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Article
Publication date: 1 April 2005

Interval Factor Method for Interval Finite Element Analysis of Truss Structures

Ma Juan, Chen Jian‐jun, Zhang Jian‐guo and Jiang Tao

The uncertainty of the interval variable is represented by interval factor, and the interval variable is described as its mean value multiplied by its interval factor…

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Abstract

The uncertainty of the interval variable is represented by interval factor, and the interval variable is described as its mean value multiplied by its interval factor. Based on interval arithmetic rules, an analytical method of interval finite element for uncertain structures but not probabilistic structure or fuzzy structure is presented by combining the interval analysis with finite element method. The static analysis of truss with interval parameters under interval load is studied and the expressions of structural interval displacement response and stress response are deduced. The influences of uncertainty of one of structural parameters or load on the displacement and stress of the structure are examined through examples and some significant conclusions are obtained.

Details

Multidiscipline Modeling in Materials and Structures, vol. 1 no. 4
Type: Research Article
DOI: https://doi.org/10.1163/157361105774501674
ISSN: 1573-6105

Keywords

  • Truss with interval parameters
  • Interval factor method
  • Interval arithmetic
  • Static analysis
  • interval finite element

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Book part
Publication date: 6 January 2016

Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement

Laura E. Jackson, M. Ayhan Kose, Christopher Otrok and Michael T. Owyang

We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model…

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Abstract

We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single-factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state-space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.

Details

Dynamic Factor Models
Type: Book
DOI: https://doi.org/10.1108/S0731-905320150000035009
ISBN: 978-1-78560-353-2

Keywords

  • Principal components
  • Kalman filter
  • data augmentation
  • business cycles
  • C3
  • C18
  • C32
  • E32

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Article
Publication date: 14 December 2020

A new risk evaluation methodology based on FMEA, MULTIMOORA, TPOP, and interval-valued hesitant fuzzy linguistic sets with an application to healthcare industry

S. Zolfaghari and S.M. Mousavi

The healthcare system is regarded as one of the most critical service industries. The surgical unit is the heart of hospitals in that any failures directly affect the…

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Abstract

Purpose

The healthcare system is regarded as one of the most critical service industries. The surgical unit is the heart of hospitals in that any failures directly affect the safety of patients, so they should be managed thoroughly. It is an intricate multi-attributes decision-making problem with uncertainty. Uncertain information in the form of fuzzy sets theory has been applied widely to describe the different aspects of system uncertainty. This study aims to present a new methodology to manage the healthcare system failures due to the multi-attributes decision-making process.

Design/methodology/approach

This study introduces a new risk evaluation methodology by failure mode and effect analysis (FMEA) and MULTIMOORA method. Group decision-making process in this methodology is presented under uncertain information in the form of interval-valued hesitant fuzzy linguistic sets (IVHFLSs). IVHFLSs encompass both qualitative and quantitative interpretation of experts to reflect their preferences, as well the ability and flexibility of derivation of linguistic information by several linguistic terms increase. To avoid the different ranking order of MULTIMOORA approaches, a new interval multi-approaches multi-attribute methodology, namely, technique of precise order preference (TPOP), is extended to provide precise ranking order.

Findings

The application and precision of proposed integrated IVHFL-MULTIMOORA methodology with TPOP is examined in a case study of healthcare systems. The results indicate the superiority of proposed methodology to prioritize and assess the failures as well as handling system uncertainty.

Originality/value

This study addresses the challenges of an organization to prioritize potential failures by implementing FMEA method. Moreover, this paper contributes to making the manager's ability in decision-making. The value of this study can be discussed in two aspects. First and foremost, this study provides a new FMEA-based methodology to rank failures precisely. The results prove that the proposed methodology is more robust to changes of different ranking order methods, applied by FMEA. On the other hand, using the capability of IVHFLSs allows collecting accurate information in an ambiguous and uncertain environment.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
DOI: https://doi.org/10.1108/K-03-2020-0184
ISSN: 0368-492X

Keywords

  • Healthcare systems
  • Risk evaluation
  • Interval-valued hesitant fuzzy linguistic sets
  • MULTIMOORA method
  • Failure mode and effect analysis (FMEA)
  • Technique of precise order preference (TPOP)

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Book part
Publication date: 13 August 2018

Research Methods in I/O Psychology

Robert L. Dipboye

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Abstract

Details

The Emerald Review of Industrial and Organizational Psychology
Type: Book
DOI: https://doi.org/10.1108/978-1-78743-785-220181005
ISBN: 978-1-78743-786-9

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Article
Publication date: 15 April 2020

A novel approach to emergency risk assessment using FMEA with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment

Huimin Li, Lelin Lv, Feng Li, Lunyan Wang and Qing Xia

The application of the traditional failure mode and effects analysis (FMEA) technique has been widely questioned in evaluation information, risk factor weights and…

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Abstract

Purpose

The application of the traditional failure mode and effects analysis (FMEA) technique has been widely questioned in evaluation information, risk factor weights and robustness of results. This paper develops a novel FMEA framework with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment to solve these problems.

