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Computational Modeling and Artificial Intelligence for Engineering Applications

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

Deadline for manuscript submissions: closed (30 March 2021) | Viewed by 31719

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Asst. Prof. Dr. Shu-Wei Chang

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Department of Civil Engineering, National Taiwan University, Taipei, 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

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Keywords

computational modeling
artificial intelligence
materials modeling
data analytics

Published Papers (11 papers)

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Research

15 pages, 1662 KiB

Open AccessArticle

A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

by Ali Sadeghi, Sajjad Amiri Doumari, Mohammad Dehghani, Zeinab Montazeri, Pavel Trojovský and Hamid Jafarabadi Ashtiani

Appl. Sci. 2021, 11(10), 4382; https://0-doi-org.brum.beds.ac.uk/10.3390/app11104382 - 12 May 2021

Cited by 12 | Viewed by 1653

Abstract

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms. Full article

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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16 pages, 503 KiB

Open AccessArticle

GBUO: “The Good, the Bad, and the Ugly” Optimizer

by Hadi Givi, Mohammad Dehghani, Zeinab Montazeri, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza and Nima Nouri

Appl. Sci. 2021, 11(5), 2042; https://doi.org/10.3390/app11052042 - 25 Feb 2021

Cited by 15 | Viewed by 2282

Abstract

Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the ugly” optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society’s movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer’s performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms. Full article

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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21 pages, 6770 KiB

Open AccessArticle

A Long/Short-Term Memory Based Automated Testing Model to Quantitatively Evaluate Game Design

by Lin-Kung Chen, Yen-Hung Chen, Shu-Fang Chang and Shun-Chieh Chang

Appl. Sci. 2020, 10(19), 6704; https://0-doi-org.brum.beds.ac.uk/10.3390/app10196704 - 25 Sep 2020

Cited by 2 | Viewed by 2240

Abstract

The mobile casual game application lifespan is getting shorter. A company has to shorten the game testing procedure to avoid being squeezed out of the game market share. There is no sufficient testing indicator to objectively evaluate the operability of different game designs. Many automated testing methodologies are proposed, but they adopt rule-based approaches and cannot provide quantitative analysis to statistically evaluate gameplay experience. This study suggests applying “Learning Time” as a testing indicator and using the learning curve to identify the operability of different game designs. This study also proposes a Long/Short-Term Memory based automated testing model (called LSTM-Testing) to statistically testing game experience through end-to-end functionality (Input: game image; Output: game action) without any manual intervention. The experiment results demonstrate LSTM-Testing can provide quantitative testing data by using learning time as the control variable, game design as the independent variable, and time to complete game as the dependent variable. This study also demonstrates how LSTM-Testing evaluates the effectiveness of different gameplay learning strategies, e.g., reviewing the newest decisions, reviewing the correct decision, or reviewing the wrong decisions. The contributions of LSTM-Testing are (1) providing an objective and quantitative analytical game-testing framework, (2) reducing the labor cost of inefficient and subjective manual game testing, and (3) allowing game company boosts software development by focusing on game intellectual property and leaves game testing to artificial intelligence (AI). Full article

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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14 pages, 3182 KiB

Open AccessArticle

Intelligent Control Based on a Neural Network for Aircraft Landing Gear with a Magnetorheological Damper in Different Landing Scenarios

by Quoc Viet Luong, Dae-Sung Jang and Jai-Hyuk Hwang

Appl. Sci. 2020, 10(17), 5962; https://0-doi-org.brum.beds.ac.uk/10.3390/app10175962 - 28 Aug 2020

Cited by 10 | Viewed by 2778

Abstract

A typical oleo-pneumatic shock-absorbing strut (classic traditional passive damper) in aircraft landing gear has a metering pin extending through the orifice, which can vary the orifice area with the compression and extension of the damper strut. Because the metering pin is designed in a single landing condition, the traditional passive damper cannot adjust its damping force in multiple landing conditions. Magnetorheological (MR) dampers have been receiving significant attention as an alternative to traditional passive dampers. An MR damper, which is a typical semi-active suspension system, can control the damping force created by MR fluid under the magnetic field. Thus, it can be controlled by electric current. This paper adopts a neural network controller trained by two different methods, which are genetic algorithm and policy gradient estimation, for aircraft landing gear with an MR damper that considers different landing scenarios. The controller learns from a large number of trials, and accordingly, the main advantage is that it runs autonomously without requiring system knowledge. Moreover, comparative numerical simulations are executed with a passive damper and adaptive hybrid controller under various aircraft masses and sink speeds for verifying the effectiveness of the proposed controller. The main simulation results show that the proposed controller exhibits comparable performance to the adaptive hybrid controller without any needs for the online estimation of landing conditions. Full article

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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23 pages, 6523 KiB

Open AccessArticle

The Next Failure Time Prediction of Escalators via Deep Neural Network with Dynamic Time Warping Preprocessing

by Zitong Zhou, Yanyang Zi, Jingsong Xie, Jinglong Chen and Tong An

Appl. Sci. 2020, 10(16), 5622; https://0-doi-org.brum.beds.ac.uk/10.3390/app10165622 - 13 Aug 2020

