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Applied Sciences
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Computer Methods in Mechanical, Civil and Biomedical Engineering
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Computer Methods in Mechanical, Civil and Biomedical Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 5723

Special Issue Editors

Dr. Jakub Krzysztof Grabski

E-Mail Website
Guest Editor
Institute of Applied Mechanics, Poznan University of Technology, Jana Pawła II 24, 60-965, Poznań, Poland
Interests: computational mechanics; corrugated cardboard; fluid mechanics; biomechanics; heat transfer; meshless methods; inverse problems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chia-Ming Fan

E-Mail Website
Guest Editor
Department of Harbor and River Engineering & Computation and Simulation Center, National Taiwan Ocean University, Keelung 20224, Taiwan
Interests: fluid mechanics; modeling and simulation; computational fluid dynamics; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Po-Wei Li

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Qingdao University, Qingdao 266071, China
Interests: computational fluid mechanics; numerical methods for PDEs; meshless methods; heat and mass transfer; inverse problem

Special Issue Information

Dear Colleagues,

Among various computational methods applied in science, one can distinguish mesh-based or meshless numerical methods, which are commonly used to analyze problems in mechanical or civil engineering, bioengineering, and other fields. The mesh-based methods require discretization at the beginning, which can be challenging for the complicated geometry of the problem under consideration. For example, the most common mesh-based method is the finite element method, which is implemented in engineering software. On the other hand, one can apply the meshless methods, which avoid the process of geometry discretization, e.g., the method of fundamental solutions or the global radial basis function collocation method.

Artificial intelligence methods have recently shown great importance in many fields of science. They are used for classification, identification, optimization, clustering, and many other tasks. The crucial stage of artificial intelligence algorithms is a learning process, which can be supervised or unsupervised. One can also apply reinforced learning. Among various techniques, one can distinguish artificial neural networks or evolutionary algorithms, which are commonly used in mechanical, civil, and biomedical engineering.

This Special Issue of Applied Sciences is devoted to the application of all these computer methods in, but not limited to, mechanical, civil, and biomedical engineering.

Dr. Jakub Krzysztof Grabski
Prof. Dr. Chia-Ming Fan
Dr. Po-Wei Li
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. Applied Sciences 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

  • numerical methods
  • finite element method
  • meshless methods
  • radial basis functions
  • method of fundamental solutions
  • artificial intelligence
  • artificial neural networks
  • evolutionary algorithms
  • direct and inverse problems

Published Papers (7 papers)

