Robustness Assessment Based on Pseudo-Static Full Probabilistic Approach

2019 ◽  
Vol 292 ◽  
pp. 123-127
Author(s):  
Andrei Tur
Keyword(s):  
Failure Modes ◽  
Probabilistic Approach ◽  
Structural Systems ◽  
Complex Method ◽  
Load Path ◽  
Nonlinear Method ◽  
Materials Properties ◽  
New Strategy ◽  
Method Paper ◽  
Robustness Assessment

Accordance with a new EN 1992 the robustness of the structural systems must be analyzed. When applying proposed new strategy, it has been verified that the structure has sufficient redundancy and possibility to mobilize so-called alternate load path (AP-method). Paper presents a modified complex method for robustness estimation in case of the RC-structural systems. Proposed pseudo-static method based on the energy-saving approach. Paper also presents a full probabilistic pseudo-static nonlinear method which was developed for structural robustness assessment, taking into account the statistical variability of the materials properties and, as a consequence, parameters of the plastic hinges and possible failure modes.

scholarly journals Machine learning-based feature importance approach for sensitivity analysis of steel frames

2021 ◽  
Author(s):  
Hyeyoung Koh ◽  
Hannah Beth Blum
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Feature Selection ◽  
Large Scale ◽  
Failure Modes ◽  
Model Development ◽  
Predictive Performance ◽  
Computational Effort ◽  
Structural Systems ◽  
Feature Importance

This study presents a machine learning-based approach for sensitivity analysis to examine how parameters affect a given structural response while accounting for uncertainty. Reliability-based sensitivity analysis involves repeated evaluations of the performance function incorporating uncertainties to estimate the influence of a model parameter, which can lead to prohibitive computational costs. This challenge is exacerbated for large-scale engineering problems which often carry a large quantity of uncertain parameters. The proposed approach is based on feature selection algorithms that rank feature importance and remove redundant predictors during model development which improve model generality and training performance by focusing only on the significant features. The approach allows performing sensitivity analysis of structural systems by providing feature rankings with reduced computational effort. The proposed approach is demonstrated with two designs of a two-bay, two-story planar steel frame with different failure modes: inelastic instability of a single member and progressive yielding. The feature variables in the data are uncertainties including material yield strength, Young’s modulus, frame sway imperfection, and residual stress. The Monte Carlo sampling method is utilized to generate random realizations of the frames from published distributions of the feature parameters, and the response variable is the frame ultimate strength obtained from finite element analyses. Decision trees are trained to identify important features. Feature rankings are derived by four feature selection techniques including impurity-based, permutation, SHAP, and Spearman's correlation. Predictive performance of the model including the important features are discussed using the evaluation metric for imbalanced datasets, Matthews correlation coefficient. Finally, the results are compared with those from reliability-based sensitivity analysis on the same example frames to show the validity of the feature selection approach. As the proposed machine learning-based approach produces the same results as the reliability-based sensitivity analysis with improved computational efficiency and accuracy, it could be extended to other structural systems.

HPHT Subsea Connector Verification and Validation Using an API 17TR8 Methodology

2021 ◽  
Author(s):  
Barry Stewart ◽  
Sam Kwok Lun Lee
Keyword(s):  
Failure Modes ◽  
Production Systems ◽  
Three Dimensional ◽  
Full Scale ◽  
Verification And Validation ◽  
Combined Loading ◽  
Load Path ◽  
Capacity Curves ◽  
Industry Standards ◽  
Full Scale Tests

Abstract Wellhead connectors form a critical part of subsea tree production systems. Their location in the riser load path means that they are subjected to high levels of bending and tension loading in addition to internal pressure and cyclic loading. As more fields continue to be discovered and developed that are defined as High Pressure and/or High Temperature (HPHT) these loading conditions become even more arduous. In order to ensure the integrity of HPHT components, industry requirements for components are setout in API 17TR8. This technical report provides a design verification methodology for HPHT products and some requirements for validation testing. The methodology provides detail on the assessment of static structural and cyclic capacities but less detail on how to assess the functional and serviceability criteria for wellhead connectors. Similarly, API 17TR8 does not include prescriptive validation requirements for wellhead connectors and refers back to historical methods. This paper describes a practical application of the API 17TR8 methodology to the development of a 20k HPHT connector and how it was implemented to verify and validate the connector design through full scale tests to failure. A methodology was developed to meet the requirements of the relevant industry standards and applied to the connector to develop capacity charts for static combined loading. Verification was carried out on three dimensional 180° FEA models to ensure all non axi-symmetric loading is accurately captured. Connector capacities are defined based on API 17TR8 criteria with elastic plastic analysis (i.e. collapse load, local failure and ratcheting), functionality/serviceability criteria defined through a FMECA review and also including API STD 17G criteria including failure modes such as lock/unlock functionality, fracture based failure, mechanical disengagement, leakage and preload exceedance. These capacities are validated through full scale testing based on the requirements of API 17TR7 and API STD 17G with combined loading applied to the Normal, Extreme and Survival capacity curves (as defined by "as-built" FEA using actual material properties). Various test parameters such as strain gauge data, hub separation data, displacements, etc. were recorded and correlated to FEA prediction to prove the validity of the methodology. Further validation was carried out by applying a combined load up to the FEA predicted failure to confirm the design margins of the connector. Post-test review was carried out to review the suitability of the requirements set out in API 17TR8 and API STD 17G for the verification and validation of subsea connectors. The results build on previous test results to validate the effectiveness of the API 17TR8 code for verification and validation of connectors. The results show that real margins between failure of the connector and rated loads are higher than those defined in API 17TR8 and show that the methodology can be conservative.

