Text-to-architecture: The new architectural language

Building design
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Bright room decorated with plants and benches - Photo by Teng Yuhong

Architecture from the text field? What sounds like Dada and digital esotericism is the hottest trend of the moment: text-to-architecture. AI tools such as Stable Diffusion and Midjourney, as well as specialized platforms, suddenly spit out plausible floor plans, renderings and even BIM-capable models from vague prompts. Architecture is becoming a dialog between man and machine – and the industry is upside down. But what’s behind the hype? Who benefits, who loses – and how far along are Germany, Austria and Switzerland? Welcome to the age in which words build.

  • Text-to-architecture refers to the use of AI to generate architectural designs, visualizations and models from speech or text.
  • Germany, Austria and Switzerland are experimenting, but real breakthroughs are rare – cultural, technical and legal hurdles are slowing things down.
  • Innovative AI platforms are already delivering impressive results: from initial sketches to complete BIM models.
  • Digitalization and AI are radically changing the job description – from the role of the traditional designer to a curating authority.
  • Sustainability by design: AI can help to design in a more resource-efficient and climate-friendly way – or have the opposite effect.
  • Technical expertise remains essential: prompt engineering, AI training data, model interpretation and critical thinking are mandatory.
  • The debate about copyright, responsibility and creativity has flared up – and is more heated than ever before.
  • Global pioneers are setting the pace, while German-speaking countries are still weighing up the risks.
  • Vision: architecture as a democratized, accessible field – danger: banalization, bias and the loss of depth and context.

From sketch to prompt: how AI is rewriting architecture

Anyone starting an architectural design today may still be reaching for a pen – or already typing in the text field. Text-to-architecture is the new interface between idea and space. What began in graphic design with generative AI images has long since arrived in the architectural cosmos. The scene is divided: Some see the machine translation of language into space as the democratization of the design world. Others fear the end of architectural handwriting and warn of an era of synthetic arbitrariness.

Technically speaking, text-to-architecture essentially works like this: An AI model is trained on millions of buildings, plans, renderings and text descriptions. It learns to link language patterns with spatial structures. If you type in “a sustainable, light-flooded residential building made of wood with a green roof in the Alps”, you will receive plausible visualizations or even parametric models within seconds. Models such as Midjourney, DALL-E or Stable Diffusion serve as initial fields of experimentation. Specialized platforms, such as Spacemaker, testfit or Luma AI, go further: they provide floor plans, volume studies and BIM-compatible outputs. Interaction is shifting – from drawing to prompting.

However, it is by no means as simple as the marketing departments of AI providers make it out to be. Those who master the tool will benefit. Those who rely on AI run the risk of overlooking its limitations. Because what is sold as “creativity” is often a statistical approximation of the mainstream. The actual architectural intelligence remains in demand: contextualization, critical reflection and the ability to distinguish between appearance and substance.

In the German-speaking world, restraint still prevails. Universities are researching, offices are experimenting – but the real lighthouse projects are missing. The fear of losing control, of losing one’s own signature and of legal gray areas is slowing down the euphoria. While competitions with AI-generated designs are already being decided in the USA and Asia, the ethical implications are still being debated in Germany. Progress is different.

Nevertheless, one thing is clear: the door is open. The question is no longer whether AI will find its way into architecture, but how. Those who use it as an inspiration tool will gain speed and bandwidth. Those who switch to autopilot risk falling into banality. The new architectural language is text-based – but the translation into the built environment remains a craft and an attitude.

Status quo DACH: Between a thirst for research and a denial of reality

Germany, Austria and Switzerland are traditionally skeptical of technological revolutions that are a threat to their own profession. Text-to-architecture is no exception. Universities – from TU Munich to ETH Zurich – are busy exploring the possibilities. Students generate concept studies via prompt, design master classes produce explanatory videos on stable diffusion. But as soon as it comes to implementation in everyday construction, the voices become quieter. Most architecture firms prefer to observe rather than invest themselves.

The reason is obvious: the legal situation is unclear, technical standards are lacking and the question of who is liable for a faulty AI design remains unresolved. The chambers warn, the associations urge caution and the building authorities wave it off. For many, AI-generated design is a nice add-on, but not a tool for the HOAI phases. The feared loss of control outweighs the short-term gain in efficiency.

