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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. 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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3. A Thermodynamic Framework for Rapid Prediction of S-N Curves Using Temperature Rise at Steady-State

   https://doi.org/10.1007/s11340-023-01016-y  

   Mahmoudi, A; Khonsari, MM (February 2024, Experimental Mechanics)

   Abstract Building S-N curves for materials traditionally involves conducting numerous fatigue tests, resulting in a time- consuming and expensive experimental procedure that can span several weeks. Thus, there is a need for a more efficient approach to extract the S-N curves. The primary purpose of this research is to propose a reliable approach in the framework of thermodynamics for the rapid prediction of fatigue failure at different stress levels. The proposed method aims to offer a simple and efficient means of extracting the S-N curve of a material. A method is introduced based on the principles of thermodynamics. It uses the fracture fatigue entropy (FFE) threshold to estimate the fatigue life by conducting a limited number of cycles at each stress level and measuring the temperature rise during the steady-state stage of fatigue. An extensive set of experimental results with carbon steel 1018 and SS 316 are conducted to illustrate the utility of the approach. Also, the efficacy of the approach in characterizing the fatigue in axial and bending loadings of SAE 1045 and SS304 specimens is presented. It successfully predicts fatigue life and creates the S-N curves. The effectiveness of the approach is evaluated successfully for different materials under different loading types. The results show that the temperature rise is an indicator of the severity of fatigue and can be used to predict life. 

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4. Fatigue damage tracking and life prediction of fiberglass composites using a laser induced graphene interlayer

   https://doi.org/https://doi.org/10.1016/j.compositesb.2021.108935  

   Groo, Lori Anne; Nasser, Jalal; Inman, Daniel; Sodano, Henry (August 2021, Composites)

   null (Ed.)

   Fiberglass-reinforced composite materials are commonly used in engineering structures subjected to dynamic loading, such as wind turbine blades, automobiles, and aircraft, where they experience a wide range of unpredictable operating conditions. The ability to monitor these structures while in operation and predict their remaining structural life without requiring their removal from service has the potential to drastically reduce maintenance costs and improve reliability. This work exploits piezoresistive laser induced graphene (LIG) integrated into fiberglass-reinforced composites for in-situ fatigue damage monitoring and lifespan prediction. The LIG is integrated within fiberglass composites using a transfer-printing process that is scalable with the potential for automation, thus reducing barriers for widespread application. The addition of the conductive LIG within the traditionally insulating fiberglass composites enables direct in-situ damage monitoring through simple passive resistance measurements during tension-tension fatigue loading. The accumulation and propagation of structural damage are detected throughout the fatigue life of the composite through changes to the electrical resistance measurements, and the measurement trends are further used to predict the onset of catastrophic composite failure. Thus, this work results in a scalable and multifunctional composite material with self-sensing capabilities for potential use in high-performing, dynamic, and flexible composite structures. 

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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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