Low cycle fatigue behavior of circumferentially notched specimens made of modified 9Cr–1Mo steel at elevated temperature

Abstract During service, notched designed components such as steam generators in the nuclear power plant usually experience fatigue damage at elevated temperatures, due to the repeated cyclic loadings during start-up and shut-down operations. Under such extreme conditions, the durability of these components is highly-affected. Besides, to assess the fatigue life of these components, a reliable determination of the local stress-strain at the notch-tips is needed. In this work, the maximum strains of circumferentially notched cylindrical specimens were calculated using the most commonly known analytical methods, namely Neuber's rule, modified Neuber's rule, Glinka's rule, and linear rule, with notch root radius of 1.25, 2.5, and 5 mm, made of modified 9Cr–1Mo steel at 550 °C, and subjected to nominal stress amplitudes of ±124.95, ±149.95, and ±174.95 MPa. The calculated local strains were compared to those obtained from Finite Element Analysis (FEA). It was found that all the analytical approximations provided unreliable local strains at the notch-tips, resulting in an overestimation or underestimation of the fatigue life. Therefore, a mathematical model that predicts the fatigue lives for 9Cr–1Mo steel at elevated temperature was proposed in terms of the applied stress amplitude and the fatigue stress concentration factor. The calculated fatigue lifetimes using the proposed model are found to be in good agreement with those obtained experimentally from the literature with relative errors, when the applied stress amplitude is ±149.95 MPa, are of 1.97%,–8.67%, and 13.54%, for notch root radii of 1.25, 2.5, and 5 mm, respectively

A Numerical Analysis on the Cyclic Behavior of 316 FR Stainless Steel and Fatigue Life Prediction

The present work aims to predict the cyclic behavior and fatigue life of 316 FR stainless steel specimens at 650 °C. First, the samples were modeled using finite element analysis under different strain amplitudes, and the obtained numerical hysteresis loops were compared against experimental results available in the literature. Then, the fatigue life was estimated using different fatigue life prediction models, namely the Coffin–Manson model, Ostergren’s damage function, and Smith–Watson–Topper model, and was compared to the experimental fatigue life. The obtained results revealed that the numerical cyclic stress–strain data are in good agreement with those obtained experimentally. In addition, the predicted fatigue lives using the previously mentioned fatigue life models and based on the provided equation parameters are within a factor of 2.5 of the experimental results. Accordingly, it is suggested that they can be used to predict the fatigue life of 316 FR stainless steel

Buckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learning

The use of circular hollow sections (CHS) has increased in recent years owing to its excellent mechanical behaviour including axial compression and torsional resistance as well as its aesthetic appearance. They are popular in a wide range of structural members including beams, columns, trusses and arches. The behaviour of hot-finished CHS beam-columns made from normal and high strength steel is the main focus of this paper. A particular attention is given to predict the ultimate buckling resistance of CHS beam-columns using the recent advancement of the artificial neural network (ANN). FE models were established and validated to generate an extensive parametric study. The ANN model is trained and validated using a total of 3439 data points collected from the generated FE models and experimental tests available in the literature. A comprehensive comparative analysis with the design rules in Eurocode 3 is conducted to evaluate the performance of the developed ANN model. It is shown that the proposed ANN based design formula provides a reliable means for predicting the buckling resistance of the CHS beam-columns. This formula can be easily implemented in any programming software, providing an excellent basis for engineers and designers to predict the buckling resistance resistance of the CHS beam-columns with a straightforward procedure in an efficient and sustainable manner with least computational time

FRP RC beams by collected test data: Comparison with design standard, parameter sensitivity, and reliability analyses

Data availability: Data will be made available on request.In the last few years, there has been a significant increase in the utilization of Fiber Reinforced Polymer (FRP) materials as reinforcing elements in concrete structures due to their excellent properties. Unlike traditional steel rebars, FRP rebars do not rust, which helps to prevent degradation and deterioration of the concrete structure over time. This growth has resulted in a rise in the application of design regulations for FRP-reinforced concrete (RC) members. There are currently no European standards that offer suggestions about FRP RC structures. This paper aims to assess the load- carrying capacity and deflection of FRP RC beams with large number of test data available against design standards. The results are compared with ACI 440.1 R-06 specifications and EC2 concepts available in fib Bulletin No. 40. It was found that both ACI and EC2 underestimate the shear flexural capacity. Both design codes presented 38% and 62% of the collected data that overestimated and underestimated the calculation of the deflection, respectively. A parameter influence analysis is performed considering the database collected, and a reliability analysis based on Annex D EN 1990 (2002) is conducted. The reliability analysis allowed suggestion new partial safety factors values of 1.45 and 1.65 for moment and shear capacities, respectively, which can be used by design engineering communities

