Architects:Joris Laarman Lab

Year :2021

Photographs :Thea van den Heuvel, Adriaan de Groot, Jande Groen, Thijs Wolzak, Merlin Moritz

Technology : MX3D, Proprietary Software

Client : City of Amsterdam

Desgigner : Joris Laarman Lab

Material & Structural Analysis : Imperial College London

Material Expert : ArcelorMittal

Research : AMS-3D Building Fieldlab, Amsterdam Institute for Advanced Metropolitan Solutions

Digital Twin Sensor Network And Systems Integrator : Autodesk

Digital Twin Modeling And Analysis : The Alan Turing Institute

Sensor Network Design & Install : FORCE Technology, University Twente & Autodesk

Scanning : Faro Technologies

Construction Expert : Heijmans & Mous

Hardware, Computing : Lenovo

Hardware, Robotics : ABB

Hardware, Sensor Network : HBM

Hardware, Welding : Oerlikon

Hardware, Air Cleaning : Plymovent

Welding Gas : Air Liquide

Size : Length: 12.2 meters; Width: 6.3 meters; Height: 2.1 meters

City : Amsterdam

Country : The Netherlands

The installation of the bridge is the culmination of several years of work. MX3D kicked off this project in 2015 when it proposed printing a metal bridge with its innovative large-scale, robotic 3D printing technology, creating a playful, inspiring example of how digital tools can create a new form language for architectural objects.

The proprietary MX3D printing technology uses off-the-shelf welding robots to build up metal objects layer by layer. The MX3D bridge design was created using generative design and topology optimisation techniques. The combination of those technologies allow for a higher form of liberty and a promise of significant material reduction.

Smart MX3D bridge is a ‘living laboratory.’ Innovative in design and technology, the bridge serves as a living laboratory. Equipped with a state-of-the-art sensor network, the ‘Smart Bridge’ is powering a cutting-edge research project. In concert with academic and industry researchers, the City of Amsterdam will use the bridge’s data streams to explore the role of IoT systems in the built environment. For instance: can we use such systems to anonymously analyse crowd behaviour, to help better understand the impact of tourism in the Red Light District. The project also addresses questions about open data, data ethics, and citizen ownership of city analytics. For this purpose the bridge was granted a two-year permit by the city of Amsterdam

Smart sensor network feeds ‘digital twin.’ Realising this vision required an extended collaboration between MX3D, The Alan Turing Institute (the Turing), Arup, Autodesk, FORCE Technology, and the University of Twente. Between them, they have spent the last three years creating and installing a sophisticated sensor network, to enable real-time data collection, to represent those data flows in live models, and to create usable analytics on top of that data which feeds into a Digital Twin of the bridge.

The bridge’s sensors collect structural measurements such as strain, rotation, load, displacement, and vibration, and also measure environmental factors such as air quality and temperature. Together, this data is used to create a ‘digital twin,’ an accurate computer model that represents the physical bridge in real-time. The digital twin will help engineers measure the bridge’s health and monitor how it changes over its lifespan. The sensor data will also be used to “teach” the bridge to understand what is happening on it, beginning with the ability to count how many people are crossing it and how quickly.

Having helped initiate the project in 2015, a team of researchers from Autodesk designed bespoke software and served as the primary systems integrator for the smart bridge. Autodesk software collects data from the sensor network and visualises it in a digital twin model representing the bridge’s response to use in real time. Autodesk worked closely with the partners of the Data Centric Engineering Programme at the Turing as well as FORCE Technology and University of Twente to design and install the sensor network. Even in its prototype form, this network was useful when performing structural testing on the bridge. Load testing and materials testing were both conducted by the Data Centric Engineering team, which proved that the bridge is able to hold at least a 19.5 ton load, well above its ultimate design load.

The UK’s national institute for data science and artificial intelligence (AI), the Turing, began its involvement with the bridge by assembling an interdisciplinary team of data science and AI experts from its Data Centric Engineering Programme. The Turing is hosting the bridge data for the full two year period covered by the bridge’s current operating permit and has conducted a thorough ethics review of the project to ensure that the scientific goals of the project do not compromise the privacy of the public. Using a custom data platform, the Turing supports researchers who require access to the sensor data stored in its secure cloud. The Turing researchers have expertise to develop novel and advanced digital twin models for prototypes of the MX3D bridge and are now applying these techniques to evolve the digital twin of the physical bridge as it is used.

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