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1. Multiaxial fatigue life assessment of a vertical-lift bridge connection using strain rosette data

   https://doi.org/http://eswp.com/product/ibc-19-40/  

   Masoud Sanayei, Sofia Puerto (April 2021, Structures Congress Conference 2019)

   null (Ed.)

   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. A framework for the analysis of multiaxial fatigue damage using strain rosettes installed on welded connections is proposed. The applicability of this methodology is shown using strain measurements collected in a welded gussetless truss connection of a vertical-lift bridge. Commonly used uniaxial fatigue analysis methods are insufficient in complex structures that experience variable amplitude, multiaxial loading, and non-proportional loading. Strain data with these characteristics are used for the estimation of the number of multiaxial stress reversals induced by in service loads and the number of associated cycles using the rain-flow method. Methods proposed for uniaxial loading and multiaxial non-proportional loading are compared. 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. The methodology proposed is anticipated to be used for real-time fatigue prognosis aiming to address critical needs related to maintenance procedures of complex structures, visual inspection techniques and evaluation tools for infrastructure 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. 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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4. Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites

   https://doi.org/https://doi.org/10.1088/1361-665X/ac093d  

   Nasser, Jalal; Groo, Lori Anne; Sodano, Henry (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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5. Entropy-based damage model for assessing the remaining useful fatigue life

   https://doi.org/10.1177/10567895231215474  

   Mahmoudi, Ali; Jirandehi, Arash_P; Amooie, Mohammad_Ali; Khonsari, MM (December 2023, International Journal of Damage Mechanics)

   A reliable approach based on an entropy-damage model for assessing remaining useful fatigue life is presented. Two damage models are presented and evaluated to assess their effectiveness in predicting remaining useful life. The first model focuses on reduced toughness caused by fatigue degradation, while the second is based on accumulating entropy during fatigue loading. The entropy-based approach employs infrared thermography to anticipate entropy accumulation and damage status. Outcomes reveal that the entropy-driven technique offers enhanced precision. Moreover, its damage growth rate remains consistent, regardless of the number of cycles leading to failure, ensuring a more stable tracking of damage evolution. It successfully predicts the remaining useful life and can treat variable load sequencing without knowing the loading history. An extensive set of experimental results with carbon steel 1018 are presented to illustrate the utility of the approach. 

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