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1. What rollercoasters can teach us about fatigue life of bridge connections

   https://doi.org/10.1007/978-3-030-47634-2_2  

   Tchemodanova, Sofia Puerto; Sanayei, Masoud (September 2020, Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS), Dynamics of Civil Structures)

   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. 

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2. Remaining Fatigue Life Prediction of a Roller Coaster Subjected to Multiaxial Nonproportional Loading Using Limited Measured Strain Locations

   https://doi.org/10.1061/9780784482230.012  

   Tchemodanova, Sofia Puerto; Tatsis, Konstantinos; Dertimanis, Vasilis; Chatzi, Eleni; Sanayei, Masoud (April 2019, Structures Congress Conference 2019)

   High cycle fatigue is a major cause of cracking in steel structures subjected to cyclic loading. It can result in substantial financial losses and structural failures compromising the safety of users. Uniaxial methods are in many cases insufficient for large in-service structures with complex geometry and connections subjected to multiaxial non-proportional loadings. A new method for fatigue life prediction for complex structures is presented using the critical plane method and the Kalman filter. The applicability of the methodology proposed is demonstrated and evaluated in a roller coaster support structure. Strain rosettes and accelerometers were installed on a support bracket near weld lines to measure responses. A substructure model is defined and used to estimate response prediction in the weld of the support bracket. The estimation of the input and the state estimation is performed using the augmented Kalman filter method, based on the response measurements and the substructured model. This new methodology is anticipated to be used for real-time fatigue prognosis of highway bridges. 

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3. An Implicit Fillet‐Weld Element Formulation for Mesh‐Insensitive Fatigue Evaluation of Complex Structures

   https://doi.org/10.1111/ffe.14486  

   Zhang, Lunyu; Wu, Shengjia; Dong, Pingsha (February 2025, Fatigue & Fracture of Engineering Materials & Structures)

   ABSTRACT In fatigue evaluation of welded structures, explicit weld representations in finite element (FE) models are needed for reliably capturing stress or strain concentration behaviors at critical weld locations, for example, weld toe or weld root, in using widely accepted traction structural stress or extrapolation hot‐spot stress methods. The laborious efforts needed for generating weld geometry have been a major challenge for fatigue evaluation of complex structures containing many welds. In this paper, we present a user‐defined fillet‐weld element formulation and its numerical implementation for computing traction mesh‐insensitive structural stresses. The fillet‐weld element is formulated by connecting several linear four‐nodes Mindlin shell elements around weld region as a user‐defined element. The resulting elements can be directly used with major commercial FE codes through an available user subroutine interface. A number of well‐documented fillet‐welded components are then used for validating the accuracy and robustness of the developed fillet‐weld elements. 

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4. A Coarse-Mesh hybrid structural stress method for fatigue evaluation of Spot-Welded structures

   https://doi.org/10.1016/j.ijfatigue.2022.107109  

   Zhang, Lunyu; Dong, Pingsha; Wang, Yuedong; Mei, Jifa (November 2022, International Journal of Fatigue)

   A hybrid structural stress method is presented for significantly simplifying spot weld representations in fatigue evaluation of complex spot-welded structures while retaining a high degree of accuracy in structural stress computation. The method is formulated by extracting nodal forces and moments around a group of domain elements connected to a spot weld represented by a regular beam element. Through a systematic decomposition technique, existing closed-form solutions, previously only valid for modeling single-spot weld test specimens, can now be used for calculating the relevant structural stresses under complex loading conditions in structures, as validated its ability in correlating fatigue test data. 

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5. Artificial Neural Networks and Phenomenological Degradation Models for Fatigue Damage Tracking and Life Prediction in Laser Induced Graphene Interlayered Fiberglass Composites

   Nasser, J. (June 2021, Smart materials and structures)

   null (Ed.)

   The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG) has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates the potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs) and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension–tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60% more accurate than those of phenomenological degradation models, displaying R2 values greater than 0.98 and root mean square error (RMSE) values smaller than 10−3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R2 values greater than 0.99 and RMSE values smaller than 2 × 10−4 once more than 30% of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs. 

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