SAFETY AND RELIABILITY OF BREAKWATERS

1984 ◽  
Vol 1 (19) ◽  
pp. 164 ◽  
Author(s):  
A. Mol ◽  
R.L. Groeneveld ◽  
A.J. Waanders
Keyword(s):  
Reliability Analysis ◽  
Probability Distributions ◽  
Simulation Method ◽  
Design Parameters ◽  
Suggested Approach ◽  
Design Procedures ◽  
Traditional Design ◽  
Safety And Reliability ◽  
Event Trees ◽  
Rubble Mound Breakwaters

This paper discusses the need to incorporate a reliability analysis in the design procedures for rubble mound breakwaters. Such an analysis is defined and a suggested approach is outlined. Failure mechanisms are analysed and categorized in Damage Event Trees. The probability of failure is computed using a level III simulation method to include time and cumulative effects and to account for skewed probability distributions. Typical outputs of the computer program are shown and compared with results according to traditional design approaches. The paper concludes that there is a definite need to include reliability analysis in the design procedures for larger breakwaters and such an analysis must consider the accuracy of design parameters and methods.

Reliability Analysis of Mechanical Systems Based on the First Four Moments of Input Parameters

2020 ◽  
Author(s):  
Singiresu S. Rao ◽  
Yang Zhou
Keyword(s):  
Reliability Analysis ◽  
Fourth Order ◽  
Probability Distributions ◽  
Limit State ◽  
Moment Function ◽  
Design Parameters ◽  
Order Moment ◽  
State Function ◽  
Exact Probability ◽  
First Four Moments

Abstract The performance of a mechanical or structural system can be improved through a proper selection of its design parameters such as the geometric dimensions, external actions (loads) and material characteristics. The computation of the reliability of a system, in general, requires a knowledge of the probability distributions of the parameters of the system. It is known that for most practical systems, the exact probability distributions of the parameters are not known. However, the first few moments of the parameters of the system may be readily available in many cases from experimental data. The determination of the reliability and the sensitivity of reliability to variations or fluctuations in the parameters of the system starts with the establishment of a suitable limit state equation. This work presents a reliability analysis approach for mechanical and structural systems using the fourth order moment function for approximating the first four moments of the limit state function. By combining the fourth-order moment function with the probabilistic perturbation method, numerical methods are developed for finding the reliability and sensitivity of reliability of the system. An automobile brake and a power screw are considered for demonstrating the methodology and effectiveness of the proposed computational approach. The results of the automobile brake are compared with those given by the Monte Carlo method.

Uncertainty in Collapse Strength Prediction of Sandwich Pipelines

2021 ◽  
Author(s):  
U. Bhardwaj ◽  
A. P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Probabilistic Models ◽  
Uncertainty Propagation ◽  
Simulation Method ◽  
External Pressure ◽  
Design Parameters ◽  
Collapse Strength ◽  
Parameters Uncertainty ◽  
Wide Range ◽  
Model Predictions ◽  
Strength Models

Abstract This paper assesses the uncertainty in the collapse strength of sandwich pipelines under external pressure predicted by various strength models in three categories based on interlayer adhesion conditions. First, the validity of the strength models is verified by comparing their predictions with sandwich pipeline collapse test data and the corresponding model uncertainty factors are derived. Then, a parametric analysis of deterministic collapse strength predictions by models is conducted, illustrating insights of models’ behaviour for a wide range of design configurations. Furthermore, the uncertainty among different model predictions is perceived at different configurations of outer and inner pipes and core thicknesses. A case study of a realistic sandwich pipeline is developed, and probabilistic models are defined to basic design parameters. Uncertainty propagation of models’ predictions is assessed by the Monte Carlo simulation method. Finally, the strength model predictions of sandwich pipelines are compared to that of an equivalent single walled pipe.

Implementing the Mechanistic–Empirical Design Guide Procedure for a Hot-Mix Asphalt–Rehabilitated Pavement in Indiana

2005 ◽  
Vol 1919 (1) ◽  
pp. 121-133 ◽  
Author(s):  
Khaled A. Galal ◽  
Ghassan R. Chehab
Keyword(s):  
Hot Mix Asphalt ◽  
Pavement Design ◽  
Design Parameters ◽  
Design Procedures ◽  
Design Concepts ◽  
Asphalt Overlay ◽  
Design Guide ◽  
Strategic Goals ◽  
And Performance

One of the Indiana Department of Transportation's (INDOT's) strategic goals is to improve its pavement design procedures. This goal can be accomplished by fully implementing the 2002 mechanistic–empirical (M-E) pavement design guide (M-E PDG) once it is approved by AASHTO. The release of the M-E PDG software has provided a unique opportunity for INDOT engineers to evaluate, calibrate, and validate the new M-E design process. A continuously reinforced concrete pavement on I-65 was rubblized and overlaid with a 13–in.-thick hot-mix asphalt overlay in 1994. The availability of the structural design, material properties, and climatic and traffic conditions, in addition to the availability of performance data, provided a unique opportunity for comparing the predicted performance of this section using the M-E procedure with the in situ performance; calibration efforts were conducted subsequently. The 1993 design of this pavement section was compared with the 2002 M-E design, and performance was predicted with the same design inputs. In addition, design levels and inputs were varied to achieve the following: ( a) assess the functionality of the M-E PDG software and the feasibility of applying M-E design concepts for structural pavement design of Indiana roadways, ( b) determine the sensitivity of the design parameters and the input levels most critical to the M-E PDG predicted distresses and their impact on the implementation strategy that would be recommended to INDOT, and ( c) evaluate the rubblization technique that was implemented on the I-65 pavement section.

