Integrating SHM and BIM for predictive bridge maintenance
Insights from the DTEC-SHM project
Publication date
2025
Document type
Konferenzbeitrag
Organisational unit
Conference
14th Japanese German Bridge Symposium ; Munich, Germany ; September 17–20, 2025
Publisher
Förderverein Konstruktiver Ingenieurbau der UniBw München e. V.
Book title
14th Japanese German Bridge Symposium
First page
641
Last page
645
Part of the university bibliography
✅
Language
English
Keyword
dtec.bw
Abstract
Structural Health Monitoring (SHM) enables continuous, sensor-based assessment of bridge conditions. To fully unlock the potential of SHM, it is crucial to integrate sensor data into a comprehensive digital workflow based on Building Information Modeling (BIM). This integration ensures that collected data is systematically analyzed, interpreted, and applied in a standardized, efficient manner.
As part of the DTEC-SHM project, two bridges have been instrumented with over 400 sensors to support extensive monitoring efforts. The sensor network includes devices for recording accelerations, inclinations, temperatures, and environmental conditions via weather stations. Additionally, innovative sensors have been implemented for durability assessment and axle load monitoring using Bridge Weigh-In-Motion (B-WIM) technology. To contextualize and interpret the sensor data, two detailed finite element (FE) models were developed, providing a valuable physical framework for analysis and a base to develop digital twins.
Building on this foundation, the project now focuses on advancing SHM applications by developing a predictive maintenance management system driven by Key Performance Indicators (KPIs). This system will support proactive, data-driven maintenance strategies.
For infrastructure owners, SHM offers essential insights into the structural condition of their assets, enabling informed decision-making for repair prioritization, optimized maintenance scheduling, and efficient resource allocation. By integrating SHM with digital tools, asset managers can shift from reactive to predictive maintenance, improving safety and ensuring the long-term resilience and sustainability of infrastructure systems.
As part of the DTEC-SHM project, two bridges have been instrumented with over 400 sensors to support extensive monitoring efforts. The sensor network includes devices for recording accelerations, inclinations, temperatures, and environmental conditions via weather stations. Additionally, innovative sensors have been implemented for durability assessment and axle load monitoring using Bridge Weigh-In-Motion (B-WIM) technology. To contextualize and interpret the sensor data, two detailed finite element (FE) models were developed, providing a valuable physical framework for analysis and a base to develop digital twins.
Building on this foundation, the project now focuses on advancing SHM applications by developing a predictive maintenance management system driven by Key Performance Indicators (KPIs). This system will support proactive, data-driven maintenance strategies.
For infrastructure owners, SHM offers essential insights into the structural condition of their assets, enabling informed decision-making for repair prioritization, optimized maintenance scheduling, and efficient resource allocation. By integrating SHM with digital tools, asset managers can shift from reactive to predictive maintenance, improving safety and ensuring the long-term resilience and sustainability of infrastructure systems.
Version
Published version
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