Applied Sciences

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17 pages, 7851 KiB

Open AccessArticle

Numerical and Theoretical Analyses of Laminated Veneer Lumber Beams Strengthened with Fiber-Reinforced Polymer Sheets

by Michał Marcin Bakalarz and Paweł Grzegorz Kossakowski

Appl. Sci. 2024, 14(15), 6448; https://doi.org/10.3390/app14156448 - 24 Jul 2024

Viewed by 763

This study outlines a method of utilizing the finite element method and a simple mathematical model to predict the behavior of laminated veneer lumber (LVL) beams strengthened with composite sheets. The numerical models were created using the Abaqus 2017 software. The LVL was [...] Read more.

This study outlines a method of utilizing the finite element method and a simple mathematical model to predict the behavior of laminated veneer lumber (LVL) beams strengthened with composite sheets. The numerical models were created using the Abaqus 2017 software. The LVL was considered as a linearly elastic or elastic–plastic material, factoring in Hill’s yield criterion. The composites were simulated as linearly elastic–ideally plastic materials. The mathematical models were predicated on the methodology of transformed cross-section. The theoretical and numerical outcomes were juxtaposed with previous empirical investigations. The comparison encompassed load-bearing capacity, stiffness, and deformation under peak force. Furthermore, presentations of normal stress maps in the LVL and composite have been illustrated. The derived maps were juxtaposed with the delineations of failure modes. An adequate correlation was identified between the theoretical, numerical, and empirical values in the case of beams reinforced with aramid, glass, and carbon sheets. The relative deviation varied from several to multiple percentages. This technique is not applicable for evaluating load-bearing capacity and deformation when only dealing with sheets with low elongation of rupture. This is a consequence of their premature failure. The proposed models may be utilized by researchers and engineers in the design of reinforcements for timber structures. Full article

(This article belongs to the Special Issue Computer Methods in Mechanical, Civil and Biomedical Engineering)

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16 pages, 3491 KiB

Open AccessArticle

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 - 3 Apr 2024

Viewed by 3391

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

Open AccessArticle

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

Cited by 3 | Viewed by 1421

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

Open AccessArticle

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

Cited by 3 | Viewed by 1141

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

Open AccessArticle

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 - 9 Oct 2023

Cited by 5 | Viewed by 1380

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

Open AccessArticle

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 5 | Viewed by 1352

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

Open AccessArticle

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 - 7 Jun 2023

Cited by 2 | Viewed by 1317

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

Open AccessArticle

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 - 9 May 2023

Cited by 4 | Viewed by 1265

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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