• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Steel and Composite Structures
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• v.44 no.2
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• pp.271-281
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• 2022

This study discusses challenges of running expandable profile liners (EPLs) to isolate trouble zones in directional section of a deep well, and summary the expandable profile liner technology (EPLT) field trial experience. Technically, the trial result reveals that it is feasible to apply the EPLT solving lost-circulation control problem and wellbore instability in the deep directional section. Propose schemes for optimizing the EPLT operation procedure to break through the existing bottleneck of EPLT in the deep directional section. Better-performing transition joints are developed to improve EPL string reliability in high borehole curvature section. High-performing and reliable expanders reduce the number of trips, offer excellent mechanical shaping efficiency, simplify the EPLT operation procedure. Application of the expansion and repair integrated tool could minimize the risk of insufficient expansion and increase the operational length of the EPL string. The new welding process and integrated automatic welding equipment improve the welding quality and EPL string structural integrity. These optimization schemes and recent new advancements in EPLT can bring significant economic benefits and promote the application of EPLT to meet future challenges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Journal of Powder Materials
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• v.30 no.3
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• pp.203-209
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• 2023

Aluminum alloy-based additive manufacturing (AM) has emerged as a popular manufacturing process for the fabrication of complex parts in the automotive and aerospace industries. The addition of an inoculant to aluminum alloy powder has been demonstrated to effectively reduce cracking by promoting the formation of equiaxed grains. However, the optimization of the AM process parameters remains challenging owing to their variability. In this study, the response surface methodology (RSM) was used to predict the crack density of AM-processed Al alloy samples. RSM was performed by setting the process parameters and equiaxed grain ratio, which influence crack propagation, as independent variables and designating crack density as a response variable. The RSM-based quadratic polynomial models for crack-density prediction were found to be highly accurate. The relationship among the process parameters, crack density, and equiaxed grain fraction was also investigated using RSM. The findings of this study highlight the efficacy of RSM as a reliable approach for optimizing the properties of AM-processed parts with limited experimental data. These results can contribute to the development of robust AM processing strategies for the fabrication of high-quality Al alloy components for various applications.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.385-393
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• 2022

Previous studies postulated insignificant capillary rise (h_c) effect above the water table (H_w) for unsaturated soils. In addition, these studies utilised dry unit weight above H_w. This paper, therefore, addresses the effect of these postulations on strength where the influence of h_c using a modified upper bound approach, Discontinuity Layout Optimization (UNSAT-DLO) for a simulated soil was predicted. Two different parametric studies to model passive earth pressure and bearing capacity problems are carried out to provide an insight into the effect of capillary rise on strength. Significant increase in strength, owing to unsaturated conditions, was obtained where the maximum increase was when suction slightly less or greater than the air entry suction. On the other hand, the results showed a negative effect of h_c. For example, up to 8.24% decrease in passive thrust (P_p) was obtained at H_w=0 m when h_c rose 1 m from 0 m. To put this into perspective, this was equivalent to a decrease of about 2° in 𝜙 at H_w=0 m and h_c =0 m in order to obtain the same result at h_c =1 m. For the bearing capacity problem, the effect was seen to be higher, up to 18.4% decrease in N_𝛾 was obtained when h_c rose from 0 m to 2.5 m at H_w =0 m. In addition, the results revealed a negative influence of assigning dry unit weight above H_w or h_c.. However, considerable increase in strength was obtained when unsaturated unit weight above h_c was assigned.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.343-352
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• 2020

Modular integrated construction (MiC) is an innovative construction method where components of a building are manufactured in an offsite factory, trucked to the job site in sections, set in place with cranes, and assembled together to form a whole building. Where circumstances merit, favorable conditions exist and implemented effectively; MiC improves project performance. However, several key factors need to converge during implementation to realize the full benefits of MiC. Thus, a thorough understanding of the factors which are critical to the success of MiC projects is imperative. Drawing on a systematic review of 47 empirical studies, this research identified 25 key success factors (KSFs) for MiC projects. Of these, the five topmost cited KSFs for MiC projects include effective working collaboration and communication among project participants; standardization, optimization, automation and benchmarking of best practices; effective supply chain management; early design freeze and completion; and efficient procurement method and contracting. The study further proposed a conceptual model of the KSFs, highlighting the interdependences of people, processes, and technology-related KSFs for the effective accomplishment of MiC projects. The set of KSFs is practically relevant as they constitute a checklist of items for management to address and deal with during the planning and execution of MiC projects. They also provide a useful basis for future empirical studies tailored towards measuring the performance and success of MiC projects. MiC project participants and stakeholders will find this research useful in reducing failure risks and achieving more desired performance outcomes. One potential impact of the study is that it may inform, guide, and improve the successful implementation of MiC projects in the construction industry. However, the rigor of the analysis and relative importance ranking of the KSFs were limited due to the absence of data.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.569-582
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• 2023