Cited by 4 | Viewed by 2382

Abstract

The escalator is one of the most popular travel methods in public places, and the safe working of the escalator is significant. Accurately predicting the escalator failure time can provide scientific guidance for maintenance to avoid accidents. However, failure data have features of short length, non-uniform sampling, and random interference, which makes the data modeling difficult. Therefore, a strategy that combines data quality enhancement with deep neural networks is proposed for escalator failure time prediction in this paper. First, a comprehensive selection indicator (CSI) that can describe the stationarity and complexity of time series is established to select inherently excellent failure sequences. According to the CSI, failure sequences with high stationarity and low complexity are selected as the referenced sequences to enhance the quality of other failure sequences by using dynamic time warping preprocessing. Secondly, a deep neural network combining the advantages of a convolutional neural network and long short-term memory is built to train and predict quality-enhanced failure sequences. Finally, the failure-recall record of six escalators used for 6 years is analyzed by using the proposed method as a case study, and the results show that the proposed method can reduce the average prediction error of failure time to less than one month. Full article

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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16 pages, 7103 KiB

Open AccessArticle

Numerical Estimation of Bonding Force of EPDM Grommet Parts with Hollow Shaft Geometry

by Dong-Seok Shin, Euy-Sik Jeon and Young-Shin Kim

Appl. Sci. 2020, 10(9), 3169; https://0-doi-org.brum.beds.ac.uk/10.3390/app10093169 - 01 May 2020

Cited by 1 | Viewed by 2381

Abstract

A grommet is a representative component that fixes the position of a cable. It is made from hyper-elastic materials (rubber), such as ethylene propylene diene monomer (EPDM). The grommet and cable are conventionally fixed through bonding; however, this method has numerous disadvantages that can be improved through relevant research. To apply a fixing method using the elastic force of EPDM rubber, this paper presents an empirical equation for approximating the bonding force of EPDM grommet parts with a hollow shaft geometry. First, tensile tests and the inverse method were used to approximate the basic mechanical properties. The physical properties were derived through basic tests; furthermore, bonding force tests and the inverse method were used on a grommet with a hollow shaft structure. In addition, the Box–Behnken design of experiments was used to predict the amount of change in the bonding force according to the geometry variables. Finally, this study was validated by comparing the approximation results derived through the design of experiments with the analysis and bonding force test results. Full article

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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18 pages, 6780 KiB

Open AccessArticle

Design and Verification of an Interval Type-2 Fuzzy Neural Network Based on Improved Particle Swarm Optimization

by Cheng-Jian Lin, Shiou-Yun Jeng, Hsueh-Yi Lin and Cheng-Yi Yu

Appl. Sci. 2020, 10(9), 3041; https://0-doi-org.brum.beds.ac.uk/10.3390/app10093041 - 27 Apr 2020

Cited by 11 | Viewed by 2321

Abstract

In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control. Full article

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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16 pages, 4226 KiB

Open AccessArticle

Simplified Theoretical Model for Temperature Evaluation in Tissue–Implant–Bone Systems during Ultrasound Diathermy

by Chang-Wei Huang

Appl. Sci. 2020, 10(4), 1306; https://0-doi-org.brum.beds.ac.uk/10.3390/app10041306 - 14 Feb 2020

Cited by 3 | Viewed by 2262

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 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

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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17 pages, 1700 KiB

Open AccessArticle

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

by Bin Zhang, Dawei Wu, Yingjie Song, Kewei Liu and Juxia Xiong

Appl. Sci. 2020, 10(2), 460; https://0-doi-org.brum.beds.ac.uk/10.3390/app10020460 - 08 Jan 2020

Cited by 2 | Viewed by 2842

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 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

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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17 pages, 3000 KiB

Open AccessArticle

Benchmarking Daily Line Loss Rates of Low Voltage Transformer Regions in Power Grid Based on Robust Neural Network

by Weijiang Wu, Lilin Cheng, Yu Zhou, Bo Xu, Haixiang Zang, Gaojun Xu and Xiaoquan Lu

Appl. Sci. 2019, 9(24), 5565; https://0-doi-org.brum.beds.ac.uk/10.3390/app9245565 - 17 Dec 2019

Cited by 7 | Viewed by 2431

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 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

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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22 pages, 7677 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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

by Mahdi Shariati, Mohammad Saeed Mafipour, Peyman Mehrabi, Alireza Bahadori, Yousef Zandi, Musab N A Salih, Hoang Nguyen, Jie Dou, Xuan Song and Shek Poi-Ngian

Appl. Sci. 2019, 9(24), 5534; https://0-doi-org.brum.beds.ac.uk/10.3390/app9245534 - 16 Dec 2019

Cited by 262 | Viewed by 6838

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 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

(This article belongs to the Special Issue Computational Modeling and Artificial Intelligence for Engineering Applications)

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