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Research

16 pages, 3491 KiB  
Article
Enhancing Sensitivity of Double-Walled Carbon Nanotubes with Longitudinal Magnetic Field
by Hamid Reza Ahmadi, Zaher Rahimi and Wojciech Sumelka
Appl. Sci. 2024, 14(7), 3010; https://doi.org/10.3390/app14073010 - 03 Apr 2024
Viewed by 367
Abstract
In this study, the behavior of double-walled carbon nanotubes (DWCNTs) used as mass sensors is explored under various boundary conditions; particular attention is paid to the crucial topic of resonant nanomechanical mass sensors. In the presented approach, nanotubes are subjected to a distributed [...] Read more.
In this study, the behavior of double-walled carbon nanotubes (DWCNTs) used as mass sensors is explored under various boundary conditions; particular attention is paid to the crucial topic of resonant nanomechanical mass sensors. In the presented approach, nanotubes are subjected to a distributed transverse magnetic force and supported by an elastic foundation. The impacts of the longitudinal magnetic field, elastic medium, and diverse physical parameters on the responsiveness of the sensors are assessed. Using the energy method, governing equations are formulated to determine the frequency shifts of the mass nanosensors. Our findings reveal significant variations in the frequency shifts due to a longitudinal magnetic field, which depends on the applied boundary conditions. This research holds significance in the design of resonant nanomechanical mass sensors and provides valuable insights into the interplay of factors affecting their performance. Through exploring the intricate dynamics of DWCNTs used as mass sensors and thus contributing to the broader understanding of nanoscale systems, the implications for advancements in sensor design are offered and applications are introduced. Full article
(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)
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21 pages, 7004 KiB  
Article
Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression
by Wendimu Fanta Gemechu, Wojciech Sitek and Gilmar Ferreira Batalha
Appl. Sci. 2024, 14(6), 2554; https://doi.org/10.3390/app14062554 - 18 Mar 2024
Viewed by 600
Abstract
This study investigates the application of regression neural networks, particularly the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and [...] Read more.
This study investigates the application of regression neural networks, particularly the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics, such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the models’ effectiveness and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R2 of 0.9900. The model, which was trained with different datasets for nearly 40 steel grades, enables the prediction of hardenability curves, but is limited to the range of the training dataset. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results. Full article
(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)
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23 pages, 4413 KiB  
Article
Machine Learning Prediction Techniques in the Optimization of Diagnostic Laboratories’ Network Operations
by Krzysztof Regulski, Andrzej Opaliński, Jakub Swadźba, Piotr Sitkowski, Paweł Wąsowicz and Agnieszka Kwietniewska-Śmietana
Appl. Sci. 2024, 14(6), 2429; https://doi.org/10.3390/app14062429 - 13 Mar 2024
Viewed by 417
Abstract
The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and [...] Read more.
The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and the quality of tests. A functional quality management system is an integral element of every diagnostic laboratory, ensuring reliability and appropriate work standards. This system includes maintaining correct and reliable analytical test results as well as the optimal use of the laboratory equipment’s processing capacity and the appropriate organization of the supply chain—both analytical material and reagents. It is extremely important to avoid situations in which tests cannot be performed due to a lack of reagents, the overloading of analyzers, or improper calibration. Therefore, the accurate prediction of the number of orders is crucial to optimize the laboratory’s operations, both in the short term—for the next few hours and minutes—and in the longer term, even monthly, which will allow for the appropriate planning of reagent stock. As part of the research presented in this article, machine learning methods were used to implement the above functionalities, which allowed for the development of a prototype of a laboratory optimization system using patient test results as a basis. Full article
(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)
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12 pages, 4957 KiB  
Article
Augmented Reality-Assisted Surgical Exposure of an Impacted Tooth: A Pilot Study
by Monica Macrì, Giuseppe D’Albis, Vincenzo D’Albis, Simona Timeo and Felice Festa
Appl. Sci. 2023, 13(19), 11097; https://doi.org/10.3390/app131911097 - 09 Oct 2023
Cited by 1 | Viewed by 814
Abstract
Three-dimensional radiological evaluation through cone beam computer tomography is essential in diagnosing and establishing proper surgical management in impacted teeth. Through Augmented Reality (AR), clinicians have the opportunity to use three-dimensional computer-generated radiologic information to visualise the patient and simultaneously the superimposition of [...] Read more.
Three-dimensional radiological evaluation through cone beam computer tomography is essential in diagnosing and establishing proper surgical management in impacted teeth. Through Augmented Reality (AR), clinicians have the opportunity to use three-dimensional computer-generated radiologic information to visualise the patient and simultaneously the superimposition of his internal structures. Here, we describe a digital workflow to assist the oral surgeon in pre-orthodontic exposure of a vestibular impacted canine using AR. The AR hardware consists of a camera and a traditional stand-up monitor. The registration and tracking are video-based and marker-free, with an automatic pose estimation obtained through VisLab 20.10.1AR software algorithm’s object recognition and tracking approach. A 3D model is created by combining the anterior teeth taken from the intraoral scan with the same teeth plus the included tooth taken from the CBCT segmentation. The 3D file is uploaded into the AR software. Model tracking is straightforward to set up without prior registration of targets or surroundings. The AR information is used successfully to define the surgical access to perform flap and osteotomy. The accuracy of model tracking matching was calculated constantly by the software. During the tracking, the process recorded an inlier ratio of 0.39:0.48. Further studies and clinical trials will evaluate the value of this novel technology in the management of impacted teeth. Full article