Austria is a little more willing to experiment. Vienna, for example, is testing AI-supported neighborhood analyses, and some private developers are having the first volume studies generated by algorithms. But even here, much remains in pilot status. Switzerland, traditionally a country with an affinity for innovation, shines with research clusters and start-ups that combine AI and architecture. However, the majority of construction projects remain traditional. The leap from demo to implementation is a long one.

A look at the training landscape is exciting. More and more universities are integrating AI tools into design training. Prompt engineering is becoming the core competence of the next generation of architects. At the same time, the analog design process remains a compulsory subject. The hope: the synthesis of digital speed and analog depth. The danger: The next generation loses itself in generating and forgets to understand.

Politics? They are watching. Funding programs focus on BIM, not on AI-based design tools. Building regulations lag years behind developments. While the world is jumping on the AI bandwagon, German-speaking countries are still standing on the platform. Whether this is due to caution or despondency is debatable. One thing is certain: the next generation will not wait any longer.

Innovations, trends and the role of AI: Who types, who builds?

The pace of innovation in the field of text-to-architecture is breathtaking. What was considered an academic experiment yesterday is now a reality on the market. AI platforms deliver floor plans, façade studies and material concepts – all based on text specifications. The quality? Fluctuating, but rapidly improving. Large offices generate initial variants, developers test urban planning scenarios on the fly. The speed at which ideas can be visualized has multiplied. This is not only changing the design phase, but the entire job description.

One trend is the integration of AI design into parametric planning processes. Tools such as Spacemaker or testfit merge data-based analysis with generative design. Anyone planning a residential area, for example, can run through various scenarios using text input – from density and orientation to shading. The AI provides variants, the human selects and adjusts. The distinction between design and analysis is becoming blurred.

A second trend is the democratization of architecture: anyone with access to a browser and an AI can generate designs. This sounds like participation, but it harbors risks. The danger of banalization is real: those who copy prompts and recycle AI outputs produce uniformity. At the same time, there is an opportunity to bring more voices and perspectives into the design process. The role of the architect is changing – from creator to curator, from draughtsman to prompt designer.

The role of prompt engineering is exciting. Those who know how to talk to AI will get better results. This requires technical understanding, creativity and critical judgment. Prompt engineering is becoming a key competence – and the new architectural language. The danger: if you just parrot the same thing, you produce interchangeable results. Those who understand the system can strengthen their own ideas.

And then there is the big question: what does all this mean for creativity? Some celebrate the explosion of possibilities, others warn against replacing intuition with statistics. One thing is certain: AI can do many things, but it cannot generate genius loci. The depth, the contextualization, the social embedding – all of this remains the task of humans. The machine types, but humans build.

Sustainability, technology and the new responsibility

Text-to-architecture promises efficiency, speed and diversity. But what does this mean for sustainability and responsibility? At first glance, it sounds tempting: AI can simulate millions of variants, suggest climate-friendly materials and optimize energy flows. In theory, this leads to more sustainable architecture – fewer resources, more adaptability, faster scenario building. The catch: the training data and algorithms are often black boxes. They reproduce what already exists, prefer standard solutions and ignore local contexts.

Anyone who adopts AI outputs without checking them runs the risk of greenwashing on a grand scale. Sustainability is not achieved by generating variants, but by understanding contexts. AI provides the suggestion, humans have to assess the consequences. This requires technical knowledge: How do the algorithms work? What data sets are they based on? How do I interpret the outputs?

Technical expertise becomes the decisive factor. Prompt engineering is just the beginning. Anyone working with text-to-architecture needs to know how AI is trained, what the risks of bias and distortion are and how to validate the results. BIM knowledge, data analysis and a critical view of the AI logic are mandatory. Anyone who doesn’t master this will be overtaken by their own machine.

The issue of responsibility is also up for debate. Who is liable for an AI-generated design? Who decides which variants are implemented? The classic role models are being broken up. Architects have to ask themselves new questions: How do you defend copyrights when AI draws from billions of other people’s works? How can quality and identity be ensured when the tool seems omnipotent?

The solution lies in the combination: AI as a tool, not as a replacement. Humans remain the thinking, responsible part of the process. AI provides inspiration, analysis and a wealth of variants. The decision on what to build remains a question of knowledge, attitude and responsibility. Those who understand this can use the new architectural language sensibly. Those who surrender to it lose control.

