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Maintainability is a critical design characteristic that shows how well a product can be maintained; maintenance time is a comprehensive parameter of product maintainability design. This paper aims to provide an integrated methodology for complex product maintainability verification and maintenance time prediction using virtual prototypes and humans in a virtual dynamic simulation of the maintenance process.

An integrated platform for maintainability verification and maintenance time prediction is designed. Decomposition of maintenance tasks, corrective measurement time method, and an impact matrix of maintenance therbligs and time are presented.

The proposed methodology can efficiently conduct complex product maintainability verification and maintenance time prediction.

Early and effective verification and prediction of the maintainability and maintenance time program can significantly improve the maintainability and availability of a complex product.

A universally applicable method for product maintainability verification and maintenance time prediction is presented.

Maintainability, as an intrinsic property that shows how well a product can be maintained, should be strictly controlled in the design stage. Maintenance space is an important aspect of maintainability and should be verified in the design stage. Methods to verify maintenance space based on expert knowledge or vision cone have been proposed. However, no proper quantitative solutions have been proposed yet to verify maintenance space in the design stage. This paper aims to provide a new method to evaluate quantitatively the maintenance space in a virtual environment by using the swept volume (SV).

An integrated platform for quantitatively evaluating maintenance space in a virtual environment is designed. Virtual reality technology and digital prototype are used to overcome the shortage of physical prototypes in the design stage. SVs are applied to represent the movement of the hand along the entire maintenance process. Maintenance operations are divided into three basic types. Each type of basic operation refers to two quantitative indexes, namely the surface area and the volume of the SV of the maintenance personnel. Data on the indexes are gathered and calculated to evaluate maintenance space.

The proposed methodology can evaluate feasibly and efficiently the maintenance space determined by the layout design of product components in the design stage.

Application of the proposed method can help designers reduce the shortness of maintenance space and improve the design layout of products.

A method to evaluate maintenance space that employs a quantitative combination of virtual environment and swept volume is pressed.

This paper aims to analyze and provide insight on the algorithms for the optimization of construction site layout planning (CSLP). It resolves problems, such as the selection of suitable algorithms, considering the optimality, optimization objectives and representation of layout solutions. The approaches for the better utilization of optimization algorithms are also presented.

To achieve the above, existing records (results = 200) were selected from three databases: Web of Science, ScienceDirect and Scopus. By implementing a systematic protocol, the articles related to the optimization algorithms for the CLSP (results = 75) were identified. Moreover, various related themes were collated and analyzed according to a coding structure.

The results indicate the consistent and increasing interest on the optimization algorithms for the CLSP, revealing that the trend in shifting to smart approaches in the construction industry is significant. Moreover, the interest in metaheuristic algorithms is dominant because 65.3% of the selected articles focus on these algorithms. The optimality, optimization objectives and solution representations are also important in algorithm selection. With the employment of other algorithms, self-developed applications and commercial software, optimization algorithms can be better utilized for solving CSLP problems. The findings also identify the gaps and directions for future research.

The selection of articles in this review does not consider the industrial perspective and practical applications of commercial software. Further comparative analyses of major algorithms are necessary because this review only focuses on algorithm types.

This paper presents a comprehensive systematic review of articles published in the recent decade. It significantly contributes to the demonstration of the status and selection of CLSP algorithms and the benefit of using these algorithms. It also identifies the research gaps in knowledge and reveals potential improvements for future research.

This paper aims to propose a novel model and calibration method to improve the absolute positioning accuracy of a robotic drilling system with secondary encoders and additional axis.

The enhanced rigid-flexible coupling model is developed by considering both kinematic parameters and link flexibility. The kinematic errors of the robot and the additional axis are considered with a model containing 27 parameters. The elastic deformation errors of the robot under self-weight of links and end-effector are estimated with a flexible link model. For calibration, an effective comprehensive calibration method is developed by further considering the coordinate systems parameters of the drilling system and using a two-step process constrained Levenberg–Marquardt identification method.

Experiments are performed on the robotic drilling system that contains a KUKA KR500 R2830 industrial robot and an additional lifting axis with a laser tracker. The results show that the maximum error and mean error are reduced to 0.311 and 0.136 mm, respectively, which verify the effectiveness of the model and the calibration method.

A novel enhanced rigid-flexible coupling model and a practical comprehensive calibration method are proposed and verified. The experiments results indicate that the absolute positioning accuracy of the system in a large workspace is greatly improved, which is conducive to the application of industrial robots in the field of aerospace assembly.

This study aims to propose a calibration method to enhance the positioning accuracy in dual-robot collaborative operations, aiming to address the challenge of drilling hole spacing errors in spacecraft core cabin brackets that require an accuracy of less than 0.5 mm.

Initially, the cooperative error of dual robots is defined. Subsequently, an integrated model is constructed that encompasses the kinematic model errors of the dual robots, as well as the establishment errors of the base and tool frames. A calibration method for optimizing the cooperative accuracy of dual robots is proposed.

The application of the proposed method satisfies the collaborative drilling requirements for the spacecraft core cabin. The average cooperative positioning error of the dual robots was reduced from 0.507 to 0.156 mm, with the maximum value and standard deviation decreasing from 1.020 and 0.202 mm to 0.603 and 0.097 mm, respectively. Drilling experiments conducted on a core cabin simulator demonstrated that after calibration, the maximum hole spacing error was reduced from 1.219 to 0.403 mm, with all spacing errors falling below the 0.5 mm threshold, thus meeting the requirements.