(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)
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18 pages, 2823 KiB  
Article
Numerical Solutions of the Nonlinear Dispersive Shallow Water Wave Equations Based on the Space–Time Coupled Generalized Finite Difference Scheme
by Po-Wei Li, Shenghan Hu and Mengyao Zhang
Appl. Sci. 2023, 13(14), 8504; https://doi.org/10.3390/app13148504 - 23 Jul 2023
Cited by 3 | Viewed by 790
Abstract
This study applies the space–time generalized finite difference scheme to solve nonlinear dispersive shallow water waves described by the modified Camassa–Holm equation, the modified Degasperis–Procesi equation, the Fornberg–Whitham equation, and its modified form. The proposed meshless numerical scheme combines the space–time generalized finite [...] Read more.
This study applies the space–time generalized finite difference scheme to solve nonlinear dispersive shallow water waves described by the modified Camassa–Holm equation, the modified Degasperis–Procesi equation, the Fornberg–Whitham equation, and its modified form. The proposed meshless numerical scheme combines the space–time generalized finite difference method, the two-step Newton’s method, and the time-marching method. The space–time approach treats the temporal derivative as a spatial derivative. This enables the discretization of all partial derivatives using a spatial discretization method and efficiently handles mixed derivatives with the proposed mesh-less numerical scheme. The space–time generalized finite difference method is derived from Taylor series expansion and the moving least-squares method. The numerical discretization process only involves functional data and weighting coefficients on the central and neighboring nodes. This results in a sparse matrix system of nonlinear algebraic equations that can be efficiently solved using the two-step Newton’s method. Additionally, the time-marching method is employed to advance the space–time domain along the time axis. Several numerical examples are presented to validate the effectiveness of the proposed space–time generalized finite difference scheme. Full article
(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)
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15 pages, 4614 KiB  
Article
Mesh-Free MLS-Based Error-Recovery Technique for Finite Element Incompressible Elastic Computations
by Nabil Ben Kahla, Saeed AlQadhi and Mohd. Ahmed
Appl. Sci. 2023, 13(12), 6890; https://doi.org/10.3390/app13126890 - 07 Jun 2023
Viewed by 920
Abstract
The finite element error and adaptive analysis are implemented in finite element procedures to increase the reliability of numerical analyses. In this paper, the mesh-free error-recovery technique based on moving least squares (MLS) interpolation is applied to recover the errors in the stresses [...] Read more.
The finite element error and adaptive analysis are implemented in finite element procedures to increase the reliability of numerical analyses. In this paper, the mesh-free error-recovery technique based on moving least squares (MLS) interpolation is applied to recover the errors in the stresses and displacements of incompressible elastic finite element solutions and errors are estimated in energy norms. The effects of element types (triangular and quadrilateral elements) and the formation of patches (mesh-free patch, mesh-dependent element-based patch, and mesh-dependent node-based patch) for error recovery in MLS and conventional least-square interpolation-error quantification are also assessed in this study. Numerical examples of incompressible elasticity, including a problem with singularity, are studied to display the effectiveness and applicability of the mesh-free MLS interpolation-error recovery technique. The mixed formulation (displacement and pressure) is adopted for a finite element analysis of the incompressible elastic problem. The rate of convergence, the effectivity of the error estimation, and modified meshes for desired accuracy are used to assess the effectiveness of the error estimators. The error-convergence rates are computed in the original FEM solution, in the post-processed solution using mesh-free MLS-based displacement, stress recovery, mesh-dependent patch-based least-square-based displacement, and stress recovery (ZZ) as (0.9777, 2.2501, 2.0012, 1.6710 and 1.5436), and (0.9736, 2.0869, 1.6931, 1.8806 and 1.4973), respectively, for four-node quadrilateral, and six-node triangular meshes. It is concluded that displacement-based recovery was more effective in the finite element incompressible elastic analysis than stress-based recovery using mesh-free and mesh-dependent patches. Full article
(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)
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11 pages, 910 KiB  
Article
Application of Adaptive Neuro-Fuzzy Inference Systems with Principal Component Analysis Model for the Forecasting of Carbonation Depth of Reinforced Concrete Structures
by Juan Liu and Xuewei Bai
Appl. Sci. 2023, 13(10), 5824; https://doi.org/10.3390/app13105824 - 09 May 2023
Cited by 3 | Viewed by 825
Abstract
The carbonation of reinforced concrete is one of the intrinsic factors that cause a significant decrease in service performance in concrete structures. To decrease the effect of carbonation-induced corrosion during the lifetime of the concrete structure, a prediction of carbonation depth should be [...] Read more.
The carbonation of reinforced concrete is one of the intrinsic factors that cause a significant decrease in service performance in concrete structures. To decrease the effect of carbonation-induced corrosion during the lifetime of the concrete structure, a prediction of carbonation depth should be made. The carbonation of concrete is affected by many factors, such as the compressive strength of the concrete, service life, carbonation time, carbon dioxide concentration, working stress, temperature, and humidity. On the basis of these seven parameters, combined with the predictive power of the adaptive network-based fuzzy inference system (ANFIS) and principal component analysis (PCA), which can reduce data dimensions before modeling, we introduced a novel approach—the PCA–ANFIS model—that can predict the carbonation of reinforced concrete. Practical engineering examples were adopted to verify the superiority of the suggested PCA–ANFIS model, with 90% of the carbonation depth data used for training and 10% used for testing. The root mean square error (RMSE) values for the ANFIS, ANN, PCA–ANN, and PCA–ANFIS training were 12.23, 6.28, 5.42, and 1.38, respectively. The results showed that the PCA–ANFIS model is accurate and can be used as a fundamental tool for predicting the service life of concrete structures. Full article
(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)
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