Debate, visions and the global context: architecture on the AI merry-go-round

The debate about text-to-architecture is heated. Some celebrate the democratization, others warn of uniformity and loss of depth. Critics point to algorithmic distortions, the tendency towards mediocrity and the danger of AI architecture degenerating into mainstream kitsch. Proponents see new opportunities for participation, diversity and speed. As is so often the case, the truth lies somewhere in between.

Visionary voices are calling for architecture education to be radically restructured. AI skills as mandatory, prompt engineering as the new drawing, collaboration with machines as everyday life. The utopia: anyone can build, anyone can design – architecture as an open, democratized field. The dystopia: uniformity, generic buildings, loss of quality and context. The task: to shape the tools in such a way that they create diversity and do not destroy it.

From a global perspective, German-speaking countries are lagging behind. The USA, China, South Korea and the Gulf States are investing heavily in generative AI for architecture. There, competitions are decided with AI designs, start-ups are developing specialized tools and architectural education is being designed AI-first. The DACH region is debating – and losing pace. Those who don’t move will be left behind.

But even the international pioneers are struggling with problems: Copyright issues, ethical debates, the risk of bias and the challenge of preserving local identity. Text-to-architecture is not a panacea, but a tool. It requires knowledge, reflection and creative power. Those who rely solely on AI produce mass instead of class.

The global architectural debate has long revolved around questions of algorithmization, the role of humans in design and responsibility for what is built. Text-to-architecture is the latest, but perhaps most radical step in this development. The future will show whether architects will master the tool – or fail.

Conclusion: Building words – but attitude is key

Text-to-architecture is not a gimmick, but a turning point. The new architectural language is text-based, AI-driven and highly dynamic. It opens up opportunities for efficiency, participation and sustainability – if used wisely. It harbours risks of trivialization, bias and loss of control – if it is blindly adapted. In German-speaking countries, there is still reluctance, whereas globally, what is typed has long been built. The key insight: AI is a tool, not a replacement. Words build – but attitude decides what remains standing. Those who understand this can shape the future of architecture. Those who hesitate will be overrun by the next prompt wave.

POTREBBE INTERESSARTI ANCHE

Mobility data for adaptive road design

Building design
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Central city street in St. Gallen with parked cars, photographed by Albatros Aslan

Imagine streets that react spontaneously to traffic flows, green spaces that grow where they are needed most and cycle paths that are created because mobility data demands them. Adaptive street design with mobility data is not a dream of the future, but the big stage for cities that not only move with the times, but determine them themselves. If you want to know how data can be turned into dynamic spaces, read on – and learn why the mobility data revolution has long since arrived on the asphalt.

  • Definition and relevance of mobility data for adaptive road design
  • Technological basics: sensors, data sources and interfaces
  • Adaptive street design: practical examples from German-speaking cities
  • Data-supported planning processes and their challenges
  • Governance, data protection and the question of data sovereignty
  • Opportunities and risks: From better traffic flows to algorithmic bias
  • Influence on sustainable urban development, climate resilience and social participation
  • Outlook: How mobility data is changing the planning culture and job profile

Mobility data: The backbone of adaptive streetscape design

Anyone talking about streetscape design today can no longer ignore mobility data. This data is far more than just columns of figures from traffic counts; it is the pulse of the city, a highly dynamic image of urban mobility that goes far beyond traditional traffic planning. Mobility data encompasses all movement flows of people and vehicles, whether on foot, by bike, car, public transport or modern sharing services. It is generated from a variety of sources: Traffic light controls, WLAN tracking, GPS from cell phones, camera sensors, induction loops, floating car data from vehicle fleets as well as from apps that record traffic and movement profiles anonymously. The trick is to link and interpret these data sources in order to obtain as complete, up-to-date and reliable a picture as possible of the reality of mobility.

The term adaptive road design describes the ability to adapt road spaces to changing requirements in a flexible and demand-oriented manner. This ranges from the temporary reallocation of lanes and dynamic traffic routing to pop-up cycle paths and flexible pedestrian zones. The basis for this is comprehensive, precise mobility data, preferably available in real time. It shows where bottlenecks occur, which routes are particularly busy or when certain means of transport are preferred. Only with this database is a truly adaptive, i.e. responsive, design even conceivable.

However, the use of such mobility data places high demands on the technical infrastructure and the skills of planners. It is not enough to simply collect data; it must be analyzed, interpreted and translated into concrete options for action. This requires modern geoinformation systems, powerful data platforms and interfaces that link different data sources with one another. Artificial intelligence and machine learning come into play to recognize patterns and create forecasts. Adaptive street design is thus becoming a discipline that combines technical expertise, planning creativity and a deep understanding of urban dynamics.