A Study on Low Cycle Fatigue Life Assessment of Notched Specimens Made of 316 LN Austenitic Stainless Steel

The local strains obtained from the best-known analytical approximations, namely, Neuber's rule, equivalent strain energy density method, and linear rule, were compared with those resulting from finite element analysis. It was found that apart from Neuber's rule with the elastic stress concentration factor ⁠, all the aforementioned analytical methods underestimate the local strains for all notch root radius, strain amplitude levels, at room temperature and 550 °C. Neuber's rule with slightly overestimates the maximum strains for lower notch root radius, namely, 1.25 mm, at high temperature. Based on the analytically and numerically obtained notch root strains, the fatigue lives were estimated using the Coffin–Manson–Basquin equation. Besides, a numerical assessment of fatigue lives was made based on Brown–Miller and maximum shear strain multi-axial fatigue life criteria. It was found that all these methods provide inaccurate fatigue life results for all notch root radius, strain amplitude level, and under both temperatures conditions. Therefore, a new method was suggested, for which only the applied strain amplitude is needed to calculate the fatigue life of notched components. It was revealed that the suggested method provides a good fatigue life prediction at a higher temperature loading state.CNRST (Centre National pour la Recherche Scientifique et Technique) in Rabat-Morocco Ph.D. excellent grant

Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes

Numerical and Analytical Studies of Low Cycle Fatigue Behavior of 316 LN Austenitic Stainless Steel

Mechanical components are frequently subjected to severe cyclic pressure and/or temperature loadings. Therefore, numerical and analytical low cycle fatigue methods become widely used in the field of engineering to estimate the design fatigue lives. The primary aim of this work is to evaluate the accuracy of the most commonly used numerical and analytical low cycle fatigue life methods for specimens made of 316 LN austenitic stainless steel and subjected to fully reversed uniaxial tension–compression loading, in the room temperature condition. It was found that both maximum shear strain and Brown–Miller criterions result in a very conservative estimation for uniaxially loaded specimens. However, maximum shear strain criteria provide better results compared to the Brown–Miller criteria. The total strain energy density approach was also used, and both the Masing and non-Masing analysis were adopted in this study. It is found that the Masing model provides conservative fatigue lives, and non-Masing model results in a more realistic fatigue life prediction for 316 LN stainless steel for both low and high strain amplitudes. The fatigue design curves obtained from the commonly used analytical low cycle fatigue equations were reexamined for 316 LN SS. The obtained design curves from Langer model and its modified versions are nonconservative for this type of material. Consequently, the authors suggest new optimized parameters to fit the given test data. The obtained curve using the currently suggested parameters is in better agreement with the experimental data for 316 LN SS.CNRST (Center National pour la Recherche Scientifique et Technique), Rabat, Morocco Ph.D. student funding

Machine learning-driven web-post buckling resistance prediction for high-strength steel beams with elliptically-based web openings

The use of periodical elliptically-based web (EBW) openings in high strength steel (HSS) beams has been increasingly popular in recent years mainly because of the high strength-to-weight ratio and the reduction in the floor height as a result of allowing different utility services to pass through the web openings. However, these sections are susceptible to web-post buckling (WPB) failure mode and therefore it is imperative that an accurate design tool is made available for prediction of the web-post buckling capacity. Therefore, the present paper aims to implement the power of various machine learning (ML) methods for prediction of the WPB capacity in HSS beams with (EBW) openings and to assess the performance of existing analytical design model. For this purpose, a numerical model is developed and validated with the aim of conducting a total of 10,764 web-post finite element models, considering S460, S690 and S960 steel grades. This data is employed to train and validate different ML algorithms including Artificial Neural Networks (ANN), Support Vector Machine Regression (SVR) and Gene Expression Programming (GEP). Finally, the paper proposes new design models for WPB resistance prediction. The results are discussed in detail, and they are compared with the numerical models and the existing analytical design method. The proposed design models based on the machine learning predictions are shown to be powerful, reliable and efficient design tools for capacity predictions of the WPB resistance of HSS beams with periodical (EBW) openings