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This paper aims to inquire into the awareness of Vietnamese architects about design for safety (DfS) and the level of engagement in applying DfS among them to get a generic view of the implementation of DfS in Vietnam.

Quantitative research was used, in which a questionnaire was sent to Vietnamese architects to evaluate how they consider and apply DfS in the design process. Inferential and descriptive statistics then analysed the obtained data to identify the role of each factor.

The results from the survey conclude that Vietnamese architects have low engagement in applying DfS despite their high awareness and positive attitude towards DfS. Besides, the participants showed the need for further DfS education and training, which is lacking in Vietnamese formal education. In addition, the research also confirms that DfS education and training have positive impacts on the frequency of DfS implementation in Vietnam.

This research contributes to the knowledge of DfS implementation in developing countries. In line with this, further studies on the DfS concept in developing countries are needed to draw a more objective overview and give the solution for the low DfS appliance.

To the best of the authors’ knowledge, this is the first study inquiring into the implication of DfS in Vietnam, contributing to improving the lack of knowledge in this field in developing countries and Vietnam in particular.

This paper aims to investigate the relationships between empowering leadership, basic psychological needs satisfaction, work-related well-being, and project citizenship behavior.

Drawing upon the self-determination theory (SDT), a conceptual model was developed and then empirically tested using a cross-sectional survey of 435 project members in Chinese construction projects.

The results fully support the research hypotheses proposed in the study, illustrating the positive impacts of empowering leadership on work-related well-being and project citizenship behavior, the mediating role of basic psychological needs satisfaction, and the positive association between work-related well-being and project citizenship behavior.

This research determines the utility of empowering leadership in the context of construction projects, especially in enhancing individual outcomes (i.e. work-related well-being and project citizenship behavior). Therefore, construction project managers can apply empowering leadership to meet the basic psychological needs of subordinates to increase project members' work-related well-being and project citizenship behavior.

To our knowledge, the present study first explores the micro-level impacts of empowering leadership in the construction context. Additionally, this study enriches the understanding of the mediating mechanism between empowering leadership and individual outcomes from a self-determination perspective.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

The purpose of this paper is to summarize the research of construction conflict from 1991 to 2020 and propose research directions for future scholarly work. During the recent decades, it is widely accepted that construction conflict is inevitable, and conflict management has become an important component of project management. However, few works were done to map the global study in this field, there is limited review that evaluates the current stage of construction conflict research.

This study adopted a holistic literature review approach that incorporates bibliometric search and scientometric analysis. A total of 698 bibliographic records from the Web of Science core collection database were collected for the scientometric analysis. CiteSpace5.7 was adopted for the science mapping purpose in this study.

Through co-authorship analysis, co-word analysis and co-citation analysis, influential scholars and journals are identified. Several research trends are highlighted according to the scientometric analyses of the construction conflict topics. For example, the application of simulation and algorithms to the study of construction conflict management systems.

Construction is a resource-intensive, multi-participant and multi-targeted industry. Conflicts always exist in the whole life cycle of construction projects, it is important for industry practitioners to be updated of the latest movement and progress of the academic research.

This study contributed to the body of knowledge in construction conflict and bridge the research gap in the thorough review of previous research work.

The purpose of this study is to address the extended travel time caused by dwelling time at stations for passengers on traditional rail transit lines. To mitigate this issue, the authors propose the “Non-stop” design, which involves trains comprised of modular vehicles that can couple and uncouple from each other during operation, thereby eliminating dwelling time at stations..

The main contributions of this paper are threefold: first, to introduce the concept of non-stop rail transit lines, which, to the best of the authors’ knowledge, has not been researched in the literature; second, to develop a framework for the operation schedule of such a line; and third, the author evaluate the potential of its implementation in terms of total passenger travel time.

The total travel time was reduced by 6% to 32.91%. The results show that the savings were more significant for long commutes and low train occupancy rates.

The non-stop system can improve existing lines without the need for the construction of additional facilities, but it requires technological advances for rolling stock.

To eliminate dwelling time at stations, the authors present the “Non-stop” design, which is based on trains composed of locomotives that couple and uncouple from each other during operation, which to the best of the authors’ knowledge has not been researched in the literature.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

International construction joint ventures (ICJVs) have been widely used as a temporary arrangement in many projects all over the world, especially in megaprojects. Within ICJVs, the national culture difference between partners affects their cooperation significantly. However, prior research has provided contradictory empirical evidence regarding these impacts. To address this problem, the purpose of this paper is to introduce cultural intelligence that judges an individual’s capability to function and manage effectively in culturally diverse settings as a moderating variable.

Multiple regression analysis and moderated multiple regression were undertaken to test proposed hypotheses. A questionnaire survey was conducted with international construction practitioners who had experiences of managing or participating in ICJVs.

The result of multiple regression analysis revealed that difference in national culture has significant negative effects on information exchange, shared problem solving and flexibility when asking for changes, thus effecting cooperation within ICJVs. Meanwhile, cultural intelligence of members can weaken these negative influences.

First, given that national culture difference affects negatively on the cooperation within ICJVs, it needs to be regarded as one of vital resources of risk which should be prevented and managed when attending ICJVs; Second, managers should build a series of mutually agreeable regulations and rules to lessen the negative effect of national culture difference; Third, it is recommended that ICJV management teams contain as many work-experienced members as possible and members within ICJVs, especially new staff, receive cross-cultural training termly to facilitate the cooperation between partners.

This research reveals the moderating effects of cultural intelligence on the relationship between national culture difference and cooperation in ICJVs as well as provides practical implications for ICJV managers to deal with national culture difference and reduce its negative impact on cooperation within ICJVs.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

The purpose of this paper is to study the effect of micro-nano surface texture on the corrosion resistance of a titanium alloy and investigate the correlation between corrosion resistance and hydrophobicity.

The surface of the Ti₆Al₄V alloy was modified by laser processing and anodizing to fabricate micro-pits, nanotubes and micro-nano surface textures. Afterward, the surface morphology, hydrophobicity and polarization curve of the samples were analyzed by cold field scanning electron microscopy, contact angle measurement instruments and a multi-channel electrochemical workstation.

The micro-nano surface texture can enhance the hydrophobicity of the Ti₆Al₄V surface, which may lead to better drag reduction to ease the friction of implants in vivo. Nevertheless, no correlation existed between surface hydrophobicity and corrosion resistance; the corrosion resistance of samples with nanotubes and high-density samples with micro-nano surface texture was extremely enhanced, indicating the similar corrosion resistance of the two.

The mechanism of micro-dimples on the corrosion resistance of the micro-nano surface texture was not studied.

The density of micro-pits needs to be optimized to guarantee excellent corrosion resistance in the design of the micro-nano surface texture; otherwise, it will not fulfill the requirement of surface modification.

The influence of the micro-nano surface texture on the corrosion resistance, as well as the relationship between hydrophobicity and corrosion resistance of the titanium alloy surface, were systematically investigated for the first time. These conclusions offer new knowledge.