This theoretical study encompasses the role of digitalization in visual management (VM) applied in construction projects to increase the situational awareness (SA) of construction workers and crews. A literature review on VM, SA and self-managed crews shows that the level of digitalization in the construction industry is low when compared to other industries, that information silos are a common practice and that the decision-making processes depend largely on the construction manager role, causing a bottleneck of information and repeated time waste. As more digitalization enters construction sites and the amount and quality of available data increases, appropriate use of this data can bring meaningful improvement to construction site management. The study shows that using updated and real-time data for VM devices can increase information flow among construction workers and crews, increasing SA throughout the construction project. This can enable the construction crews to be more autonomous and self-managed, resulting in decentralized decision-making processes to solve task-related problems. Further steps for empirical research are suggested.
This paper presents the concept for the integration of Indoor Positioning System (IPS) and Building Information Model (BIM), and hypothesizes about the possible benefits of this integration to situational awareness and visual management in construction projects.Literature review shows that the volume and quality of data enabling situational awareness during construction projects is increasing with the use of new technologies, such as indoor positioning systems and other applications of Internet of Things (IoT). However, these information streams have been used individually so far. BIM as the interface integrating different streams of situational awareness information can result in better data-driven construction management and production. This study suggests that using BIM in 3D visualization of the indoor positioning of construction resources (workers, material, and equipment) enables visual management based on situation awareness on construction project activities. Better situational awareness of construction resources on-site based on visualization in BIM can improve the identification and elimination of waste and the identification of workflow interruptions, potentially permitting better planning and increasing productivity.The study suggests further steps for empirical research to prototype the concept and validate it with industry partners and practitioners.
In the construction industry, digitalisation has led to increasing efforts to improve construction management using digital visual management (VM) devices. Although the amount of research on digital VM (DVM) in the design phase and in the management of construction sites has also increased, its implementation during the production phase and by construction crews remains limited. The objective of this study is to explore the adoption of DVM in construction sites, assess construction workers’ experiences regarding digital and analogue VM devices, and understand the challenges that hinder the adoption of such devices. This study used a mixed method approach, combining qualitative and quantitative research. Data included visual site explorations, surveys of construction workers and crew managers, and unstructured interviews with site managers and development directors to assess the use of DVM devices in construction sites, the need for them and their current implementation. The findings showed that VM should be conveniently located near the job site instead of the office site, which is the current situation. Construction crews who experienced more production and schedule disruptions reported that VM supported their work, compared with crews that had fewer disruptions. VM devices on construction sites are analogue, and their usage continues to be in construction site management, which perpetuates information silos during construction projects. The findings of this study provide insights into the development and deployment of DVM devices on construction sites. Construction workers’ need for visual information close at hand is of interest to both scholars and practitioners in future research and development.
Takt production is gaining increasing visibility in the construction industry. To further improve the current takt production practices, visual management tools could offer improved efficiency in the production control phase. However, the effects of visual management in takt control setting have not yet received much attention in research.This study aimed to investigate the effects of various visual tools in a takt production setting to gain knowledge on how these tools could aid takt control efficiency. The research utilized a design science research approach to create visual management tools and iterate them based on feedback. Interviews, site observation, and takt progress tracking were used to evaluate the implemented tools.The findings indicate that workers on site want to be more aware of the production plan, and information helps them to work in the right location at the right time. To help workers, visual management tools need to recognizable, explicit, and contain correct and up-to-date information. However, there are cultural issues related to implementation, especially on the need for information going through foremen to crews.
Construction sites contain a lot of waste, and eliminating it enables productivity gains and health and safety improvements. Computer vision is a promising technology that is being used in various construction applications. Construction sites with limited human resources could benefit from automated computer vision-based waste analysis. This paper presents preliminary findings related to the algorithm-based waste detection of piling works and explores potential applications from a visual management perspective. An experimental approach was used in the study, and images from a construction site in Finland were used to train the algorithm. The main findings revealed that the amount of waste shown by the images was substantial and that ground-level and drone images could be combined to create a comprehensive view of pile waste inventories. This paper also presents potential applications of image-based pattern recognition for infrastructure sites where the use of drone and ground-level images is standard practice. Several problems emerged when using transfer learning to train the algorithm, the most significant of which were variations in the scenery of images used for training and the limited number of images. The solutions to these problems lie in collecting more data and experimenting with other deep learning-based methods which will be explored in future.
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