Due to advancements in artificial intelligence, sensing and communication technologies, there has been a boost in robot applications within the construction industry, particularly in off-site and Modern Methods of Construction (MMC). Robots (e.g., robot arms and Automated Guided Vehicles (AGVs)) are being utilized to automate labour-intensive and repetitive construction activities such as heavy lifting, assembly, and material handling. Using robots can address the underlying issues in the construction industry, including low productivity , high incident rate  and skilled labour shortage . The construction industry is currently transitioning to replace humans with robots, but fully automated construction is still deemed a long-term goal . Consequently, developing an effective plan for integrating manual and automatic activities and managing human-robot interfaces is imperative. In this regard, layout planning is one of the key planning tasks. The dimension and location of robots’ workstations and moving paths need to be decided in layout planning for robot-assisted operations. Additionally, the workstations’ position, and storage size need to be determined in layout planning to maintain the integrity of human and robot activities—thereby avoiding interruptions in the workflow. Another important factor in layout planning is minimising unnecessary physical interfaces between humans and robots to reduce the safety incident risks. Previous literature has come up with various methods [5, 6, 7] to improve layout planning; however, they have not comprehensively addressed all the aspects of human-robot interactions in their models. Furthermore, they have not considered the dynamic and changing environment of construction operations, which may lead to the inefficiency of the layouts. To bridge these gaps, this research aims to develop a simulation-based digital twin (DT) for layout planning. In this context, DT is defined as a digital representation of dynamic construction operation and the physical layout. This approach will advance DT research by augmenting current approaches (which primarily have focused on visualising the physical asset) with a simulation that can model human and robot activities and their operational and physical interfaces. This method can thoroughly evaluate the layout efficiency in a risk-free virtual environment. Another essential advancement expected from this study is the development of a data-driven simulation model by dynamically incorporating real-time data from construction projects into the model and update the model to represent the reality of the construction operations. These models can inform decision-makers about the variations in the actual operation and forecast potential consequences.
The main objectives of this research are as follows:
- The development of a framework for creating simulation-based DT for layout planning.
- The development of models for simulating human-robot interactions in the layout planning context.
- The development of predictive and prescriptive models using simulation and AI techniques, such as reinforcement learning to identify optimum layouts while considering the changing environment.
The Principal Supervisor for this project is Dr SeyedReza RazaviAlavi.
Eligibility and How to Apply:
Please note eligibility requirement:
- Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
- Appropriate IELTS score, if required.
- Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere or if they have previously been awarded a PhD.
For further details of how to apply, entry requirements and the application form, see
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF22/…) will not be considered.
Deadline for applications: 18 February 2022
Start Date: 1 October 2022
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