Implementing Structural Health Monitoring (SHM) systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible. Thus, estimating the state (condition) of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential way. For this purpose, Structural State Translation (SST) has been recently proposed to predict the response data of civil structures based on the information acquired from a dissimilar structure. This study uses the SST methodology to translate the state of one bridge (
An Intelligently Designed AI for Structural Health Monitoring of a Reinforced Concrete Bridge
e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques.
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- Award ID(s):
- 1633608
- NSF-PAR ID:
- 10341160
- Date Published:
- Journal Name:
- The Concrete Industry in the Era of AI
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Bridge #1) to a new state based on the knowledge acquired from a structurally dissimilar bridge (Bridge #2 ). Specifically, the Domain-Generalized Cycle-Generative (DGCG) model is trained in the Domain Generalization learning approach on two distinct data domains obtained fromBridge #1 ; the bridges have two different conditions:State-H andState-D . Then, the model is used to generalize and transfer the knowledge onBridge #1 toBridge #2 . In doing so, DGCG translates the state ofBridge #2 to the state that the model has learned after being trained. In one scenario,Bridge #2’s State-H is translated toState-D ; in another scenario,Bridge #2’s State-D is translated toState-H . The translated bridge states are then compared with the real ones via modal identifiers and mean magnitude-squared coherence (MMSC), showing that the translated states are remarkably similar to the real ones. For instance, the modes of the translated and real bridge states are similar, with the maximum frequency difference of 1.12% and the minimum correlation of 0.923 in Modal Assurance Criterion values, as well as the minimum of 0.947 in Average MMSC values. In conclusion, this study demonstrates that SST is a promising methodology for research with data scarcity and population-based structural health monitoring (PBSHM). In addition, a critical discussion about the methodology adopted in this study is also offered to address some related concerns. -
Rollercoasters are challenging structures. Although the ever-changing geometry can guarantee a thrilling ride, the complexity of loading patterns due to the intricate geometry make testing and analysis of these structures challenging. Fatigue-induced damage is one of the most common types of damage experienced by civil engineering structures subjected to cyclic loading such as bridges and rollercoasters. Fatigue cracking eventually occurs when structures undergo a certain number of loading and unloading recurrences. This cyclic loading under stresses above a certain limit induces microcracking that can eventually propagate into failure of a member or connection. Because of the geometric and structural similarities between rollercoasters and bridge connections, similar techniques can be used for structural health monitoring and estimation of remaining fatigue life. Uniaxial fatigue analysis methods are widely used for the analysis of bridge connections. However, there is little guidance for the analysis of complex connections. They can experience variable amplitude, multiaxial, and non-proportional loading. In such cases uniaxial fatigue methods are insufficient and can lead to underestimates. A framework for the understanding and analysis of multiaxial fatigue damage using strain data collected from strain rosettes is presented. Uniaxial and multiaxial fatigue analysis methods proposed for non-proportional loading are compared. Methods proposed are applicable to both rollercoaster and bridge connections. The critical plane method is used for the estimation of multiaxial fatigue life. Results show that non-proportional loading and the accuracy of the critical plane estimation can cause a significant decrease in the estimates of remaining fatigue life. This methodology is anticipated to be used for real-time fatigue prognosis and evaluation tools for bridge networks.more » « less
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Abstract Impedance-based structural health monitoring (SHM) is recognized as a non-intrusive, highly sensitive, and model-independent SHM solution that is readily applicable to complex structures. This SHM method relies on analyzing the electromechanical impedance (EMI) signature of the structure under test over the time span of its operation. Changes in the EMI signature, compared to a baseline measured at the healthy state of the structure, often indicate damage. This method has successfully been applied to assess the integrity of numerous civil, aerospace, and mechanical components and structures. However, EMI sensitivity to environmental conditions, the temperature, in particular, has been an ongoing challenge facing the wide adoption of this method. Temperature-induced variation in EMI signatures can be misinterpreted as damage, leading to false positives, or may overshadow the effects of incipient damage in the structure.
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