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Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Never-theless, five soft constrained machine learning algorithms (SCMLAs), where soft con-straints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms., QC 20230126

Premature and unexpected creep damage is a significant concern in high-temperature engineering. Identifying outliers in creep rupture data is essential for assessing the risk of premature creep failure. This study proposes a new method to evaluate premature creep failure using log-logistic distribution fit of prediction errors and outlier positions. Fitting results for seven different alloys were obtained from extrapolation procedures using soft-constrained machine learning algorithms (SCMLAs) and constrained time-temperature parameters (TTPs) based on prior research. A comprehensive statistical analysis was conducted for all materials. The log-logistic distribution was validated as a suitable method for fitting prediction error distributions. Regression plots demonstrate effective residual balance and accurate outlier capture. The best fitting methods were identified based on the width of the distributions. Outlier positions were used to evaluate the probability of premature creep failure quantitatively. For example, a 0.5% probability of observing creep rupture strengths that are approximately 50% lower than the standardized creep stress was found for TP316H, T321H, and high Cr steels SUH616B. These findings offer valuable insights for estimating premature creep failure in materials., QC 20230822