Image from Gramazio Kohler Research, ETH Zurich. Sourced at
Image from Gramazio Kohler Research, ETH Zurich. Sourced at
Image from Gramazio Kohler Research, ETH Zurich. Sourced at

Next Generation Highrise

Start Date:
End Date:
PI Principal Investigator
data-driven agglomeration
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Award | Publication | Exhibition:
Biennale Architettura 2021 - How Will We Live Together?
Contribution to the Singapore National Pavilion, to gather: The Architecture of Relationships
Averaging 465 visitors per day to the Singapore Pavilion (82,000+ visitors as of 15/11/2021)
Singapore Good Design SG Mark 2019 NGH
Next Generic Residential High-Rise - Flexible Housing Typologies and their Social and Environmental Sustainability
Heckmann, O., Budig, M., Ng, A.
The Hong Kong Polytechnic University, Hong Kong
Paper and presentation
Integrating Building Information Modeling (BIM) in Building Life Cycle Assessments
Ng, A., Cheah, L., Budig, M., Heckmann, O.
Beijing, China
Next Generation Residential High-Rise: Evaluating and Comparing the Global Warming Potential of Different Structural Systems and Materials
Symposium of the International Association for Shell and Spatial Structures (IASS 2019), Form and Force
Barcelona, Spain
Full Paper
Next-Generation Residential High-Rise : Advancing Flexibility and Circular Design by Evaluative Computational Means
IAAC Responsive Cities Symposium 2019: Disrupting Through Circular Design
Institute for Advanced Architecture of Catalonia, Barcelona, Spain
Full Paper
Data-driven Embodied Carbon Evaluation of Early Building Design Iterations
CAADRIA 2020, RE: Anthropocene - Design in the Age of Humans, Bangkok, Thailand, 5-8 August 2020
Full Paper
Next Generation Residential High-Rise: Computational Support of Flexibility and Circular Design
CAADRIA 2020, RE: Anthropocene - Design in the Age of Humans, Bangkok, Thailand, 5-8 August 2020
Full Paper
Digital tools for resilience: calculating our way to net-zero cities
Interview for Research Magazine on Smart Cities
pdf download at
11 2017
Wenn Holz und Beton enge Nachbarn sind (when wood and concrete are close neighbours)
Digital tools for resilience: calculating our way to net-zero cities
Spring 2021
Computational Screening-LCA Tools for Early Design Stages
Journal article, submitted to editors with final edits and publication in progress
Budig, Michael; Heckmann, Oliver; Hudert, Markus; Ng, Amanda; Xuereb Conti, Zack; Lork, Clement, Jun Hao
ISSN: 14780771
International Journal of Architectural Computing
Toolkits for Renewable and Regenerative Buildings
Book chapter, publication March 2022
Devni Acharya1, Emma Boucher2, Richard Boyd2, Michael Budig3, Elisa Magnini2, Neil Walmsley1; 1 Arup Singapore, 2 Arup London, 3 SUTD
ISBN 978-1-119-73485-7
Editors: Heckmann, O. and Siew, M.K.

Decarbonisation is one of the pressing challenges in the built environment as the unprecedented growth of cities, particularly in Asia will continue to have a massive environmental impact. Demographic changes put additional pressure on how we plan buildings for uncertain futures. Planners, construction industries, governmental agencies and stakeholders require new approaches to design and respective material choices to minimise the embodied carbon. Data and tools are fragmented and hardly allow for a holistic integration of knowledge in one platform as the digitalisation in the building industries is still at its infancy. One of the areas that is difficult to measure is the embodied carbon of a building in its planning process, indicated as Global Warming Potential (GWP in kg CO2-eq per kg of material). Most available Carbon Calculators depend on detailed data such as Building Integration Management (BIM) models that are usually only available in later design stages. 

Our research at SUTD has developed methods, mathematical models and mock-up computational tools that allow designers to make informed design decisions and assess the environmental performance in early design and assessment stages. We have also established a framework for flexible building designs: habitation patterns are continuously changing and unforeseen situations such as the 2020 pandemic demand the ability for quick adaptations. 

The research has been focussing on three integrative aspects for the development of such tool: 

  1. 1.An intuitive feedback on environmental performance in early design stages.
  2. 2.The assessment of flexibility in regard to the impact of alterations on the service life-time of buildings. 
  3. 3.The integration of available Life Cycle Inventory (LCI) data into predictive modelling to generate a range of probable outcomes indicated as Global Warming Potential (GWP, usually measured in kg of CO2 eq per kg of material).

The research has been constrained to residential typologies and specifically to hybrid concrete-timber construction systems in order to establish an applicable methodology and the respective mock-up tools. 