In many German, Austrian and Swiss cities, mobility data is already part of planning practice. Intelligent traffic guidance systems, dynamic traffic lights and real-time information for local public transport are visible results. But adaptive streetscape design goes further: it asks how the streetscape itself can be changed to respond to new mobility patterns. The goal is a city in which space follows demand – not the other way around.

Overall, mobility data is the backbone of a city that sees itself as a learning, flexible and participatory system. It enables planning to no longer be understood as a rigid corset, but as an open, continuous process. The street space becomes a stage on which data and users jointly determine the choreography.

Technology, sensors and data interfaces: The invisible infrastructure

Behind every adaptive road design is a complex network of sensors, data management and digital infrastructure. The collection of mobility data begins with classic induction loops in the roadway that count vehicles and extends to state-of-the-art camera systems with automatic object recognition. Floating car data, i.e. movement data from vehicle fleets that allow conclusions to be drawn about traffic density and congestion trends in real time, is also becoming increasingly relevant. Mobile devices that provide anonymized position data via GPS complete the picture and make it possible to systematically record pedestrian and bicycle traffic for the first time. Particularly exciting is the use of WLAN and Bluetooth tracking, which makes movement patterns in public spaces visible without storing personal data.

All these sensors and data sources provide raw data that must first be collected and processed. This is where so-called urban data platforms come into play, which act as data hubs. They aggregate, filter and harmonize information from a wide variety of sources and make it available for planning, administration and the public. The interoperability of these platforms is crucial, as mobility data only unfolds its full value when it can be interpreted in the context of other urban data – such as weather, construction sites, events or energy consumption. Open interfaces (APIs) and standardized data formats are therefore essential to enable collaboration between different stakeholders, systems and administrative levels.

Another key element is the real-time capability of the data. Adaptive road design depends on being able to react quickly and flexibly to changes. This requires that data is not only collected, but also processed and visualized in fractions of a second. Modern dashboards, coupled with AI-based evaluation tools, enable planners to see at a glance where action is needed. Automatic alarm systems indicate sudden changes, for example if an accident shifts the flow of traffic or a major event leads to congestion on public transport.

The integration of machine learning opens up new dimensions: Systems learn from past patterns, recognize seasonal fluctuations, recurring bottlenecks or mobility behaviour when the weather changes. Forecasting models simulate how certain measures – such as new cycle lanes, temporary play streets or detour – will affect the overall structure. In this way, planning decisions can be made based on data, scenarios can be run through and measures can be tested in a targeted manner.

All these technological possibilities stand and fall with the acceptance and trust of the population. Data protection and data security are therefore not peripheral issues, but an elementary component of the infrastructure. Only if citizens can be sure that their movement data will be used anonymously and responsibly will the necessary basis for legitimizing adaptive, data-based urban design be created.

Adaptive street design in practice: between pop-up cycle paths and real-time traffic

Numerous projects in German-speaking cities show how mobility data can make adaptive streetscapes a reality. The potential became particularly visible during the coronavirus pandemic, when pop-up cycle paths were created in many places. In Berlin, temporary cycle lanes were set up on the basis of current traffic data to provide short-term space for the increase in bicycle traffic. Sensors and counting stations provided the basis for recording demand and capacity utilization and adapting the measures in a targeted manner. The evaluation of the data enabled continuous optimization: where usage remained particularly high, temporary solutions were converted into permanent infrastructure.

Vienna also relies on mobility data to make street spaces more flexible. The “Digital Twin Vienna” project combines real-time traffic data with information on pedestrian flows, public transport utilization and weather conditions. This allows traffic lights to be adjusted dynamically, temporary meeting zones to be created and bottlenecks to be identified at an early stage. In Zurich, on the other hand, floating car data and anonymized movement profiles are used to identify bottlenecks and temporarily rededicate road space – for events, construction site management or to relieve heavily frequented junctions, for example.

Another example is Hamburg, where the Urban Data Platform integrates traffic flows, roadworks information and environmental data. Adaptive traffic management, flexible loading zones for delivery traffic and dynamic parking space management are directly linked to the evaluation of current mobility data. The effects are immediately noticeable: less congestion, faster response times in the event of incidents and more efficient use of limited road space.

