in engineering sciences. We invite researchers to contribute original research articles, as well as review articles, that will stimulate the continuing research effort on applications of the meta-heuristic and computing techniques to assess/solve engineering problems.

I. Artificial Neural Networks (ANNs)
II. Building Materials
III. Cement-based Mortars
IV. Composite Materials
V. Concrete Materials
VI. Evolutionary multimodal optimization
VII. Forecasting Models
VIII. Fuzzy set theory and hybrid fuzzy models
IX. Genetic algorithm and genetic programming
X. Heuristic Models
XI. Hybrid intelligent systems
XII. Nanomaterials

Published Papers
  • Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models
  • Abstract The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visual methods, which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise. As a result, a robust, reliable, and repeatable method of damage identification is required. Ensemble learning algorithms for identifying structural damage are evaluated in this article, which use deep convolutional neural networks, including simple averaging, integrated stacking, separate stacking, and hybrid weighted averaging ensemble and differential evolution (WAE-DE) ensemble models. Damage identification is carried out on three types of damage. The proposed algorithms are… More
  •   Views:560       Downloads:166        Download PDF

  • Rock Strength Estimation Using Several Tree-Based ML Techniques
  • Abstract The uniaxial compressive strength (UCS) of rock is an essential property of rock material in different relevant applications, such as rock slope, tunnel construction, and foundation. It takes enormous time and effort to obtain the UCS values directly in the laboratory. Accordingly, an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance. This study presents powerful boosting trees evaluation framework, i.e., adaptive boosting machine, extreme gradient boosting machine (XGBoost), and category gradient boosting machine, for estimating the UCS of sandstone. Schmidt hammer rebound number, P-wave velocity,… More
  •   Views:491       Downloads:212        Download PDF

  • Prerequisite Relations among Knowledge Units: A Case Study of Computer Science Domain
  • Abstract The importance of prerequisites for education has recently become a promising research direction. This work proposes a statistical model for measuring dependencies in learning resources between knowledge units. Instructors are expected to present knowledge units in a semantically well-organized manner to facilitate students’ understanding of the material. The proposed model reveals how inner concepts of a knowledge unit are dependent on each other and on concepts not in the knowledge unit. To help understand the complexity of the inner concepts themselves, WordNet is included as an external knowledge base in this model. The goal is to develop a model that… More
  •   Views:475       Downloads:195        Download PDF

  • Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam
  • Abstract Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best… More
  •   Views:885       Downloads:414        Download PDF

  • Seismic Performance of Assembled Shear Wall with Defective Sleeve Connection
  • Abstract In this paper, three kinds of shear walls with full sleeve grouting, fully defective sleeve and partially defective are designed for finite element analysis to analyze the influence of defects on the seismic performance of shear walls. The research shows that at the beginning of loading (5 s), the three models begin to appear compressive damage at the bottom of the wall in all three models. The damage of the defect-free model develops rapidly, and the damage of the fully defective model is basically the same as that of the partially defective model. With the gradual increase of displacement control (15 s),… More
  •   Views:637       Downloads:463        Download PDF

  • Evaluating the Clogging Behavior of Pervious Concrete (PC) Using the Machine Learning Techniques
  • Abstract

    Pervious concrete (PC) is at risk of clogging due to the continuous blockage of sand into it during its service time. This study aims to evaluate and predict such clogging behavior of PC using hybrid machine learning techniques. Based on the 84 groups of the dataset developed in the earlier study, the clogging behavior of the PC was determined by the algorithm combing the SVM (support vector machines) and particle swarm optimization (PSO) methods. The PSO algorithm was employed to adjust the hyperparameters of the SVM and verify the performance using 10-fold cross-validation. The predicting results of the developed model… More

  •   Views:669       Downloads:475        Download PDF

  • Maximum Probabilistic and Dynamic Traffic Load Effects on Short-to-Medium Span Bridges
  • Abstract The steadily growing traffic load has resulted in lots of bridge collapse events over the past decades, especially for short-to-medium span bridges. This study investigated probabilistic and dynamic traffic load effects on short-to-medium span bridges using practical heavy traffic data in China. Mathematical formulations for traffic-bridge coupled vibration and probabilistic extrapolation were derived. A framework for extrapolating probabilistic and dynamic traffic load effect was presented to conduct an efficient and accurate extrapolation. An equivalent dynamic wheel load model was demonstrated to be feasible for short-to-medium span bridges. Numerical studies of two types of simply-supported bridges were conducted based on site-specific… More
  •   Views:1391       Downloads:805       Cited by:14        Download PDF

  • The Influence of Various Structure Surface Boundary Conditions on Pressure Characteristics of Underwater Explosion
  • Abstract The shock wave of the underwater explosion can cause severe damage to the ship structure. The propagation characteristics of shock waves near the structure surface are complex, involving lots of complex phenomena such as reflection, transmission, diffraction, and cavitation. However, different structure surface boundaries have a significant effect on the propagation characteristics of pressure. This paper focuses on investigating the behavior of shock wave propagation and cavitation from underwater explosions near various structure surfaces. A coupled Runge–Kutta discontinuous Galerkin (RKDG) and finite element method (FEM) is utilized to solve the problem of the complex waves of fluids and structure dynamic… More
  •   Views:1391       Downloads:1158        Download PDF

  • A Novel Heuristic Algorithm for the Modeling and Risk Assessment of the COVID-19 Pandemic Phenomenon
  • Abstract The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the… More
  •   Views:4077       Downloads:1618       Cited by:21        Download PDF