Structural health monitoring (SHM) is critical for ensuring the safety of infrastructure such as bridges. This article presents a digital twin solution for the SHM of railway bridges using low-cost wireless accelerometers and machine learning (ML). The system architecture combines on-premises edge computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, the accelerometers stream raw vibration data, which are processed in the frequency domain and analyzed using machine learning to detect anomalies that indicate potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge for which vibration data were collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks that indicate potential changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warnings of bridge damage and also supports the use of in-house-designed sensors with lower cost and edge computing capabilities such as those used in the demonstration. The successful on-premises–cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructures like bridges. This article presents a digital twin solution for SHM of railway bridges using low-cost wireless accelerometers and Machine Learning (ML). The system architecture combines edge on-premises computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, accelerometers stream raw vibration data, which is processed in the frequency domain and analyzed via machine learning to detect anomalies indicating potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge, where vibration data was collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks, suggesting changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warning of bridge damage and also supports the use of in-house designed sensors of lower-cost and edge computing capabilities than those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

The European building industry is facing a strong increase in renovation processes, which are still non-cost-effective, involve unproperly coordinated stakeholders, are disturbing for the occupants, and cause important inefficiencies in the overall renovation process. In this context, digitalization and Building Information Modelling (BIM), as an enabler, is the key solution that may drive renovation interventions to ensure a more successful and leaner process, aiding the whole value chain of actors to achieve its full potential. This research describes the OpenBIM methodology applied in order to transform the implicit knowledge from the stakeholders involved in the building renovation process, not structured enough for automation, into an OpenBIM digital process based on the BIM standards. The outcomes of this research are the OpenBIM ready workflows that represent the renovation process and information requirements according to the involvement of different stakeholders rooted in the analysis of barriers, requirements, and needs. Those workflows are the basis for the future development of specific products and tools for boosting digitalization and interoperability in the renovation process.

EPES system will consist of a set of interoperable ICT tools, which will enable the manufacturing and engineering companies enter into a continuous process of upgrading their products along their life cycle within the frame of the Virtual Factory and Product Service System concept, through a configurable and adaptable set of software services. The services will focus on improving the performance of products in operation taking into account different knowledge based aspects as reliability, availability, maintainability, costs, productivity, quality, energy efficiency, environmental impact, working environment, etc. In this regard, and having as a reference an industry driven Business Case focused on the windmill sector, the approach followed by EPES to generate and develop high added value services to support the windmill farms maintenance will be discussed. The capabilities resulting from the research will enable the capitalization on trustable global and local Sustainability Intelligence. Product engineering teams will be able to exploit this intelligence to adapt design, operation and disposal strategies through managed “eco-constraints” relevant to their market contexts.

The European building industry is facing a strong increase of renovation processes, which are still non-cost-effective, involve unproperly coordinated stakeholders, are disturbing for the occupants, and imply important inefficiencies of the overall renovation process. In this context, digitalization and Building Information Modeling (BIM), as an enabler, is the key challenge that may drive the renovation interventions to ensure a successful process covering the whole value chain of actors to achieve its full potential. This research describes the OpenBIM methodology applied to transform the implicit knowledge from the stakeholders involved in a building renovation process into an IDDS digital process.