Reliability Analysis of Redundant Dynamic Positioning Control System With Human Factor Involved

2016 ◽  
Author(s):  
Fang Wang ◽  
Yong Bai ◽  
Feng Xu
Keyword(s):  
Control System ◽  
Markov Process ◽  
Reliability Analysis ◽  
Oil And Gas ◽  
Dynamic Positioning ◽  
Positioning Control ◽  
Safety And Reliability ◽  
Long Time ◽  
Component Failure Rates ◽  
Technical Failures

Deepwater oil and gas explorations bring more safety and reliability problems for the dynamically positioned vessels. With the demands for the safety of vessel crew and onboard device increasing, the single control architecture of dynamic positioning (DP) system can not guarantee the long-time faultless operation for deeper waters, which calls for much more reliable control architectures, such as the Class 2 and Class 3 system, which can tolerate a single failure of system according to International Maritime Organization’s (IMO) DP classification. The reliability analysis of the main control station of DP Class 3 system is proposed from a general technical prospective. The fault transitions of the triple-redundant DP control system are modeled by Markov process. The effects of variation in component failure rates on the system reliability are investigated. Considering the DP operation involved a human-machine system, the DP operator factors are taken into account, and the human operation error failures together with technical failures are incorporated to the Markov process to predict the reliability of the DP control system.

Heat Exchange Calculation of a Regenerative Air Heater with Numerical Simulation Method

2013 ◽  
Vol 860-863 ◽  
pp. 1416-1419
Author(s):  
Ri Guang Wei ◽  
Zhen Xiao Qu ◽  
Jian Qiang Gao
Keyword(s):  
Numerical Simulation ◽  
Simulation Method ◽  
Calculation Model ◽  
Design Parameters ◽  
Practical Calculation ◽  
Air Preheater ◽  
Air Heater ◽  
Pulverized Coal Boiler ◽  
Set Up ◽  
Coal Boiler

According to the structure and working principle of rotary air preheater,the heat transfer calculation model is set up with reasonable simplification. Combining with the design parameters of the rotary air preheater of a 400 t/h pulverized coal boiler unit ,the results of practical calculation show that the said thermodynamic calculation method not only has higher precision of calculation,but also can get the temperature distributions of the gas, air and heat surface in each cross-section of the rotary air preheater. The result of numerical simulation calculation tallies well with the original designed data. It can be used for the heat calculation both two-sectorial and three-sectorial air heater; it can be used for performance analysis of the regenerative air heater.

scholarly journals Semi-Supervised Deep Learning for High-Dimensional Uncertainty Quantification

2020 ◽  
Author(s):  
Zequn Wang ◽  
Mingyang Li
Keyword(s):  
Uncertainty Quantification ◽  
Reliability Analysis ◽  
Supervised Learning ◽  
Dimensional Space ◽  
Limit State ◽  
Failure Surface ◽  
Simulation Method ◽  
High Dimensional ◽  
State Function ◽  
Latent Space

Abstract Conventional uncertainty quantification methods usually lacks the capability of dealing with high-dimensional problems due to the curse of dimensionality. This paper presents a semi-supervised learning framework for dimension reduction and reliability analysis. An autoencoder is first adopted for mapping the high-dimensional space into a low-dimensional latent space, which contains a distinguishable failure surface. Then a deep feedforward neural network (DFN) is utilized to learn the mapping relationship and reconstruct the latent space, while the Gaussian process (GP) modeling technique is used to build the surrogate model of the transformed limit state function. During the training process of the DFN, the discrepancy between the actual and reconstructed latent space is minimized through semi-supervised learning for ensuring the accuracy. Both labeled and unlabeled samples are utilized for defining the loss function of the DFN. Evolutionary algorithm is adopted to train the DFN, then the Monte Carlo simulation method is used for uncertainty quantification and reliability analysis based on the proposed framework. The effectiveness is demonstrated through a mathematical example.

scholarly journals Technical-Economic Analysis of Grapple Saw

2020 ◽  
Vol 41 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Ricardo Hideaki Miyajima ◽  
Paulo Torres Fenner ◽  
Gislaine Cristina Batistela ◽  
Danilo Simões
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distributions ◽  
Simulation Method ◽  
Forest Harvesting ◽  
Production Costs ◽  
Wood Harvesting ◽  
Food And Agriculture ◽  
The Monte Carlo Method

The processing of Eucalyptus logs is a stage that follows the full tree system in mechanized forest harvesting, commonly performed by grapple saw. Therefore, this activity presents some associated uncertainties, especially regarding technical and silvicultural factors that can affect productivity and production costs. To get around this problem, Monte Carlo simulation can be applied, or rather a technique that allows to measure the probabilities of values from factors that are under conditions of uncertainties, to which probability distributions are attributed. The objective of this study was to apply the Monte Carlo method for determining the probabilistic technical-economical coefficients of log processing using two different grapple saw models. Field data were obtained from an area of forest planted with Eucalyptus, located in the State of São Paulo, Brazil. For the technical analysis, the time study protocol was applied by the method of continuous reading of the operational cycle elements, which resulted in production. As for the estimated cost of programmed hour, the applied methods were recommended by the Food and Agriculture Organization of the United Nations. The incorporation of the uncertainties was carried out by applying the Monte Carlo simulation method, by which 100,000 random values were generated. The results showed that the crane empty movement is the operational element that most impacts the total time for processing the logs; the variables that most influence the productivity are specific to each grapple saw model; the difference of USD 0.04 m3 in production costs was observed between processors with gripping area of 0.58 m2 and 0.85 m2. The Monte Carlo method proved to be an applicable tool for mechanized wood harvesting for presenting a range of probability of occurrences for the operational elements and for the production cost.

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