“How can the synergistic lamination of wood and graphene enable the creation of a real-time digital twin for this material system? Leveraging graphene as a deformation sensor, how can the acquired data be stored and utilized to assess and extend the structural lifespan of the material? “
Wood | Industry
Wood, arguably the most prominent biomaterial, has played a fundamental role throughout human history, serving as the primary material for constructing shelters, buildings, and various structures. In response to the urgent need to decrease CO2 emissions in the construction industry, the mass timber sector has potentially grown. Technological advancements have enabled the construction of buildings ranging from 13 to 32 floors using mass timber. The industry is currently valued at $1 billion, has experienced significant growth in recent years and is projected to reach $2.3 billion by 2033., as well as the development of promising techniques for free-form wooden architecture.
Between 50 and 60% of virgin wood goes to the construction industry and between 70 and 95% of wood after the building’s lifespan is recyclable but it goes to waste after deconstruction.
Sensors and BIM Technology | Security for Built Environment
Graphene | The Future of Graphene Industry
Material System | Wood and Graphene composite (Glue lamination)
Imagine a material system empowered by real-time tracking from within, allowing us to create digital twins and also extend the lifespan of materials, contributing to the realm of Sustainable Material Engineering
Introducing the digital twin concept to the mass timber construction industry involves leveraging wood and graphene composites to monitor deformation in real-time, store and optimize data throughout construction and operation phases, and facilitate efficient material classification post-deconstruction. This innovative approach aims to extend the lifecycle of mass timber constructions while taking into account the eco-friendly properties of graphene and the recyclability of wood. Notably, graphene’s inclusion does not hinder the recyclability of wood, as both materials are carbon-based.
A material service life passport with wood and graphene composite material, potentially collected directly from the material itself, includes details such as composition analysis, manufacturing data, durability assessments, maintenance recommendations, safety and environmental compliance, usage history, and digital identification. This comprehensive will serve as a valuable tool for managing, maintaining, and evaluating the performance of the material throughout its service life. Implementing a cloud database accessible from portable devices allows owners, investors, and users to easily access through a user-friendly interface, stakeholders can search for materials using identifiers, facilitating efficient monitoring and enabling informed decisions regarding material reuse. This approach enhances transparency, promotes sustainable practices, and streamlines the management of material resources throughout their lifecycle.
Prototype
Experiment Set-up
Experiment
When the set up and the connections to the computer are complete, a first script is activated to get inputs from the user, the arduino and the camera regarding the specific set up that is going to be tested. I must emphasize that this part is done after a long process of calibrating the electrical resistance using a multimeter and the Arduino. Without any calibration, the changes can’t be detected due to the big amount of noise in the circuit and the small range of the change
After the data collection is done another script is used to analyze the stored images and detect the physical changes in the prototype. The marked points on the beam are compared to their original location when no load was applied.
Digital Flow
The first milestone in our flow is to extract and store enough data regarding electrical resistance changes and physical deformations in order to train a machine learning model to correlate between them. Those 2 sets of data are automatically stored in Csv files with specific timestamps that connect them together. In addition, general data regarding the beam physical properties (like cross section & length), the environmental conditions (as temperature and humidity) and the load applied are stored as well. After the model training is done we will be able to collect and store the changes in the electrical resistance of each structural wood & graphene element in order to: first, get real time alerts on structural failures second, classify each component for reuse in material marketplaces and third, use the dataset that is collected as a design tool for future projects and complex geometries.
Applications in Architecture
Creating an interface that will allow real time monitoring and detection of structural failures. Each specific structural component will have its own life service passport that will be continuously updated throughout its lifespan.
In the disassembly process, the life service passport of each element will allow to identify and classify each part for different reuse purposes
And finally, as mentioned before, This unique data set will allow to push the boundary even further and to achieve more complex designs with less material waste.