Lead-acid Battery is the oldest rechargeable battery system and has maintained its position in the rechargeable battery field. The battery causes thermal runaway for various reasons, which can lead to major accidents. Therefore, preventing thermal runaway is a key part of the battery management system. Recently, research is underway to categorize thermal runaway battery cells into machine learning. In this paper, we present a thermal runaway hazard cell detection and verification algorithm using DBSCAN and statistical method. An experiment was conducted to classify thermal runaway hazard cells using only the resistance values as measured by the Battery Management System (BMS). The results demonstrated the efficacy of the proposed algorithms in accurately classifying thermal runaway cells. Furthermore, the proposed algorithm was able to classify thermal runaway cells between thermal runaway hazard cells and cells containing noise. Additionally, the thermal runaway hazard cells were early detected through the optimization of DBSCAN parameters using a grid search approach.

• Advances in aircraft and spacecraft science
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• v.10 no.3
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• pp.223-243
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• 2023

There is a burgeoning demand for minimizing the mass of satellites because of its direct impact on reducing launch-to-orbit cost. This must be done without compromising the structure's efficiency. The present paper introduces a relatively low-cost and easily implementable approach for optimizing structural mass to a maximum natural frequency. The natural frequencies of the satellite are of utmost pertinence to the application requirements, as the sensitive electronic instrumentation and onboard computers should not be affected by the vibrations of the satellite structure. This methodology is applied to a realistic model of Al-Azhar University micro-satellite in partnership with the Egyptian Space Agency. The procedure used in structural design can be summarized in two steps. The first step is to select the most favorable primary structural configuration among several different candidate variants. The nominated variant is selected as the one scoring maximum relative dynamic stiffness. The second step is to use perforation patterns reduce the overall mass of structural elements in the selected variant without changing the weight. The results of the presented procedure demonstrate that the mass reduction percentage was found to be 39% when compared to the unperforated configuration that had the same plate thickness. The findings of this study challenge the commonly accepted notion that isogrid perforations are the most effective means of achieving the goal of reducing mass while maintaining stiffness. Rather, the study highlights the potential benefits of exploring a wider range of perforation unit cells during the design process. The study revealed that rectangular perforation patterns had the lowest efficiency in terms of modal stiffness, while triangular patterns resulted in the highest efficiency. These results suggest that there may be significant gains to be made by considering a broader range of perforation shapes and configurations in the design of lightweight structures.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.15-24
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• 2024

One of the most significant issues in combinatorial optimization is the classical NP-complete conundrum known as the 0/1 Knapsack Problem. This study delves deeply into the investigation of practical solutions, emphasizing two classic algorithmic paradigms, brute force, and dynamic programming, along with the metaheuristic and nature-inspired family algorithm known as the Genetic Algorithm (GA). The research begins with a thorough analysis of the dynamic programming technique, utilizing its ability to handle overlapping subproblems and an ideal substructure. We evaluate the benefits of dynamic programming in the context of the 0/1 Knapsack Problem by carefully dissecting its nuances in contrast to GA. Simultaneously, the study examines the brute force algorithm, a simple yet comprehensive method compared to Branch & Bound. This strategy entails investigating every potential combination, offering a starting point for comparison with more advanced techniques. The paper explores the computational complexity of the brute force approach, highlighting its limitations and usefulness in resolving the 0/1 Knapsack Problem in contrast to the set above of algorithms.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.113-122
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• 2024

This comprehensive study delves into the analysis of user review data across various metaverse platforms, employing advanced text mining techniques such as TF-IDF and Word2Vec to gain insights into user perceptions. The primary objective is to uncover the factors that contribute to user satisfaction and dissatisfaction, thereby providing a nuanced understanding of user experiences in the metaverse. Through TF-IDF analysis, the research identifies key words and phrases frequently mentioned in user reviews, highlighting aspects that resonate positively with users, such as the ability to engage in creative activities and social interactions within these virtual environments. Word2Vec analysis further enriches this understanding by revealing the contextual relationships between words, offering a deeper insight into user sentiments and the specific features that enhance their engagement with the platforms. A significant finding of this study is the identification of common grievances among users, particularly related to the processes of refunds and login, which point to broader issues within payment systems and user interface designs across platforms. These insights are critical for developers and operators of metaverse platforms, suggesting a focused approach towards enhancing user experiences by amplifying positive aspects. The research underscores the importance of continuous improvement in user interface design and the transparency of payment systems to foster a loyal user base. By providing a comprehensive analysis of user reviews, this study offers valuable guidance for the strategic development and optimization of metaverse platforms, ensuring they remain responsive to user needs and continue to evolve as vibrant, engaging virtual environments.