Operational data collection and analysis for a smart 3D-printed footbridge

Purpose This paper presents the structural monitoring results of the world's first 3D-printed steel footbridge, using a fixed sensor network built into the bridge, to better understand both the behaviour of this novel structure and the way it is used. Design/methodology/approach The bridge was publicly exhibited and then installed for two years in central Amsterdam. The main features of the sensor network installed to monitor its behaviour are described. The bridge's behaviour was studied using a combination of labelled data collected in controlled conditions at the University of Twente and long-term monitoring during normal use in Amsterdam. Findings The data collected show that thermal behaviour can be effectively decoupled from the response of the bridge due to pedestrian loading and that the pedestrian movements captured by camera can be anonymized as coordinates, which can be correlated with the loads and strains produced by those pedestrians. The Pearson correlation condition is used to identify the type of movement on the bridge, effectively distinguishing between heel-drops, running and walking movements. Originality/value The richness of such a dataset is demonstrated, measured using embedded sensors and Internet of Things technology. Analysis of these measurements gives insights into the behaviour of a unique large 3D-printed steel structure and the use of a busy piece of urban infrastructure more generally.

Authors

Institutions

Publication Details

Journal
Smart and Sustainable Built Environment
Published
2025-09-27
DOI
https://doi.org/10.1108/sasbe-04-2025-0167
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Operational data collection and analysis for a smart 3D-printed footbridge

Thomas Reynolds, Faridaddin Vahdatikhaki, Alex Tessier, Sage Cammers-Goodwin et al.
Smart and Sustainable Built Environment
Infrastructure Maintenance and Monitoring
article

Operational data collection and analysis for a smart 3D-printed footbridge

Thomas Reynolds, Faridaddin Vahdatikhaki, Alex Tessier, Sage Cammers-Goodwin, Alec Shuldiner, Michael Nagenborg, C Buchanan, Zachariah Wynne, Rolands Kromanis, Theo Glashier, Pan Zhang, Kasper Siderius, Josh Cameron
article en

Abstract

Purpose This paper presents the structural monitoring results of the world's first 3D-printed steel footbridge, using a fixed sensor network built into the bridge, to better understand both the behaviour of this novel structure and the way it is used. Design/methodology/approach The bridge was publicly exhibited and then installed for two years in central Amsterdam. The main features of the sensor network installed to monitor its behaviour are described. The bridge's behaviour was studied using a combination of labelled data collected in controlled conditions at the University of Twente and long-term monitoring during normal use in Amsterdam. Findings The data collected show that thermal behaviour can be effectively decoupled from the response of the bridge due to pedestrian loading and that the pedestrian movements captured by camera can be anonymized as coordinates, which can be correlated with the loads and strains produced by those pedestrians. The Pearson correlation condition is used to identify the type of movement on the bridge, effectively distinguishing between heel-drops, running and walking movements. Originality/value The richness of such a dataset is demonstrated, measured using embedded sensors and Internet of Things technology. Analysis of these measurements gives insights into the behaviour of a unique large 3D-printed steel structure and the use of a busy piece of urban infrastructure more generally.

Smart and Sustainable Built Environment
Autodesk (United States) (US), GTx (United States) (US), Autodesk (Canada) (CA), NIHR Imperial Biomedical Research Centre (GB), Imperial College London (GB), WSP (Canada) (CA), University of Twente (NL), University of Edinburgh (GB)
Openalex Percentile: Top 72%
Infrastructure Maintenance and Monitoring
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.