scholarly journals Risk Assessment in Advanced Engineering Design

10.14311/432 ◽  
2003 ◽  
Vol 43 (3) ◽  
Author(s):  
M. Holický
Keyword(s):  
Engineering Design ◽  
Probability Of Failure ◽  
Extreme Conditions ◽  
Probabilistic Design ◽  
Engineering Systems ◽  
Engineering Structures ◽  
Design Procedures ◽  
Target Probability ◽  
Acceptable Risks ◽  
Fundamental Quantity

Traditional methods for designing of civil engineering structures and other engineering systems are frequently based on the concept of target probability of failure. However, this fundamental quantity is usually specified on the basis of comparative studies and past experience only. Moreover, probabilistic design methods suffer from several deficiencies, including lack of consideration for accidental and other hazard situations and their consequences. Both of these extreme conditions are more and more frequently becoming causes of serious failures and other adverse events. Available experience clearly indicates that probabilistic design procedures may be efficiently supplemented by a risk analysis and assessment, which can take into account various consequences of unfavourable events. It is therefore anticipated that in addition to traditional probabilistic concepts the methods of advanced engineering design will also commonly include criteria for acceptable risks.

scholarly journals Prospects for Advanced Engineering Design Based on Risk Assessment

10.14311/244 ◽  
2001 ◽  
Vol 41 (4-5) ◽  
Author(s):  
M. Holický
Keyword(s):  
Risk Assessment ◽  
Engineering Design ◽  
Probabilistic Approach ◽  
Design Procedure ◽  
Probability Of Failure ◽  
Probabilistic Design ◽  
Target Probability ◽  
Acceptable Risks ◽  
Near Future ◽  
Causes Of Failure

Current approaches to the design of structures are based on the concept of target probability of failure. This value is, however, often specified on the basis of comparative studies and past experience only. Moreover, the traditional probabilistic approach cannot properly consider gross errors and accidental situations, both of which are becoming more frequent causes of failure. This paper shows that it is useful to supplement a probabilistic design procedure by a risk analysis and assessment, which can take into account the consequences of all unfavourable events. It is anticipated that in the near future advanced engineering design will include criteria of acceptable risks in addition to the traditional probabilistic conditions.

Calibration of reliability-based safety factors for sand boiling in excavations

2020 ◽  
Vol 57 (5) ◽  
pp. 742-753
Author(s):  
Ignatius Tommy Pratama ◽  
Chang-Yu Ou ◽  
Jianye Ching
Keyword(s):  
Factor Of Safety ◽  
Probability Of Failure ◽  
Reliability Theory ◽  
Design Codes ◽  
Test Results ◽  
Safety Factors ◽  
Target Probability ◽  
Analysis Methods ◽  
Actual Factor ◽  
The Relationship

This study calibrated the required factors of safety of five analysis methods for sand boiling using reliability theory. The factors of safety computed by the five analysis methods were compared with the results of a series of sand boiling model tests. The comparison shows that rigorous methods (Terzaghi’s and Harza’s methods) were more accurate in predicting the factors of safety compared to the simplified methods (Harr’s, simplified Terzaghi’s, and simplified Harza’s methods). The statistics of the model factor for each method, defined as the actual factor of safety divided by the computed one, was calibrated by the model test results. These statistics were then used to establish the relationship between the target probability of failure and the required factor of safety by reliability theory. Verification using a full-scale sand boiling case history shows that the required factor of safety calibrated by the reliability theory was more reasonable than the required factors of safety in references and design codes.

Modular Distributed Models of Engineering Structures

2003 ◽  
Author(s):  
Clark J. Radcliffe ◽  
Jon Sticklen
Keyword(s):  
Engineering Design ◽  
Degrees Of Freedom ◽  
Structural Model ◽  
Integrated Design ◽  
Model Systems ◽  
Structural Stiffness ◽  
Engineering Structures ◽  
Distributed Modeling ◽  
Communications Network ◽  
Component Models

Approaches to engineering design and manufacturing such as integrated design and manufacture and just in time fabrication depend on interaction with and among component supply companies that most often use very diverse technologies. The Internet Engineering Design Agents (i-EDA) software system uses a distributed, component-based, agent methodology that is realized following a strong black box approach to modeling. An individual Design Agent (DA) is a virtual product capable of encapsulating both descriptive and model based information about the product it represents. Hierarchically recursive agents for sub-systems and/or components are linked via a communications network to form larger integrated model systems. A two dimensional bridge system structural model is used as an example to illustrate the distributed assembly of structural models from components registered as DA’s on a communications network. Modular Distributed Modeling (MDM) of engineering structures performs static deflection analysis using traditional, fixed causality, structural stiffness models. This paper presents the methodology required to assemble traditional structural stiffness models provided by internet agents representing structural components. The methodology discussed assembles these component models into the structural stiffness model of an assembly distributed by an agents represent that physical assembly of components. Using this modular distributed modeling method; models of complex assemblies can be built and distributed while hiding the topology and characteristics of their structural subassemblies. The automated, modular, assembly of structural stiffness models will be derived for discrete physical connections. Discrete connections are important to the assembly of components such as truss and shaft structures where the relationship between component displacements involve discrete, matching, degrees of freedom on components to be assembled. Specific examples of discrete assembly of truss bridge component models will be presented.

