Digital twinning of self-sensing structures using the statistical finite element method

Abstract

The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element (FE) model, as commonly used in design and construction, can help make sense of the copious amount of collected sensor data. This paper demonstrates the application of the statistical finite element method (statFEM), which provides a principled means of synthesizing data and physics-based models, in developing a digital twin of a self-sensing structure. As a case study, an instrumented steel railway bridge of $ 27.34\hskip1.5pt \mathrm{m} $ length located along the West Coast Mainline near Staffordshire in the UK is considered. Using strain data captured from fiber Bragg grating sensors at 108 locations along the bridge superstructure, statFEM can predict the “true” system response while taking into account the uncertainties in sensor readings, applied loading, and FE model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modeled during the passage of a passenger train. The statFEM digital twin is able to generate reasonable strain distribution predictions at locations where no measurement data are available, including at several points along the main I-beams and on structural elements on which sensors are not even installed. The implications for long-term structural health monitoring and assessment include optimization of sensor placement and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data are available.

Research progress and prospect of digital twin in bridge engineering

“…For example, a digital twin of a real machine can be built on a cloud platform and use the collected data and existing knowledge to simulate health conditions (Lee et al, 2013). Despite these advancements, research on digital twin was still relatively limited, and application development was hindered (Lu and Brilakis, 2019) 101 Automation in construction E (Broo et al, 2022) 18 Automation in construction F, E (Pregnolato et al, 2022) 9 Automation in construction F (Song et al, 2023) 3 Automation in construction F (Yang et al, 2022) 2 Automation in construction E (Pantoja-Rosero et al, 2023) 1 Automation in construction E (Gao et al, 2023) -Automation in construction F, O&M (Bittencourt et al, 2021) 1 Proceedings of the 10th international conference on bridge maintenance, safety and management, IABMAS 2020 F, O&M (Lai et al, 2022) 4 Case studies in construction materials O&M (Lu et al, 2020) 1 Construction research congress 2020 E (Febrianto et al, 2022) 3 Data-centric engineering E (Sánchez-Rodríguez et al, 2020) 6 EG-ICE 2020 workshop on intelligent computing in engineering, proceedings E (Jiang et al, 2021b) 28 Engineering structures O&M (Guo and Fang, 2023) -Engineering with computers E (Broo and Schooling, 2021) 11 IEEE access F (Dang et al, 2022) 22 IEEE transactions on industrial informatics F, O&M (Dan et al, 2021) 12 IEEE transactions on intelligent transportation systems F, O&M (Shim et al, 2019b) 15 International conference on smart infrastructure and construction 2019 O&M (Van Nimmen et al, 2021) 7 Journal of bridge engineering O&M (Omer et al, 2021) 7 Journal of bridge engineering E, O&M (Zhao et al, 2022) 7 Journal of bridge engineering O&M (Ye et al, 2020b) 26 Journal of civil structural health monitoring O&M (Baisthakur and Chakraborty, 2021) 7…”

“…Although numerical models for structural analysis can be directly generated from the gDT of bridges, the accuracy and real-time capability of nDT created using this method require improvement (Shu et al, 2019). An alternative approach to creating the nDT involves updating structural parameters using Bayesian inference methods and monitoring response data (Febrianto et al, 2022; Ghahari et al, 2022). Additionally, a combination of computer vision and vibration measurement has been utilized to achieve geometric and stiffness property updates of the nDT of a bridge (Lai et al, 2022).…”

Design and implementation of a smart infrastructure digital twin

“…For instance, a conventional Finite Element (FE) model of the bridge (Butler et al, 2018) would be a standalone VI object (VI 1). However, a more complex FE model, such as statFEM (Febrianto et al, 2021;Girolami et al, 2021), would be another VI object (VI 2) that depends on the PI object, due to the probabilistic nature of the statFEM framework that requires measured data. The back-end of the digital twin's software is a collection of containers working in parallel on the cloud platform.…”

Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization

“…Thus, optimization is necessary to reduce the boom’s weight and to improve the machine’s performance. Currently, physical assets can be digitally replicated and analyzed using digital twin technology [ 6 , 7 , 8 ]. This enables the performance analysis and optimization, scenario simulation, and outcome estimation of the intended model.…”