Artificial Intelligence in the Built Environment

Our built environment has a huge impact on all aspects of our lives, not in the least quality of life and climate change. As an effect, huge potential resides in digitizing this industry and making data available for intelligent management and optimization of our environment (acoustic performance checking, sensor-based building management, IoT-enabled construction sites, etc.). Various Artificial Intelligence techniques are investigated and adopted in the department of the Built Environment and used for improving the quality of our built environment.

Research Themes

Ambient Intelligence

Ambient Intelligence is receiving much attention for realizing smart buildings and smart cities where sensors and algorithms are incorporated in the environment to optimize processes in real-time ranging from in-door climate control to public transport services.

Digital Twins of buildings and urban areas

Digital Twins of buildings and urban areas are of use for monitoring and improving operational buildings (HVAC, lighting, solar shading, etc), construction sites (feedback loop with construction site equipment), and for urban flow monitoring (people, goods, traffic).

Prediction

Prediction has always been of major importance in the built environment, particularly for facilitating informed decision-making in the design and engineering phases. Machine Learning techniques are increasingly adopted to predict behavior of buildings, urban areas and their use (flow monitoring).

Robotics

Robotics are of increasing importance to construction sites, manufacturing plants, cities and operational buildings, thus transforming the built environment in a semi-automated environment with high-tech robotics in all its aspects.

Energy Transition

A huge shift in energy use is needed for the built environment. Switching from the use of natural gas to renewable heating sources relies heavily on AI algorithms (prediction, learning, monitoring) and devices (ambient intelligence, edge AI).

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Urban Mobility

In order to improve urban mobility, an increasing number of personal information systems and decision support systems are built and used, relying on advanced visualization techniques (VR, AR), digital twins of people in the environment, and machine learning algorithms for prediction and providing personalized advice.

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Reference projects

Mobility, Ambient Intelligence, Prediction, Digital Twins

Machine Learning and sensor data

Based on various machine learning algorithms including Bayesian belief networks and decision trees, an integrated data analytics tool is developed to generate the spatial and temporal information related to travel and activities. By Tao Feng

Ambient Intelligence, Urban Mobility, Prediction, Mobility

Self-adaptive personal information systems

A Bayesian method for incremental learning an individual’s preferences based on his or her choice behavior is developed and applied in personal travel information systems. By Theo Arentze

Digital Twins, Engineering Systems, Ambient Intelligence, Edge AI

Digital Twins and Linked Building Data

Web-based information systems are developed, which collect various sorts of data about a building in a decentralized manner, including detailed 3D object models, point clouds, image data, semantic data, sensor data. By Pieter Pauwels

Robotics, Digital Twins, Engineering Systems, Prediction

AI in manufacturing

Manufacturing of complex geometries (parametric and structurally optimized structures with minimal material use) will be enabled by digital manufacturing techniques, e.g. robotics in construction and additive manufacturing. By Rob Wolfs

Robotics, Digital Twins, Ambient Intelligence, Health, Engineering Systems

House as Robot

By developing extreme scenarios and building mock-up’s, we aim to evolve everyday living spaces to adaptive living organisms that understand and empathize with the user. By Masi Mohammadi

Digital Twins, Ambient Intellingence, Engineering Systems, Mobility

SWT and Linked Data in Autonomous Mobility

Using ideas behind Linked Data and Semantic Web, allows the AI in charge of handling Autonomous Vehicles to consume valuable information from the surrounding ecosystem in order to achieve more optimal calculation of control parameters of the vehicle while using minimal amount of network resources. By Milos Viktorovic

Ambient Intelligence, Digital Twins, Engineering Systems

Automated fault detection of photovoltaic (PV) systems

Large-scale monitoring of distributed PV systems in comparison with expected PV output generated by a digital twin network, taking into account dynamic weather conditions, partial shading due to urban surroundings (e.g. from LiDAR data) and the non-linear characteristics of inverters and power systems. By Roel Loonen

Prediction, Engineering Systems

Expert systems for spatial-structural-physics design generation

The built environment is responsible for about 40% of the worldwide energy and material resources, and housing demands are growing, due to an increase of population, and become more complex. This requires fully optimized renovated and new buildings, and, of utmost importance, also optimized design processes: the products and their conceptualization need to be revolutionized. An open source available C++ toolbox has been developed, which provides super structured and super structure free spatial design representations; expert systems to generate, modify, and assess discipline specific representations; and data analysis and visualization tools. By Hèrm Hofmeyer

Healthy Working and Living Environments

With increasing pressure in our daily work and life, it is difficult to maintain a healthy balance and healthy environment to live in (air quality, acoustics, ambiance, and so forth). Sensing our environment and adjusting it to our individual and common needs requires AI techniques (ambient intelligence, digital twins, prediction).

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Smart Cities and Buildings

The built environment, both on a building and urban scale, is heavily embedded with devices, sensors, and actuators. As a result, the environment is made artificially intelligent, and it actively responds to its users (interactive ambient intelligence).

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Staff

Nitant Upasani

Full Professor emeritus

Wim Zeiler

EAISI Health

Jelle Versteeghe

Assistant Professor

Dujuan Yang

Information Systems Built Environment
Associate Professor

Pieter Pauwels

EAISI High Tech Systems
Associate Professor

Roel Loonen

EAISI High Tech Systems
Assistant Professor

Rob Wolfs

3D Concrete Printing

Melvin Wong

Full Professor

Theo Arentze

Real Estate and Urban Development

Alex Donkers

Tao Feng

Aiyu Zhu

Associate Professor

Herm Hofmeyer

Applied Mechanics and Design
Assistant Professor

Ekatarina Petrova

Davide Leonetti

Industry 4.0 in Construction

Construction sites and manufacturing for construction is heavily digitized and automated (digital twins, robotics). Autonomous robots are increasingly incorporated in factories and construction tasks, improving the productivity of the construction industry.

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Autonomous Vehicles

Urban mobility is shifting heavily, from a more traditional car- and pedestrian centered mobility, into a dense network of various kinds of transportation means. Autonomous vehicles are expected to invade the city fabric and interact with a network of devices (IoT) for a range of daily tasks.

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