A reliability-based probabilistic evaluation of the wave-induced scour depth around marine structure piles

Homaei, Farshad; Najafzadeh, Mohammad

doi:10.1016/j.oceaneng.2019.106818

Cited by 42 publications

(9 citation statements)

References 19 publications

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“…The tails of the distributions are significant in terms of the occurrence of extreme values of the random variable, that is, values with a very low probability of exceedance. Thus, to evaluate the goodness-of-fit of the distributions, the following single-dimensional statistical tests were used: the Kolmogorov-Smirnov test (DK-S) [46,47], the Anderson-Darling test (DA-D) [48], the Liao-Shimokawa test (DL-S) [49], and Kuiper's test (DK) [50]. (For details, see Appendix B.)…”

Section: Problem Formulation and Methodologymentioning

confidence: 99%

A Probabilistic Model for Maximum Rainfall Frequency Analysis

Ciupak

Ozga-Zieliński

Tokarczyk

et al. 2021

Water

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As determining the probability of the exceedance of maximum precipitation over a specified duration is critical to hydrotechnical design, particularly in the context of climate change, a model was developed to perform a frequency analysis of maximum precipitation of a specified duration. The PMAXΤP model (Precipitation MAXimum Time (duration) Probability) harbors a pair of computational modules fulfilling different roles: (i) statistical analysis of precipitation series, and (ii) estimation of maximum precipitation for a specified duration and its probability of exceedance. The input data consist of homogeneous 30-element series of precipitation values for 16 different durations: 5, 10, 15, 30, 45, 60, 90, 120, 180, 360, 720, 1080, 1440, 2160, 2880, and 4320 min, obtained through Annual Maximum Precipitation (AMP) and Peaks-Over-Threshold (POT) approaches. The statistical analysis of the precipitation series includes: (i) detecting outliers using the Grubbs–Beck test; (ii) checking for the random variable’s independence using the Wald–Wolfowitz test and the Anderson serial correlation coefficient test; (iii) checking the random variable’s stationarity using nonparametric tests, e.g., the Kruskal–Wallis test and Spearman rank correlation coefficient test for trends of mean and variance; (iv) identifying the trend of the random variables using correlation and regression analysis, including an evaluation of the form of the trend function; and (v) checking for the internal correlation of the random variables using the Anderson autocorrelation coefficient test. To estimate maximum precipitations of a specified duration and with a specified probability of exceedance, three-parameter theoretical probability distributions were used: a shifted gamma distribution (Pearson type III), a log-normal distribution, a Weibull distribution (Fisher–Tippett type III), a log-gamma distribution, as well as a two-parameter Gumbel distribution. The best distribution was selected by: (i) maximum likelihood estimation of parameters; (ii) tests of the hypothesis of goodness of fit of the theoretical probability distribution function with the empirical distribution using Pearson’s χ2 test; (iii) selection of the best-fitting function within each type according to the criterion of minimum Kolmogorov distance; (iv) selection of the most credible probability distribution function from the set of various types of best-fitting functions according to the Akaike information criterion; and (v) verification of the most credible function using single-dimensional tests of goodness of fit: the Kolmogorov–Smirnov test, the Anderson–Darling test, the Liao–Shimokawa test, and Kuiper’s test. The PMAXTP model was tested on data from two meteorological stations in northern Poland (Chojnice and Bialystok) drawn from a digital database of high-resolution precipitation records for the period of 1986 to 2015, available for 100 stations in Poland (i.e., the Polish Atlas of Rainfall Intensities (PANDa)). Values of maximum precipitation with a specified probability of exceedance obtained from the PMAXTP model were compared with values obtained from the probabilistic Bogdanowicz–Stachý model. The comparative analysis was based on the standard error of fit, graphs of the density function for the probability of exceedance, and estimated quantile errors. The errors of fit were lower for the PMAXTP compared to the Bogdanowicz–Stachý model. For both stations, the smallest errors were obtained for the quantiles determined on the basis of maximum precipitation POT using PMAXTP.

