Search results

The present study employs social network analysis (SNA) to demonstrate the extent to which various building information modeling (BIM) functions can be used to address significant issues faced by the Tanzanian public sector construction projects.

This study adopted secondary data obtained from a comprehensive literature review on core BIM functions and the underlying issues faced by the Tanzanian public sector construction projects. This study then adopted SNA for associating the BIM functions with relevant construction issues.

For Objective 1, the findings revealed that BIM can address 68% of significant issues faced by the Tanzanian public sector construction projects. For Objective 2, the findings revealed that the identified functions mainly addressed issues in the early phases of the project. Finally, for Objective 3, the most effective function was “spatial coordination.”

This literature-based study does not fully capture both the current contextual issues faced by the industry and the BIM capability of stakeholders involved. In addition, this research does not distinguish between public project size and type which can influence the types of issues faced and consequently the use of BIM function. Accordingly, the research presented in this study needs to be complemented by on-the-ground feedback of industry stakeholders and needs to investigate how project size and type impacts the types of issues that emerge and the use of BIM.

With respect to practice, the findings of the present study highlight key BIM functions practitioners can begin to target.

In Tanzania, public sector construction projects contribute greatly to social development of Tanzania's population. Owing to the current state of public sector projects underperforming and the negative impact the projects have on the country's development, an intervention measure such as BIM has the potential to enhance the effective and efficient delivery of these projects and thereby promote the social development of the country's population.

With respect to theory, this study demonstrates how core functions of BIM can be mapped with various construction-related issues in order to evaluate the efficacies of the BIM-based investments for improving overall performance in the public sector projects. With respect to practice, the method illustrated in the present study can be applied by policymakers and practitioners to identify core BIM functions to target to address pressing contextual issues faced by public sector projects in the respective contexts.

Innovation originally meant ‘change’ or ‘renewal’. Over time, it became associated with creating new products, methods, materials, markets, and forms of organisation. Today, in everyday language, innovation is used as a much broader term that encompasses non-materialistic, non-economical ideas such as social, educational, philosophical, political, environmental, or spiritual innovations. What makes something innovative is subjective and depends not only on the perceived novelty of the content but also on the co-constructed meaning of what is possible or what it changes in our lives. Therefore, innovation leaders must also become experts in co-constructing meanings with their teams. In this chapter, a structured solution-focused framework will be introduced with the intent to support innovative teams in maintaining effective team dialogue, foster more innovative team collaboration, better innovations, or an improved innovation process. In fostering an interactive and dynamic team process, solution-focused leaders engage in deliberate interactions that often initiate a positive dynamic leading into an even more innovative future.

The purpose of this paper is to propose and evaluate the method for grasping a defined set of objects in an unstructured environment. To this end, the authors propose the method of integrating convolutional neural network (CNN)-based object detection and the category-free grasping method. The considered scenario is related to mobile manipulating platforms that move freely between workstations and manipulate defined objects. In this application, the robot is not positioned with respect to the table and manipulated objects. The robot detects objects in the environment and uses grasping methods to determine the reference pose of the gripper.

The authors implemented the whole pipeline which includes object detection, grasp planning and motion execution on the real robot. The selected grasping method uses raw depth images to find the configuration of the gripper. The authors compared the proposed approach with a representative grasping method that uses a 3D point cloud as an input to determine the grasp for the robotic arm equipped with a two-fingered gripper. To measure and compare the efficiency of these methods, the authors measured the success rate in various scenarios. Additionally, they evaluated the accuracy of object detection and pose estimation modules.

The performed experiments revealed that the CNN-based object detection and the category-free grasping methods can be integrated to obtain the system which allows grasping defined objects in the unstructured environment. The authors also identified the specific limitations of neural-based and point cloud-based methods. They show how the determined properties influence the performance of the whole system.

The authors identified the limitations of the proposed methods and the improvements are envisioned as part of future research.

The evaluation of the grasping and object detection methods on the mobile manipulating robot may be useful for all researchers working on the autonomy of similar platforms in various applications.

The proposed method increases the autonomy of robots in applications in the small industry which is related to repetitive tasks in a noisy and potentially risky environment. This allows reducing the human workload in these types of environments.

The main contribution of this research is the integration of the state-of-the-art methods for grasping objects with object detection methods and evaluation of the whole system on the industrial robot. Moreover, the properties of each subsystem are identified and measured.