However, adaptive road design is not limited to motorized traffic. The focus is increasingly on pedestrians and cyclists. In Basel, for example, data from counting points and movement analyses are used to create temporary pedestrian zones and safe routes to school – adapted to the time of day, weather and number of events. This creates a public space that is not only efficient, but also people-friendly.

These examples show: Adaptive street design is not a technocratic experiment, but a lived practice. It makes public spaces more flexible, safer and more sustainable – and opens up new possibilities for responding to social and climatic challenges. However, data quality, transparency and participation must always be taken into account.

Governance, data protection and participation: The invisible levers

As impressive as the technological possibilities are, the question of governance is crucial. Who controls, who decides and who monitors the use of mobility data? Adaptive road design requires clear responsibilities and a framework that combines data sovereignty, data protection and participation. In Germany, Austria and Switzerland, it is usually the municipalities that retain sovereignty over urban data platforms – often in cooperation with external service providers who provide technical solutions and analysis tools.

Data protection is more than just a bureaucratic obstacle. It is the guarantor of acceptance and trust. Movement data is sensitive, even if it is anonymized. This is why many cities rely on privacy by design: even during the development of the systems, it is ensured that no conclusions can be drawn about individuals. Data is aggregated, pseudonymized and provided with clear deletion deadlines. Regular audits, open documentation and independent control bodies ensure additional transparency.

Transparency is also the key word when it comes to participation. Adaptive street design thrives on the involvement not only of experts but also of the public. Open data portals, interactive visualizations and participatory planning processes make it possible to make measures comprehensible and verifiable. In this way, citizens do not become the object of data-based control, but actors in a joint learning process. Digital participation formats, from online surveys to interactive maps, create new channels for participation and feedback.

But governance does not end with administration. Cooperation between different disciplines – urban planning, traffic planning, IT, law and society – is also key. Adaptive street design is a cross-cutting issue that breaks down silos and requires new forms of cooperation. Interdisciplinary teams, agile working methods and an open error culture are just as important as technical standards and legal clarity.

Ultimately, the question remains: how can adaptive street design be prevented from becoming a playing field for commercial interests? The trend towards the commercialization of urban data models cannot be overlooked. It is therefore essential that municipalities expand their data competence, retain control over critical infrastructure and define clear rules for access to and use of mobility data. Only in this way will the adaptive, data-based city remain a common good – and not a black box of private providers.

Opportunities, risks and the paradigm shift in urban planning

The use of mobility data for adaptive street design opens up enormous opportunities – but also new risks. On the plus side, there are more efficient traffic flows, less congestion, better air quality and more space for active mobility. Cities become more resilient, more flexible and can react more quickly to crises or new trends. The integration of mobility data into planning enables unprecedented precision and dynamism that makes traditional planning tools look old-fashioned. The ability to run through various scenarios, test measures and observe their effects in real time is particularly impressive.

But as the power of data grows, so does the responsibility. Algorithmic distortions, unfair distribution of resources or the disadvantaging of certain groups are real dangers if data is interpreted in an unreflected or one-sided way. Adaptive systems run the risk of reinforcing existing inequalities if they are not consciously counteracted. It is therefore essential that planners, administrators and the public work together to define guidelines that safeguard values such as justice, transparency and sustainability.

Another risk lies in the technocratization of planning. If decisions are only made on the basis of data, there is a danger that local characteristics, social needs or design quality will fade into the background. This is why the role of professional planners remains indispensable: they are the ones who interpret data, place it in a spatial context and harmonize it with other goals – such as climate protection, quality of stay or social participation.

What does all this mean for the profession of urban planning and landscape architecture? It is becoming more digital, more dynamic and more interdisciplinary. Data literacy is becoming a key qualification, and the ability to deal with uncertainties and contradictions is becoming a central challenge. Adaptive street design requires planners who not only use technology, but also critically reflect on and design it.

The end result is a paradigm shift: planning is no longer static, but a process. The street space is not a finished product, but a living organism that is constantly changing. Mobility data makes this dynamic visible and controllable – but it is not an end in itself. It remains the task of planners to turn data into living spaces that function, inspire and connect.

Conclusion: Data-driven urban design – from a technical tool to a new planning culture

The use of mobility data for adaptive street design is far more than just a technical trend. It marks the dawn of a new planning culture in which data, technology and human intuition work hand in hand. Cities that use mobility data intelligently will become more flexible, more sustainable and more liveable. They can control traffic flows, make better use of space, respond to crises and recognize the needs of their residents in real time. But the path is challenging: it requires high-tech and attitude, data protection and dialog, new competencies and clear rules.