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Section: Problem Formulation and Methodologymentioning

confidence: 99%

A Probabilistic Model for Maximum Rainfall Frequency Analysis

Ciupak

Ozga-Zieliński

Tokarczyk

et al. 2021

Water

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“…For a given problem, LSF is difference between available capacity for output of system and values obtained by mathematical expressions govern on system variables. Therefore, the probability of limit state of violation is described as (Zampieri et al, 2016;Homaei and Najafzadeh, 2020),…”

Section: Probabilistic Analysis Of Break Rate Modelmentioning

confidence: 99%

Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques

Amiri-Ardakani

Najafzadeh

2021

Water Resour Manage

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Deterioration of urban Water Distribution Networks (WDNs) is one of the primary cases of water supply losses, leading to the huge expenditures on the replacement and rehabilitation of elementsWDNs. An accurate prediction of pipes failure rate play a substantial role in the management of WDNs. In this study, a field study was conducted to register pipes break and relevant causes in the WDN of Yazd City, Iran. In this way, 851 water pipes were incepted and localized by the Global Positioning System (GPS) apparatus. Then, 1033 failure cases were reported in the eight zones of under study WDN during March-December 2014. Pipes break rate (BRP) was calculated using the depth of pipe installation (hP), number of failure (NP), pressure of water pipes in operation (P), and age of pipe (AP). After completing a pipe break database, robust Artificial Intelligence models, namely Multivariate Adaptive Regression Spline (MARS), Gene-Expression Programming (GEP), and M5 Model Tree were employed to extract precise formulation for the pipes break rate estimation. Results of the proposed relationships demonstrated that MARS model with Coefficient of Correlation (R) of 0.981 and Root Mean Square Error (RMSE) of 0.544 provided more satisfying efficiency than M5 model (R=0.888 and RMSE=1.096). Furthermore, statistical results indicated that MARS and GEP models had comparatively at the same accuracy level. Explicit 2 equations by AI models were satisfactorily comparable with those obtained by literature review in terms of various conditions: physical, operational, and environmental factors and complexity of AI models. Through a probabilistic framework for the pipes break rate, the results of first-order reliability analysis that MARS technique had highly satisfying performance when MARSextracted-equation was assigned as a limit state function.

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“…In recent years, with the application of machine learning in various fields of science and engineering [6][7][8][9][10][11], many researchers use data-driven methods to solve geological problems. Some intelligent systems [2,12,13] have been used to improve the prediction and accuracy of sonic wave velocity prediction when sonic logs have been lost due to poor storage, poor logging, failure of logging instruments, and bad hole conditions.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

Liu

Zhao

Wang

2021

Mathematical Problems in Engineering

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The existing artificial intelligence model uses single-point logging data as the eigenvalue to predict shear wave travel times (DTS), which does not consider the longitudinal continuity of logging data along the reservoir and lacks the multiwell data processing method. Low prediction accuracy of shear wave travel time affects the accuracy of elastic parameters and results in inaccurate sand production prediction. This paper establishes the shear wave prediction model based on the standardization, normalization, and depth correction of conventional logging data with five artificial intelligence methods (linear regression, random forest, support vector regression, XGBoost, and ANN). The adjacent data points in depth are used as machine learning eigenvalues to improve the practicability of interwell and the accuracy of single-well prediction. The results show that the model built with XGBoost using five points outperforms other models in predicting. The R2 of 0.994 and 0.964 are obtained for the training set and testing set, respectively. Every model considering reservoir vertical geological continuity predicts test set DTS with higher accuracy than single-point prediction. The developed model provides a tool to determine geomechanical parameters and give a preliminary suggestion on the possibility of sand production where shear wave travel times are not available. The implementation of the model provides an economic and reliable alternative for the oil and gas industry.

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A reliability-based probabilistic evaluation of the wave-induced scour depth around marine structure piles

Cited by 42 publications

References 19 publications

A Probabilistic Model for Maximum Rainfall Frequency Analysis

A Probabilistic Model for Maximum Rainfall Frequency Analysis

Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques

Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

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