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Built Environment
TU DelftTU EindhovenUniversity of TwenteWageningen University
Built Environment


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Within the construction sector, a wealth of data can be used to solve various challenges. These include energy transition, renovation projects, the development of smart cities, and the industrialization of the sector. Because of its contribution to a wide range of challenges, digitization is also seen as a 'key enabler.' Broad application possibilities mean a large scope. To make the scope workable, four digitization objectives have been drawn up in the strategic program: Artificial Intelligence, Robotics and Automation, Mixed Reality (AR-VR-MR), and Digital Twins.

Artificial Intelligence (AI)

The first objective, Artificial Intelligence (AI), uses optimization techniques and large data sets. Self-learning algorithms can, for example, develop prediction models for building simulations, analyze design models and building processes, find anomalies and errors in (building) models, and control self-learning robots. Combined with the above, it provides a strong basis for decision support in all kinds of places in the built environment. Challenges are to develop reliable, insightful, and usable AI algorithms that, in terms of performance, reliability, and logic, minimally support and, in some cases, replace human thinking and human action.

This program line focuses on developing methods to analyze the reliability of existing data, automate/optimize design and planning, and make machines self-learning and safer. AI is applied within the other three objectives, for example, by linking AI to 'hardware in the loop simulations,' which make predictions about, for example, failure probabilities of infrastructure. It is important to directly connect monitoring actual performance in practice, which can eventually lead to acceptable risks in predictions.

The AI program focuses on three topics within the building and construction sector.

Robotics and Automation

Robotics uses (semi-)autonomous machines that take over tasks that are too risky, repetitive, difficult, or inefficient for humans. As machine changeover time has decreased and flexibility has increased, they can more often produce more cheaply than a human and play a role in production halls. However, the integration of robotics and mechanics on the (inherently) dynamic construction site remains a challenge. Machines such as 3D concrete printers, steel welders, cranes, autonomous vehicles (AV), drones, and excavators can be controlled (semi-automatically) from control rooms to automate the construction site. Challenges lie around developing all these applications for both controlled construction halls and uncontrolled outdoor conditions. This is currently in its infancy but is critical to meeting our 2030 goals.

This digitization objective, therefore, focuses on the development and testing of machines in production halls and on the construction site. First, prototypes will be developed for different construction processes (e.g., to support digging and lifting, monitoring progress on the construction site, or for assembly by welding and 3D printing), followed later by implementation studies in more recalcitrant environments.

The Robotics and Automation subprogram has four focus areas:

  • Smart buildings and infrastructure;
  • Industrialization & smart manufacturing systems;
  • Robots and ‘cobots’ on the building site;
  • Maintenance, monitoring, and inspection.

Mixed Reality (AR-VR-MR)

Virtual Reality, Augmented Reality and Mixed Reality integrate virtual data in real-time with a camera image of reality. This supports an intuitive way of visualizing key elements in the design, implementation, management, maintenance, and inspection but also provides the basis for training and process improvement. AR technologies have three basic functions in construction: visualization, information retrieval, and interaction. Challenges include developing real-time virtual representations of activities within buildings, machines, and robots, allowing a Digital Twin to be 'experienced' and maintained remotely. Another challenge is linking VR with training and process improvements in so-called feedback support systems.

This objective focuses on the applications and analysis of off-the-shelf virtual and augmented reality tools in design, planning, implementation, management, and maintenance. It also studies how technology plays a role in remote control of, for example, automation processes. It is particularly concerned with how these tasks support and to what extent the tools need to be further developed to help usage scenarios.

The Mixed Reality (AR - VR - MR) objective has three focus points:

  • VR for education and training;
  • VR/AR for life cycle management;
  • AR for design and implementation.

Digital Twins

The final objective, Digital Twins, is, as the name hints, a digital copy of an existing object. A Digital Twin of a building provides information from the realization to the use phase of construction. The bi-directional flow of data defines a Digital Twin. Sensor technology allows this communication and, as a result, creates a cyber-physical system. Think, for example, of collecting data about the use of light and heating, then modeling this in the digital building to subsequently control the physical structure automatically. Challenges lie around developing Digital Twin concepts for lifecycle management, developing 'as-built models,' and linking to BIM. In addition, this coupling must be done securely and privacy-consciously coupled with monitoring techniques and the development of user applications.

This program line focuses on developing methods to register geometric and usage data (sensors) of objects and construction processes in the built environment in user-friendly tools and models. It will also contribute to developing standards in collaboration with the Digital System GO.

The Digital Twins objective has three focal points:

  • Creating DT in relation to BIM and uniform agreement sets for information and knowledge modeling;
  • Making DT intelligent through data analysis techniques and Artificial Intelligence;
  • Applying DT in different usage scenarios.

Pieter Pauwels
Eindhoven University of Technology - Built Environment
Léon Olde Scholtenhuis
University of Twente - Engineering Technology
Jantien Stoter
Delft University of Technology - Architecture
Giorgio Agugiaro
Delft University of Technology - Architecture
Ekaterina Petrova
Eindhoven University of Technology - Built Environment
Bauke de Vries
Eindhoven University of Technology - Built Environment
Hans Voordijk
University of Twente - Engineering Technology
Dennis Pohl
Delft University of Technology - Architecture
André Dorée
University of Twente - Engineering Technology
Georg Vrachliotis
Delft University of Technology - Architecture
Perica Savanović
InHolland University of Applied Sciences - Research and Innovation Center Engineering, Design and Computer Science
Rizal Sebastian
The Hague University of Applied Sciences - Technology, Innovation & Society
Christian Struck
Saxion University of Applied Sciences - Business, Building & Technology