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Computational Modeling Techniques in Sustainable Materials, Systems and Structures
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Computational Modeling Techniques in Sustainable Materials, Systems and Structures

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Materials".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 19105

Special Issue Editors

Prof. Dr. Panagiotis G. Asteris

E-Mail Website
Guest Editor
Computational Mechanics Laboratory, Department of Civil Engineering, School of Pedagogical and Technological Education, Athens, Greece
Interests: artificial Intelligence; computational mechanics; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Hai-Bang Ly

E-Mail Website
Guest Editor
University of Transport Technology, Hanoi, Vietnam
Interests: computational mechanics; materials science; machine learning; numerical methods

Special Issue Information

Dear Colleagues,

Recent efforts and guidelines aiming to lower the environmental impact and footprint of materials, systems, and structures have led the scientific community to the development of eco-friendly materials. However, despite the importance of eco-friendly materials, they have not as yet been studied to the extent necessary for their wide application, in contrast to typical widely-used materials. This highlights a demand for the use and development of advanced computational techniques, aiming to reveal the nature and behavior of developed eco-friendly materials, thus facilitating their use in an effort towards achieving sustainability.

Metaheuristic and computing methods techniques are poised to transform the way in which humans interact with machines, as well as the role 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 in which machines have taken over.

These methods comprise a category of computer science which is involved in the research, design, and application of intelligent computers. Traditional methods for modeling 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 methods, focusing on nonlinear 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 the development of computational methods for solving problems in the fields of sustainable materials, systems, and structures. Furthermore, please see below a list of potential keywords about the proposed journal.

Prof. Dr. Panagiotis G. Asteris
Dr. Hai-Bang Ly
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sustainability 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 2400 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

  • neural network and deep neural network
  • fuzzy set theory and hybrid fuzzy models
  • genetic algorithm and genetic programming
  • hybrid intelligent systems
  • optimization algorithms
  • multicriteria decision making (MCDM)
  • evolutionary multimodal optimization
  • multiexpression programming
  • machine learning techniques
  • regression-based simulation algorithm

Published Papers (4 papers)

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29 pages, 4803 KiB  
Article
Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination
by Binh Thai Pham, Trung Nguyen-Thoi, Hai-Bang Ly, Manh Duc Nguyen, Nadhir Al-Ansari, Van-Quan Tran and Tien-Thinh Le
Sustainability 2020, 12(6), 2339; https://doi.org/10.3390/su12062339 - 17 Mar 2020
Cited by 50 | Viewed by 4306
Abstract
Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an [...] Read more.
Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model. Full article
(This article belongs to the Special Issue Computational Modeling Techniques in Sustainable Materials, Systems and Structures)
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17 pages, 3454 KiB  
Article
Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness
by Danial Jahed Armaghani, Panagiotis G. Asteris, Behnam Askarian, Mahdi Hasanipanah, Reza Tarinejad and Van Van Huynh
Sustainability 2020, 12(6), 2229; https://doi.org/10.3390/su12062229 - 12 Mar 2020
Cited by 75 | Viewed by 4194
Abstract
The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight [...] Read more.
The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight SVM models with different kernel types, i.e., the radial basis function (RBF), the linear (LIN), the sigmoid (SIG), and the polynomial (POL). Four of these models were developed using only the SVM method, while the four other models were hybridized with a feature selection (FS) technique. The performance of each model was assessed using five performance indices and a simple ranking system. The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models. Concerning the importance of variables for predicting the brittleness index (BI), the Schmidt hammer rebound number (Rn) was identified as the most important variable by the three single-based models, developed by POL, SIG, and LIN kernels. However, the single SVM model developed by RBF identified density as the most important input variable. Concerning the hybrid SVM models, three models that were developed using the RBF, POL, and SIG kernels identified the point load strength index as the most important input, while the model developed using the LIN identified the Rn as the most important input. All four single-based SVM models identified the p-wave velocity (Vp) as the least important input. Concerning the least important factors for predicting the BI of the rock in hybrid-based models, Vp was identified as the least important factor by FS-SVM-POL, FS-SVM-SIG, and FS-SVM-LIN, while the FS-SVM-RBF identified Rn as the least important input. Full article
(This article belongs to the Special Issue Computational Modeling Techniques in Sustainable Materials, Systems and Structures)
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22 pages, 3119 KiB  
Article
A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation
by Dong Van Dao, Hojjat Adeli, Hai-Bang Ly, Lu Minh Le, Vuong Minh Le, Tien-Thinh Le and Binh Thai Pham
Sustainability 2020, 12(3), 830; https://doi.org/10.3390/su12030830 - 22 Jan 2020
Cited by 140 | Viewed by 7902
Abstract
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation [...] Read more.
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength. Full article
(This article belongs to the Special Issue Computational Modeling Techniques in Sustainable Materials, Systems and Structures)
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2 pages, 164 KiB  
Erratum
Erratum: Ly, H.-B., et al. Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams. Sustainability 2020, 12, 2709
by Hai-Bang Ly, Tien-Thinh Le, Huong-Lan Thi Vu, Van Quan Tran, Lu Minh Le and Binh Thai Pham
Sustainability 2020, 12(17), 7029; https://doi.org/10.3390/su12177029 - 28 Aug 2020
Cited by 3 | Viewed by 1624
Abstract
The authors would like to make the following corrections to the published paper [...] Full article
(This article belongs to the Special Issue Computational Modeling Techniques in Sustainable Materials, Systems and Structures)
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