Firstly, the mock-up tool for simplified Life Cycle Assessment (LCA, measured in Global Warming Potential GWP) demonstrates how designers can get an intuitively legible and visual feedback to systematically compare the environmental performance of alternative design iterations at initial design stages. To enable such a systematic comparison of design variants the workflow follows an 'Open Building' approach and segments designs into permanent support and adaptable infill systems.  Here, it goes further and differentiates into various degrees of flexibility in each of the main systems: concrete construction systems would normally be used as permanent support structures such as cores (containing circulation, infrastructure, and service functions), whereas partly load bearing components made from timber could be adapted and changed over long periods of time, and hence they would have a basic degree of flexibility. The infill systems are also further distinguished into conventional partitioning systems (such as drywalls that already have a higher degree of flexibility, but to the cost of their destruction) and modular systems that can be altered in various combinations. A Shoebox approach was adopted for the visual representation of a building concept in a simplified and intuitively legible interface. In the Shoebox representation, a series of dynamically alterable modules represent alternative load-bearing systems and variable material fractions. These are linked to a simplified parametric building model to extract data for the comparison of GWP results (fig. 1). 

Fig. 1 – Shoebox approach: the cube on the left represents one spatial unit within a building. The radar charts on the right displays the estimated embodied carbon outputs for different design variants, and for different construction systems.

Secondly, it focussed on the flexibility of buildings so that they can respond to demands for functional changes and make use of a systematic differentiation between permanent support- and adaptable infill components. As mentioned, both systems are further categorised into kits of parts with variant degrees of flexibility. Predictive mathematical models are used to translate cycles of changing demands during service life-time into varying configurations within the infill system. The degree of flexibility is used to specify the potential service lifetime extension of buildings, helping decision-makers to decrease the overall environmental impact of a building (fig. 2). 

Fig. 2 – An embedded graph syntax detects the probability of walls to be changed over time.

Thirdly, the research outlined the concept for a predictive mathematical model to overcome current challenges in the available LCI data:

  1. 1.This data is mostly specific to particular markets and regions only;
  2. 2.Varying Life Cycle Assessment (LCA) methods are applied, which makes it difficult or even impossible to compare;
  3. 3.There are huge gaps in the data in those global regions that undergo the fastest growth in urbanisation. 

The methodology of this workflow suggests the collection of data from existing buildings with simplified input data (reduction to the essential parameters), integration of the information into Bayesian Neural Network models (capable of machine learning by updating its predictions with the availability of more current data) and predicting a range of possible outcomes to help a user make more informed decisions (fig. 3). 

Fig. 3 – The bar on the left represents a simplified list of possible construction systems, the user can adjust the dimensions on the building scale in the center and on the unit scale on the right. An additional choice to determine material ratios at (bottom) enables users to asses more adaptable and more environmentally friendly alternatives on the element scale.

Whereas the mock-up tool is design-based and requires minimal information on floor plans (such as in concept development stages), this data-driven tool requires no prior knowledge of the design and is thus applicable in even earlier phases (such as a project framing and programming phase).

Background Research 

This research has been funded by the SUTD-MIT International Design Centre since August 2018. The developed methodology and workflow was initially specifically catered to projects in South East Asia, a region of massive urban expansion with increasing Greenhouse Gas (GHG) emissions. Since this is taking place in close proximity to one of the world’s largest forested areas with vast resources of wood, a renewable and sustainable material. The research was exploring the potentials and constraints of hybrid concrete-timber construction systems in residential typologies. Currently, the available concrete technologies are on a very advanced level whereas timber is  rarely applied in construction despite recent technological innovations and attempts for a reintroduction. After it has been replaced by concrete and steel as the predominant materials for the construction of cities in the past century, we are here assessing the potential impacts of partly supplementing concrete structures with timber. This has an immediate impact on the embodied carbon and indirectly offers the possibility of keeping the more lightweight timber elements adaptable during a building’s lifetime.

Fig. 4 – Next Generation High Rise: methodology for an early stage design Life Cycle Assessment (LCA) of different construction systems (top left) with an integration of occupational patterns due to demographic changes over a buildings’ operational life time (centre right).

The team was awarded the Arup Global Research Challenge 2019 and will continue working on the mock-up tool, focusing on the integration of structural data and the determination of material quantities. The research was among five selected out of 75 participants in an open international call for research proposals by Arup. 

Oliver Heckmann, Lynette Cheah, Richard DeNeufville, Colin Yip, Markus Hudert, Amanda Ng Qi Boon, Zhi Tian Tee, Clement Lork, Loo Jun Wen, Zachary Xuereb Conti, Ray Cheng Chern Xi
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Images (c) Gramazio Kohler Research, ETH Zurich
Images (c) Gramazio Kohler Research, ETH Zurich
Images (c) Gramazio Kohler Research, ETH Zurich
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Oliver Heckmann, Lynette Cheah, Richard DeNeufville, Colin Yip, Markus Hudert, Amanda Ng Qi Boon, Zhi Tian Tee, Clement Lork, Loo Jun Wen, Zachary Xuereb Conti, Ray Cheng Chern Xi