This paper addresses the drilling accuracy requirements for spacecraft core cabins by using a calibration method to reduce the cooperative error of dual robots. The algorithm has been validated through experiments using ER 220 robots, confirming its effectiveness in fulfilling the drilling task requirements.

The purpose of the study is to examine the psychological impact of COVID-19 on health workers' career satisfaction and intention to leave the health profession, with neurotic personality type as a moderator.

A total of 277 health workers in two public hospitals in Ghana were included in this study. Purposive and convenience sampling techniques were adopted for the study, focusing on eight departments that were involved in the management of COVID-19 cases. Validated instruments were used to measure burnout, intention to leave, neurotic personality and career satisfaction. Using AMOS and partial least squares structural equation modeling (PLS-SEM), various techniques were employed to analyze mediating and moderating mechanisms.

The departments had staff sizes ranging from 19 to 40, with 67% female and 33% male, with an average age of 31. Nurses accounted for the majority of responses (67.8%), followed by physicians (13.9%), sonographers (0.9%), lab technicians (0.9%) and other respondents (16.5%). The study found that health workers’ level of burnout during COVID-19 had a positive effect on their intention to leave the health profession. Career satisfaction does not mediate this relationship; however, career satisfaction negatively influences the intention to leave the health profession. A neurotic personality does not moderate this relationship.

This study provides validation of burnout and intention to leave among health workers in Ghana during COVID-19 and supports the proposition that threats to resources (burnout) and having a resource (career satisfaction) have effects on the intention to leave one’s profession.

Sinusoidal signals are often used as the inputs of the six degree of freedom (DOF) motion simulator platforms. The purpose of this paper is to propose a fuzzy incremental controller (FIC) to improve sinusoidal signal tracking performances of an electrohydraulic Gough-Stewart platform (GSP).

An FIC is proposed to control an electrohydraulic GSP without any model parameters. The FIC output can be self-organized by only using the hydraulic actuator position information. The control rules are determined by a systematic deterministic method.

Experimental results show that the proposed FIC is valid and can achieve better tracking performances compared with classical PID controller and a decoupling controller (a model-based controller).

An FIC using a systematic deterministic rule-base determination method is proposed to improve sinusoidal signal tracking performances of electrohydraulic GSP.

Various approaches have emerged to assist practitioners in making more informed decisions in highway construction projects. However, industry practitioners are still using subjective ways to make decisions. Also, researchers have developed tools and techniques with similar objectives. Lack of information on what has been developed might lead to those issues. Therefore, this paper aims to review trends of evolution, pinpoint strengths and gaps in the literature and identifies potential future directions for decision-making research in highway construction projects.

A systematic review was conducted on published articles on decision-making in highway construction projects using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique.

The analysis of 101 articles revealed that existing decision-making research in highway construction projects targets improvements in four areas: feasibility, conceptual, detailed scope and detailed design. The four areas consist of sixteen subthemes that are detailed in this study. In addition, most research involved developing decision support tools and systems as well as decision-making models, techniques and frameworks. Lastly, several research areas have emerged, such as adding more decision criteria including those with uncertainties, expanding existing decision-making models into decision support systems, benchmarking decision criteria between different sample populations and exploring inter-and intra-relationships between decision criteria.

This paper provides an overview of existing research on decision-making in highway construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research. Finally, industry practitioners can use the findings to develop strategies for effective decision-making processes.

The purpose of this paper is to develop a comprehensive understanding of the public safety risks of international construction projects (ICPs) from the perspective of threat and vulnerability. A novel and comprehensive risk assessment approach is developed from a systemic perspective and applied to the Belt and Road Initiative (BRI) to improve the public safety risk management strategy for ICPs in BRI.

First, a public safety risk indicator system was constructed from the two dimensions, namely threat and vulnerability. Next, an integrated measurement model was constructed by combining the Genetic Algorithm-Backpropagation (GA-BP) neural network, fuzzy comprehensive evaluation method and matter-element extension (MME) method. Data from 49 countries involved in the BRI, as well as five typical projects, were used to validate the model. Finally, targeted risk prevention measures were identified for use at the national, enterprise and project levels.

The findings indicate that while the vulnerability risks of typical projects in each region of the BRI were generally low, threat risks were high in West Asia and North Africa, Commonwealth of Independent States (CIS) countries and South Asia.

First, the structure of the public safety risk system of ICPs was analyzed using vulnerability and system theories. The connotation of public safety risk was defined based on two dimensions, namely threat and vulnerability. The idea of measuring threat risk with public data and measuring vulnerability risk with project data was clarified, and the risk measurement was integrated into the measurement results to help researchers and managers understand and systematically consider the public safety risks of ICPs. Second, a public safety risk indicator system was constructed, including 18 threat risk indicators and 14 vulnerability risk indicators to address the gaps in the existing research. The MEE model was employed to overcome the problem of incompatible indicator systems and provide stable and credible integrated measurement results. Finally, the whole-process public safety risk management scheme designed in this study can help to both provide a reference point for the Chinese enterprises and oversea contractors in market selection as well as improve ICP public safety risk management.

Trajectory tracking is a common problem in the field of mobile robots which has attracted a lot of attention in the past two decades. Therefore, besides the search for new controllers to achieve a better performance, improvement and optimization of existing control rules are necessary. Trajectory tracking control laws usually contain constant gains which affect greatly the robot’s performance.

In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.

Simulations and experiments are performed to assess the ability of the suggested scheme. The obtained results show the effectiveness of the proposed method.

In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.