A probabilistic approach to modify the complex method

1982 ◽  
Vol 6 (2) ◽  
pp. 97-99 ◽  
Author(s):  
K.B.S. Prasad ◽  
K.B.S. Saibabu
Keyword(s):  
Probabilistic Approach ◽  
Complex Method

Surrogate-model-based reliability method for structural systems with dependent truncated random variables

2017 ◽  
Vol 231 (3) ◽  
pp. 265-274 ◽  
Author(s):  
Ning-Cong Xiao ◽  
Libin Duan ◽  
Zhangchun Tang
Keyword(s):  
Surrogate Model ◽  
Failure Modes ◽  
Random Variables ◽  
Probability Of Failure ◽  
Structural Systems ◽  
Multiple Failure Modes ◽  
Model Based ◽  
Reliability Sensitivity ◽  
Reliability Method ◽  
Uncertainty Space

Calculating probability of failure and reliability sensitivity for a structural system with dependent truncated random variables and multiple failure modes efficiently is a challenge mainly due to the complicated features and intersections for the multiple failure modes, as well as the correlated performance functions. In this article, a new surrogate-model-based reliability method is proposed for structural systems with dependent truncated random variables and multiple failure modes. Copula functions are used to model the correlation for truncated random variables. A small size of uniformly distribution samples in the supported intervals is generated to cover the entire uncertainty space fully and properly. An accurate surrogate model is constructed based on the proposed training points and support vector machines to approximate the relationships between the inputs and system responses accurately for almost the entire uncertainty space. The approaches to calculate probability of failure and reliability sensitivity for structural systems with truncated random variables and multiple failure modes based on the constructed surrogate model are derived. The accuracy and efficiency of the proposed method are demonstrated using two numerical examples.

A New Probabilistic Approach for Accurate Fatigue Data Analysis of Ceramic Materials

1999 ◽  
Author(s):  
Bjoern Schenk ◽  
Peggy J. Brehm ◽  
M. N. Menon ◽  
William T. Tucker ◽  
Alonso D. Peralta
Keyword(s):  
Data Analysis ◽  
Failure Modes ◽  
Probabilistic Approach ◽  
Time Dependent ◽  
Material Strength ◽  
Static Fatigue ◽  
Oak Ridge ◽  
Fatigue Data ◽  
Ceramic Components ◽  
Dependent Failure

Statistical methods for the design of ceramic components for time-dependent failure modes have been developed which can significantly enhance component reliability, reduce baseline data generation costs, and lead to more accurate estimates of slow crack growth (SCG) parameters. These methods are incorporated into the AlliedSignal Engines CERAMIC and ERICA computer codes. Use of the codes facilitates generation of material strength parameters and SCG parameters simultaneously, by pooling fast fracture data from specimens that are of different sizes, or stressed by different loading conditions, with data derived from static fatigue experiments. The codes also include approaches to calculation of confidence bounds for the Weibull and SCG parameters of censored data and for the predicted reliability of ceramic components. This paper presents a summary of this new fatigue data analysis technique and an example demonstrating the capabilities of the codes with respect to time-dependent failure modes. This work was sponsored by the U.S. Department of Energy Oak Ridge National Laboratory (DoE/ORNL) under Contract No. DE-AC05-84OR21400.

Assessment of Residential Housing in Léogâne, Haiti, and Identification of Needs for Rebuilding after the January 2010 Earthquake

2011 ◽  
Vol 27 (1_suppl1) ◽  
pp. 299-322 ◽  
Author(s):  
Dustin Mix ◽  
Tracy Kijewski-Correa ◽  
Alexandros A. Taflanidis
Keyword(s):  
Failure Modes ◽  
Construction Materials ◽  
Masonry Walls ◽  
Structural Systems ◽  
Residential Housing ◽  
Significant Damage ◽  
The University ◽  
Concrete Elements ◽  
Engineering Constraints

Two months after the 2010 Haiti Earthquake, a reconnaissance team from the University of Notre Dame traveled to Léogâne with a follow up trip in August 2010. The team sought to determine the failure modes for residential housing in the area and survey the structural systems, construction materials, building practices, and non-engineering constraints that dictate these practices. The failure modes observed were commonly initiated from undersized/under-reinforced columns, though even structures with adequately sized columns sustained significant damage due to shear forces transferred by stiff but brittle unreinforced masonry walls. Inadequate seismic detailing of reinforced concrete elements, deficient materials and construction practices, and lack of seismic considerations in the design of structural systems with sufficient lateral interconnectivity were also observed. Finally, strategies now being pursued by the authors will be showcased in an effort to develop multihazard-resilient housing that can be sustained locally.