Adaptive street design is not a sure-fire success, but a social and planning experiment that requires courage, creativity and a sense of responsibility. It offers enormous opportunities for sustainable urban development, climate resilience and social participation – but also harbours the risk of technocratization and commercialization. It is crucial that mobility data is understood and used as a common good, that transparency and participation are prioritized and that planning remains human even in the digital age.

Planners who now see mobility data as a tool and inspiration are not only designing streetscapes, but also reinventing urban planning. And that’s a good thing – because the city of tomorrow will not only be built, it will be measured, interpreted, adapted and brought to life together. Welcome to data-driven, adaptive urban design – where public space is constantly being created anew.

Gray colossus

Building design

Worth more than a glance: the ceiling painting

Having barely arrived in Rotterdam, Baumeister Academy winner Maxi Graber shares a photo of the Cornucopia painting in the Markthal on the Academy Instagram account. In 2014, Maxi’s internship office MVRDV built the first market hall in the Netherlands. Reason enough for us to take another look at the gray colossus.

Having barely arrived in Rotterdam, Baumeister Academy winner Maxi Graber shares a photo of the Cornucopia painting in the Markthal on the Academy Instagram account. The post literally goes through the roof. In 2014, Maxi’s internship office MVRDV built the first Markthal in the Netherlands and covered it with a large arch and 200 apartments. Reason enough for us to take another look at the gray colossus. Our editor Sabine Schneider traveled to Rotterdam in 2015 and reported on her visit in the Baumeister March issue. Here is an excerpt from her report.

It won’t be easy. I start my journey to Rotterdam with tense anticipation. I know the market hall in Rotterdam well from publications, and my opinion is clear: it’s a monstrous construction that obviously wants to make itself smaller than it is on the outside with its cladding of camouflage gray granite slabs, but screams all the louder on the inside with a kitschy sky of giant fruits. In cross-section, the building forms a half-baked horseshoe, a tunnel that leads nowhere, an oversized fairground stall with apartments on the hump. A new typology, as the architects are promoting the project? Save us from that.

In fact, my criticism of the façade and form is now far less important when I am on site: the ribbon-like square of the Binnenrotte in the center, under which the tracks run and which therefore cannot be built on, appears cheerless, empty, draughty and not well defined on five out of seven days when there is no weekly market. The large, gray market hall has the same problem as the surrounding buildings: it is an island between islands – it lacks urban density. It does not appear permeable, but stands slightly elevated a few steps above the square, its reflective panes closing off the huge gate, sealing it off. It can only be entered through three narrow revolving doors that you have to squeeze through.

MVRDV have set up simple steel scaffolding as market stalls in Hall 96 on an area roughly the size of a soccer pitch. It’s fun to look, try, stroll and buy here. There is everything from currywurst to exclusive steak, from Dutch cheese to Turkish sweets. A good idea is to set up a terrace on the roof of the stalls, creating a “tasting room” on the roof. Something like this is often missing in traditional markets, because you work up an appetite while strolling around. However, it also brings the market closer to one of the usual “food courts” in shopping malls.

Restaurants, cafés, a cookery school, a household goods store and a wine shop have moved into the first two floors of the long sides of the tunnel. The interior façades of the 102 rental apartments and 126 condominiums, all of which have windows overlooking the market and a terrace to the outside, curve above. The higher you climb in the building, the more oblique the view of the market becomes, until at the very top of the 24 penthouses on the eleventh and last floor you can look straight down vertically.

Concept and compromises

But how did this design come about? Rotterdam is planning to renovate the former old town district and held an investor competition in 2004. The developer Provast submitted the design by MVRDV and won first prize, as the architects were able to combine the two specified residential slabs with a market. Priority was given to housing; there was no budget for a market hall. This resulted in the horseshoe shape, as the upper apartments, which close the arch, were too deep for good lighting – so the shape was slanted at the top. Towards the first floor, the storeys widen again in order to enlarge the retail space as required by the developer. In this way, the constraints did not shape the architectural idea, but deformed it like chewing gum.

You can find the full report here!

And you can find out more about Baumeister Academy there!

The Baumeister Academy is supported by GRAPHISOFT, BAU 2019 and Schöck Bauteile GmbH.