Participants matter: Effectiveness of VR-based training on the knowledge,trust in the robot,and self-efficacy of construction workers and university students

Virtual Reality (VR)-based training has gained attention from the scientific community in the Architecture, Engineering, and Construction (AEC) industry as a cost-effective and safe method that eliminates the safety risks that may impose on workers during the training compared to traditional training methods (e.g., in-person hands-on training, apprenticeship). Although researchers have developed VR-based training for construction workers, some have recruited students rather than workers to understand the effect of their VR-based training. However, students are different from construction workers in many ways, which can threaten the validity of such studies. Hence, research is needed to investigate the extent to which the findings of a VR-based training study are contingent on whether students or construction workers were used as the study sample. This paper strives to compare the effectiveness of VR-based training on university students’ and construction workers’ knowledge acquisition, trust in the robot, and robot operation self-efficacy in remote operation of a construction robot. Twenty-five construction workers and twenty-five graduate construction engineering students were recruited to complete a VR-based training for remote operating a demolition robot. We used quantitative analyses to answer our research questions. Our study shows that the results are dependent on the target sample in that students gained more knowledge, whereas construction workers gained more trust in the robot and more self-efficacy in robot operation. These findings suggest that the effectiveness of VR-based training on students may not necessarily associate with its effectiveness on construction workers.     

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.     

Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

Understanding the traffic patterns of construction workers on high-risk construction sites is important for implementing behaviour-based safety management. However, safety risks in worker trajectories are a complex system with high uncertainty. This resulted in few studies focusing on analysing the spatial–temporal risk in workers' trajectories from a systematic perspective. This study designs a new framework to explore the spatial–temporal patterns of safety risks in the trajectories of construction workers based on complex network theory. First, an integrated site safety risk classification method by combining hazard sources and group trajectory distribution is developed to accurately describe the spatial distribution of site risks. Second, a new complex network chronnet is used to construct complex networks containing risk information for spatial–temporal analysis. Finally, key risk areas and risk transition patterns are identified through the analysis of network measures. The study also developed a computational program that allows the network to be constructed and analysed in real-time. The feasibility and effectiveness of the method are verified through a case study. The results show that the method can reveal the risk distribution at the micro level, and explore the risk clustering and transition features in worker trajectories at the macro level. The study allows for an accurate analysis of dynamic risk patterns in construction workers' trajectories from a systematic perspective. It can also provide theoretical and practical support for personalized and adaptive behaviour-based safety management for construction workers.     

Measuring Project Management Performance: Case of Construction Industry

This study develops construction-specific determinants and indicators of project management performance based on the 14 knowledge areas of the project management body of knowledge guide. Interrelations among the knowledge areas and influence of these factors on performance are investigated. Data collected from 121 construction projects are analyzed using structural equation modeling. Research findings suggest that project integration, communications, safety, risk, human resources, financial, and cost management have a direct impact, whereas scope and time management have an indirect effect on performance. The findings reported in this study are expected to help construction project managers devise and implement strategies and develop actions to improve project success. An engineering manager can use the framework developed in this research to define the underlying factors of efficient project management practices.     

Critical factors and paths influencing construction workers’ safety risk tolerances

While workers’ safety risk tolerances have been regarded as a main reason for their unsafe behaviors, little is known about why different people have different risk tolerances even when confronting the same situation. The aim of this research is to identify the critical factors and paths that influence workers’ safety risk tolerance and to explore how they contribute to accident causal model from a system thinking perceptive. A number of methods were carried out to analyze the data collected through interviews and questionnaire surveys. In the first and second steps of the research, factor identification, factor ranking and factor analysis were carried out, and the results show that workers’ safety risk tolerance can be influenced by four groups of factors, namely: (1) personal subjective perception; (2) work knowledge and experiences; (3) work characteristics; and (4) safety management. In the third step of the research, hypothetical influencing path model was developed and tested by using structural equation modeling (SEM). It is found that the effects of external factors (safety management and work characteristics) on risk tolerance are larger than that of internal factors (personal subjective perception and work knowledge & experiences). Specifically, safety management contributes the most to workers’ safety risk tolerance through its direct effect and indirect effect; while personal subjective perception comes the second and can act as an intermedia for work characteristics. This research provides an in-depth insight of workers’ unsafe behaviors by depicting the contributing factors as shown in the accident causal model developed in this research.     

建筑工程施工成本监测预警及协调控制

通过对施工成本影响因素的识别,建立了施工成本监测与预警信号模型,以及施工成本协调控制模型等。     

The impact of drywall handling tools on the low back

Carpenters and other construction workers who install drywall have high rates of strains and sprains to the low back and shoulder. Drywall is heavy and awkward to handle resulting in increased risk of injury. The purpose of this study was to evaluate several low-cost coupling tools that have the potential to reduce awkward postures in drywall installers. Five coupling tools were evaluated using the Lumbar Motion Monitor that measures trunk kinematics and predicts probability of low back disorder group membership risk (LBD risk). Workers answered surveys about their comfort while using each tool. The results indicate that use of the 2-person manual lift and the J-handle provide the best reduction in awkward postures, motions, low back sagittal moment, and LBD risk. The two-person manual lift appears to be the safest method of lifting and moving drywall, though using the two-person J-handle also significantly reduces injury risk. Given that carpenters are skeptical about using equipment that can get in the way or get lost, a practical recommendation is promotion of two-person manual lifting. For single-person lifts, the Old Man tool is a viable option to decrease risk of MSDs.     

理想条件下混合态量子系统的Lyapunov稳定化策略

在假定被控系统满足非退化、没有退化跃迁和完全连通的理想条件下,借助于带有附加自由度的Lyapunov函数研究了混合态量子系统的稳定化问题.基于LaSalle原理推导了闭环系统的最大不变集和任一初始态下的收敛状态集,给出了系统对最大不变集中任一平衡态渐近稳定化的自由度的构造原则.最后通过一个两能级系统的数值仿真,验证了所得理论结果的正确性.     

基于词汇相似度的IPC与CLC映射

专利作为一种具有特殊性质的文献,包含先进的技术方案,但存在管理困难、相对孤立、使用率低等弊端。针对该问题,定义分类法类目的概念模型,通过计算类目之间的概念相似度,为国际专利分类法与中国图书分类法建立类目映射。在计算类目相似度中引入与类目相关的词汇语义相似度计算,综合考虑类目的上下文环境对类目间关系的影响,降低专利数据的孤立性,实现专利数据与其他期刊数据的交互操作。实验表明,该方法能有效提高类目间相似度计算的准确率。     

Best value concessionaire selection through a fuzzy logic system

Best value is the ultimate goal in the development of infrastructure projects through public private partnerships. This necessitates a best value approach in selecting the right private partner (i.e., the concessionaire) that involves evaluating alternative concessionaires against multiple conflicting criteria of varying degrees of importance and uncertainty. Correspondingly, this paper has developed a quantitative best value concessionaire selection system. The essence of this system lies in (1) the establishment of four-package evaluation criteria that can effectively measure the concessionaire’s capability and “predict” its future performance toward achieving the government’s best value objectives and (2) the development of a fuzzy-composite scoring and ranking method that ensures the right “tradeoff” between these criteria by relatively weighting fuzzy scores and logically aggregating evaluation results. This system would not only provide the government integrated assessments of alternative concessionaires so as to award the contract to the right concessionaire whose proposal is perceived to be able to maximize the outcome of the project, but also satisfy the requirements of the legal decision in order to withstand legal challenges concerning the public contract awarded in a best value approach. A hypothetical case study is provided to demonstrate the application of this best value concessionaire selection system.