Analysis of the Partial Safety Factor Method Using Probabilistic Techniques

2010 ◽  
Author(s):  
B. A. Lindley ◽  
P. M. James
Keyword(s):  
Probabilistic Method ◽  
Probability Of Failure ◽  
Safety Factors ◽  
First Order Reliability Method ◽  
First Order ◽  
Target Probability ◽  
Reliability Method ◽  
Input Parameters ◽  
Partial Safety Factors ◽  
Hand Calculation

Partial Safety Factors (PSFs) are scaling factors which are used to modify the input parameters to a deterministic fracture mechanics assessment in order to consider the effects of variability or uncertainty in the values of the input parameters. BS7910 and SINTAP have adopted the technique, both of which use the First Order Reliability Method (FORM) to derive values for PSFs. The PSFs are tabulated, varying with the target probability of failure, p(F), and the Coefficient of Variance (COV) of the variable. An accurate assessment of p(F) requires a probabilistic method with enough simulations. This has previously been found to be time consuming, due to the large number of simulations required. The PSF method has been seen as a quick way of calculating an approximate, conservative value of p(F). This paper contains a review of the PSF method, conducted using an efficient probabilistic method called the Hybrid probabilistic method. The Hybrid probabilistic method is used to find p(F) at a large number of assessment points, for a range of different PSFs. These p(F) values are compared to those obtained using the PSF method. It is found that the PSF method was usually, and often extremely, conservative. However there are also cases where the PSF method was non-conservative. This result is verified by a hand calculation. Modifications to the PSF method are suggested, including the establishment of a minimum PSF on each variable to reduce non-conservatisms. In light of the existence of efficient probabilistic techniques, the non-conservatisms that have been found in the PSF method, coupled with the impracticality of completely removing these non-conservatisms, it is recommended that a full probabilistic assessment should generally be performed.

Confidence Interval Simulation for Systems of Random Variables

2005 ◽  
Vol 129 (3) ◽  
pp. 836-842 ◽  
Author(s):  
Thomas A. Cruse ◽  
Jeffrey M. Brown
Keyword(s):  
Experimental Data ◽  
Confidence Interval ◽  
Bayesian Network ◽  
Upper Bound ◽  
Random Variables ◽  
Network Models ◽  
Probability Of Failure ◽  
Cumulative Distribution ◽  
Probabilistic Design ◽  
Design Assessment

Bayesian network models are seen as important tools in probabilistic design assessment for complex systems. Such network models for system reliability analysis provide a single probability of failure value whether the experimental data used to model the random variables in the problem are perfectly known or derive from limited experimental data. The values of the probability of failure for each of those two cases are not the same, of course, but the point is that there is no way to derive a Bayesian type of confidence interval from such reliability network models. Bayesian confidence (or belief) intervals for a probability of failure are needed for complex system problems in order to extract information on which random variables are dominant, not just for the expected probability of failure but also for some upper bound, such as for a 95% confidence upper bound. We believe that such confidence bounds on the probability of failure will be needed for certifying turbine engine components and systems based on probabilistic design methods. This paper reports on a proposed use of a two-step Bayesian network modeling strategy that provides a full cumulative distribution function for the probability of failure, conditioned by the experimental evidence for the selected random variables. The example is based on a hypothetical high-cycle fatigue design problem for a transport aircraft engine application.

Applications and prospects for AI in mechanical engineering design

1990 ◽  
Vol 5 (3) ◽  
pp. 167-179 ◽  
Author(s):  
Ian M. Carter
Keyword(s):  
Engineering Design ◽  
Conceptual Design ◽  
Mechanical Engineering ◽  
Subject Area ◽  
Computer Support ◽  
Design Activity ◽  
Engineering Systems ◽  
Design Systems ◽  
Broad Subject ◽  
Future Topics

AbstractMechanical engineering design is a broad subject area covering many topics and bas influences upon many other engineering disciplines and activities. Computer support for mechanical engineering design activity has been in draughting Systems and analysis packages, but there has been little in conceptual design assistance. This paper presents a number of areas of work in which AI techniques and developments are being used, sometimes in conjunction with traditional methods, to improve the support of design. The approaches to design and design Systems are covered, along with some techniques that are used. Specifie design Systems illustrate progress, and integration issues and simultaneous engineering Systems indicate the way research is moving. Finally, discussion of the trends and future topics indicates where and how effort may be applied in the future.

scholarly journals End-to-end engineering technologies as resource-saving technologies

2021 ◽  
Vol 279 ◽  
pp. 01005
Author(s):  
Mariya Kilina ◽  
Vyacheslav Grishenko ◽  
Denis Dymochkin
Keyword(s):  
Engineering Design ◽  
Industry 4.0 ◽  
Resource Saving ◽  
Design Procedures ◽  
Parallel Design ◽  
End To End ◽  
Design Objects ◽  
Iterative Procedures ◽  
The Cost ◽  
Engineering Personnel

The article discusses approaches and methods to engineering design in light of INDUSTRY 4.0 technology. Versions of serial and parallel design are described. An analysis of end-to-end design in accordance with INDUSTRY 4.0 technologies is presented, the advantages of this approach to creating complex mechanical engineering facilities are shown. These examples show a reduction in resources and time to develop new design objects. So the use of Wave technology in design allows you to reduce the cost of engineering personnel by 66%, increase iterative procedures while reducing the time of design procedures, reduce the number of elements of the system, without reducing its reliability.

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