Fracture and Wear as Factors Affecting Stochastic Tool-Life Models and Machining Economics

1977 ◽  
Vol 99 (1) ◽  
pp. 281-286 ◽  
Author(s):  
S. Rossetto ◽  
R. Levi
Keyword(s):  
Failure Modes ◽  
Probabilistic Approach ◽  
Cutting Tools ◽  
Distribution Functions ◽  
Profit Rate ◽  
Tool Failure ◽  
Factors Affecting ◽  
Machining Economics ◽  
Fracture Processes ◽  
Production Conditions

Under production conditions cutting tools often fail under several failure modes, the occurrence of a single one only for a given operation being rather exceptional. In light of this observation a stochastic model is developed, considering as causes of tool failure both wear and fracture processes. Machining economics are then analyzed with a probabilistic approach, deriving distribution functions of profit rate.

An Optimization Analysis of Prestressed Space Grid Structures Based on System Reliability

2010 ◽  
Vol 97-101 ◽  
pp. 3294-3298
Author(s):  
Chun Yu Zhang ◽  
Zhen Qing Wang ◽  
Mu Qiao
Keyword(s):  
Optimum Design ◽  
System Reliability ◽  
Failure Modes ◽  
Minimum Weight ◽  
Structural Systems ◽  
Control Parameters ◽  
Improved Genetic Algorithm ◽  
Optimization Analysis ◽  
Structure System ◽  
Space Grid

The system reliability of prestressed space grid structures were using as control parameters. Branch-bound method was used to determine main failure modes. The reliability of system was calculated by PNET method. Improved genetic algorithm was used in the minimum weight optimum design of the structure system under reliability constraint. Using this method, the complex sensitivity analysis of structural systems could be avoided and made optimum design of the structure system easy. The result of an example showed that the effectiveness of the method.

scholarly journals Fragility analysis of concrete elevated water tanks under seismic loads

2021 ◽  
Vol 15 (57) ◽  
pp. 93-113
Author(s):  
Hocine Hammoum ◽  
Amar Aliche ◽  
Karima Bouzelha ◽  
Younes Aoues ◽  
Ouali Amiri ◽  
...  
Keyword(s):  
Failure Modes ◽  
Fragility Curves ◽  
Probabilistic Approach ◽  
Seismic Loading ◽  
Soil Types ◽  
Structural Failure ◽  
Safety Factors ◽  
Partial Safety Factors ◽  
Elevated Water ◽  
Water Tanks

The design of concrete elevated water tanks involves several kinds of uncertainties. Traditionally, the design of these structures is based on a deterministic analysis. Partial safety factors prescribed in design codes are applied to take into account these uncertainties and to ensure sufficiently safe design. However, this approach does not allow rational evaluation of the risk related to the structural failure and consequently its reliability. In fact, the partial safety factors can lead to over-designed structures; or to under designed structural components leading to a lack of structural robustness. In this study, a probabilistic approach based on Monte Carlo simulations is used to analyze the reliability of elevated water tanks submitted to hazard seismic loading. This reliability approach, takes into account mainly two parameters. Firstly, the hydraulic charge in the tank container which is a function of time, and secondly, the hazard seismic loading through the Peak Ground Acceleration is considered as a random variable. Fragility curves depending on seismic zones and soil types are obtained by using the probabilistic approach, where they demonstrate the dominant failure modes that can cause the structural failure with respect to different seismic levels, soil types and water height level in the tank container.

Fast and simple dataset selection for machine learning

2019 ◽  
Vol 67 (10) ◽  
pp. 833-842
Author(s):  
Timm J. Peter ◽  
Oliver Nelles
Keyword(s):  
New Method ◽  
Complex Method ◽  
Optimal Point ◽  
Point Distribution ◽  
New Approach ◽  
Original Dataset ◽  
Representative Subset ◽  
New Strategy ◽  
Data Points ◽  
Space Filling Design

Abstract The task of data reduction is discussed and a novel selection approach which allows to control the optimal point distribution of the selected data subset is proposed. The proposed approach utilizes the estimation of probability density functions (pdfs). Due to its structure, the new method is capable of selecting a subset either by approximating the pdf of the original dataset or by approximating an arbitrary, desired target pdf. The new strategy evaluates the estimated pdfs solely on the selected data points, resulting in a simple and efficient algorithm with low computational and memory demand. The performance of the new approach is investigated for two different scenarios. For representative subset selection of a dataset, the new approach is compared to a recently proposed, more complex method and shows comparable results. For the demonstration of the capability of matching a target pdf, a uniform distribution is chosen as an example. Here the new method is compared to strategies for space-filling design of experiments and shows convincing results.

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