Special Issue "Computational Modeling and Artificial Intelligence for Engineering Applications"

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 15 August 2020.

Special Issue Editor

Asst. Prof. Dr. Shu-Wei Chang
E-Mail Website
Guest Editor
Department of Civil Engineering, National Taiwan University, Taiwan
Interests: computational mechanics and materials; biomechanics; mechanobiology

Special Issue Information

Dear Colleagues,

In recent years, computational modeling and artificial intelligence have been employed widely by scientists to many engineering applications, including exciting advances in understanding the mechanical behaviors of synthetic and biological materials/composites and the development of novel materials, which demonstrate great potential in a wide range of engineering applications for the energy, construction, environment, and biomedical industry. Novel computational modeling techniques and artificial intelligence could lead to breakthroughs for the discovery of new materials and new methods for many engineering applications. The persisting growth of computational modeling and artificial intelligence has strongly reshaped the way scientists resolve and overcome engineering challenges. The integration of computational modeling and artificial intelligence has brought great opportunities for many fields. This Special Issue welcomes high-quality papers that report significant advances on the development and application of computational modeling and artificial intellegence for engineering problems.

Asst. Prof. Dr. Shu-Wei Chang
Guest Editor

Manuscript Submission Information

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Keywords

  • computational modeling
  • artificial intelligence
  • materials modeling
  • data analytics

Published Papers (4 papers)

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Research

Open AccessArticle
Simplified Theoretical Model for Temperature Evaluation in Tissue–Implant–Bone Systems during Ultrasound Diathermy
Appl. Sci. 2020, 10(4), 1306; https://doi.org/10.3390/app10041306 - 14 Feb 2020
Abstract
Deep heating procedures are helpful in treating joint contractures that frequently occur with fractures and joint diseases involving surgical implants and artificial joint prostheses. This study uses a one-dimensional composite medium model consisting of parallel slabs as a simplified approach to shed light [...] Read more.
Deep heating procedures are helpful in treating joint contractures that frequently occur with fractures and joint diseases involving surgical implants and artificial joint prostheses. This study uses a one-dimensional composite medium model consisting of parallel slabs as a simplified approach to shed light on the influences of implants during ultrasound diathermy. Analytical solutions for the one-dimensional transient heat generation and conduction problem were derived using the orthogonal expansion technique and a Green’s function approach. The analytical solutions provided deep insight into the temperature profile by therapeutic ultrasound heating in the composite system. The effects of the implant material type, tissue thickness, and ultrasound operation frequency on temperature distribution were studied for clinical application. In addition, sensitivity analyses were carried out to investigate the influences of material properties on the temperature distribution during ultrasound diathermy. Based on the derived analytical solutions, the numerical simulations indicate that materials with high density, high specific heat, and low thermal conductivity may be optimal implant materials. Among available implant materials, a tantalum implant, which can achieve a lower temperature rise within the tissue (hydrogel) and bone layers during ultrasound diathermy, is a better choice thanks to its thermodynamics. Full article
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Open AccessArticle
A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem
Appl. Sci. 2020, 10(2), 460; https://doi.org/10.3390/app10020460 - 08 Jan 2020
Abstract
With the rapid economic development, manufacturing enterprises are increasingly using an efficient workshop production scheduling system in an attempt to enhance their competitive position. The classical workshop production scheduling problem is far from the actual production situation, so it is difficult to apply [...] Read more.
With the rapid economic development, manufacturing enterprises are increasingly using an efficient workshop production scheduling system in an attempt to enhance their competitive position. The classical workshop production scheduling problem is far from the actual production situation, so it is difficult to apply it to production practice. In recent years, the research on machine scheduling has become a hot topic in the fields of manufacturing systems. This paper considers the batch processing machine (BPM) scheduling problem for scheduling independent jobs with arbitrary sizes. A novel fast parallel batch scheduling algorithm is put forward to minimize the makespan in this paper. Each of the machines with different capacities can only handle jobs with sizes less than the capacity of the machine. Multiple jobs can be processed as a batch simultaneously on one machine only if their total size does not exceed the machine capacity. The processing time of a batch is determined by the longest of all the jobs processed in the batch. A novel and fast 4.5-approximation algorithm is developed for the above scheduling problem. For the special case of all the jobs having the same processing times, a simple and fast 2-approximation algorithm is achieved. The experimental results show that fast algorithms further improve the competitive ratio. Compared to the optimal solutions generated by CPLEX, fast algorithms are capable of generating a feasible solution within a very short time. Fast algorithms have less computational costs. Full article
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Open AccessArticle
Benchmarking Daily Line Loss Rates of Low Voltage Transformer Regions in Power Grid Based on Robust Neural Network
Appl. Sci. 2019, 9(24), 5565; https://doi.org/10.3390/app9245565 - 17 Dec 2019
Abstract
Line loss is inherent in transmission and distribution stages, which can cause certain impacts on the profits of power-supply corporations. Thus, it is an important indicator and a benchmark value of which is needed to evaluate daily line loss rates in low voltage [...] Read more.
Line loss is inherent in transmission and distribution stages, which can cause certain impacts on the profits of power-supply corporations. Thus, it is an important indicator and a benchmark value of which is needed to evaluate daily line loss rates in low voltage transformer regions. However, the number of regions is usually very large, and the dataset of line loss rates contains massive outliers. It is critical to develop a regression model with both great robustness and efficiency when trained on big data samples. In this case, a novel method based on robust neural network (RNN) is proposed. It is a multi-path network model with denoising auto-encoder (DAE), which takes the advantages of dropout, L2 regularization and Huber loss function. It can achieve several different outputs, which are utilized to compute benchmark values and reasonable intervals. Based on the comparison results, the proposed RNN possesses both superb robustness and accuracy, which outperforms the testing conventional regression models. According to the benchmark analysis, there are about 13% outliers in the collected dataset and about 45% regions that hold outliers within a month. Hence, the quality of line loss rate data should still be further improved. Full article
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Open AccessFeature PaperArticle
Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete
Appl. Sci. 2019, 9(24), 5534; https://doi.org/10.3390/app9245534 - 16 Dec 2019
Cited by 2
Abstract
Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly [...] Read more.
Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly but also time-consuming. Moreover, the impact of other parameters cannot be easily seen in the behavior of the connectors. This paper aims to investigate the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC). To generate the required data, an experimental project was conducted. Dimensions of the channel connectors and the compressive strength of concrete were adopted as the inputs of the model, and load and slip were predicted as the outputs. To evaluate the ANN-PSO model, an ANN model was also developed and tuned by a backpropagation (BP) learning algorithm. The results of the paper revealed that an ANN model could properly predict the behavior of channel connectors and eliminate the need for conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices. Full article
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