Design/methodology/approach

This paper introduces innovatively interval-value Pythagorean fuzzy weighted averaging (IVPFWA) operator, Tchebycheff metric distance and interval-value Pythagorean fuzzy weighted geometric (IVPFWG) operator into the MULTIMOORA submethods to obtain the risk ranking order for emergencies. Finally, an illustrative case is provided to demonstrate the practicality and feasibility of the novel fuzzy FMEA framework.

Findings

The feasibility and validity of the proposed method are verified by comparing with the existing methods. The calculation results indicate that the proposed method is more consistent with the actual situation of project and has more reference value.

Practical implications

The research results can provide supporting information for risk management decisions and offer decision-making basis for formulation of the follow-up emergency control and disposal scheme, which has certain guiding significance for the practical popularization and application of risk management strategies in the infrastructure projects.

Originality/value

A novel approach using FMEA with extended MULTIMOORA method is developed under IVPF environment, which considers weights of risk factors and experts. The method proposed has significantly improved the integrity of information in expert evaluation and the robustness of results.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 1
Type: Research Article
DOI: https://doi.org/10.1108/IJICC-08-2019-0091
ISSN: 1756-378X

Keywords

  • Failure mode and effects analysis
  • Emergency
  • MULTIMOORA
  • Interval-valued Pythagorean fuzzy sets
  • Risk management

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Article
Publication date: 16 March 2020

Simulation of the temperature field for massive concrete structures using an interval finite element method

Zhiqiang Xie, Lei Wang, Zhengyang Zhu, Zhi Fu and Xingdong Lv

The purpose of this paper is to introduce an interval finite element method (IFEM) to simulate the temperature field of mass concrete under multiple influence…

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Abstract

Purpose

The purpose of this paper is to introduce an interval finite element method (IFEM) to simulate the temperature field of mass concrete under multiple influence uncertainties e.g. environmental temperature, material properties, pouring construction and pipe cooling.

Design/methodology/approach

Uncertainties of the significant factors such as the ambient temperature, the adiabatic temperature rise, the placing temperature and the pipe cooling are comprehensively studied and represented as the interval numbers. Then, an IFEM equation is derived and a method for obtaining interval results based on monotonicity is also presented. To verify the proposed method, a non-adiabatic temperature rise test was carried out and subsequently simulated with the method. An excellent agreement is achieved between the simulation results and the monitoring data.

Findings

An IFEM method is proposed and a non-adiabatic temperature rise test is simulated to verify the method. The interval results are discussed and compared with monitoring data. The proposed method is found to be feasible and effective.

Originality/value

Compared with the traditional finite element methods, the proposed method taking the uncertainty of various factors into account and it will be helpful for engineers to gain a better understanding of the real condition.

Details

Engineering Computations, vol. 37 no. 7
Type: Research Article
DOI: https://doi.org/10.1108/EC-10-2019-0456
ISSN: 0264-4401

Keywords

  • Temperature field
  • Concrete structure
  • Interval finite element method
  • Pipe cooling

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Article
Publication date: 29 May 2020

ANN-based prediction intervals to forecast labour productivity

Farnad Nasirzadeh, H.M. Dipu Kabir, Mahmood Akbari, Abbas Khosravi, Saeid Nahavandi and David G. Carmichael

This study aims to propose the adoption of artificial neural network (ANN)-based prediction intervals (PIs) to give more reliable prediction of labour productivity using…

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Abstract

Purpose

This study aims to propose the adoption of artificial neural network (ANN)-based prediction intervals (PIs) to give more reliable prediction of labour productivity using historical data.

Design/methodology/approach

Using the proposed PI method, various sources of uncertainty affecting predictions can be accounted for, and a PI is proposed instead of a less reliable single-point estimate. The proposed PI consists of a lower and upper bound in which the realization of the predicted variable, namely, labour productivity, is anticipated to fall with a defined probability and represented in terms of a confidence level (CL).

Findings

The proposed PI method is implemented on a case study project to predict labour productivity. The quality of the generated PIs for the labour productivity is investigated at three confidence levels. The results show that the proposed method can predict the value of labour productivity efficiently.

Practical implications

This study is the first attempt in construction management to undertake a shift from deterministic point predictions to interval forecasts to improve the reliability of predictions. The proposed PI method will help project managers obtain accurate and credible predictions of labour productivity using historical data. With a better understanding of future outcomes, project managers can adopt appropriate improvement strategies to enhance labour productivity before commencing a project.

Originality/value

Point predictions provided by traditional deterministic ANN-based forecasting methodologies may be unreliable due to the different sources of uncertainty affecting predictions. The current study proposes ANN-based PIs as an alternative and robust tool to give a more reliable prediction of labour productivity using historical data. Using the proposed method, various sources of uncertainty affecting the predictions are accounted for, and a PI is proposed instead of a less reliable single point estimate.

Details

Engineering, Construction and Architectural Management, vol. 27 no. 9
Type: Research Article
DOI: https://doi.org/10.1108/ECAM-08-2019-0406
ISSN: 0969-9988

Keywords

  • Labour productivity
  • Prediction interval
  • Uncertainties
  • Neural networks

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Article
Publication date: 19 August 2019

Determine the optimal capital structure of BOT projects using interval numbers with Tianjin Binhai New District Metro Z4 line in China as an example

Yuning Wang and Xiaohua Jin

Various factors may influence project finance when a multi-sourced debt financing strategy is used for financing capital investments, in general, and public infrastructure…

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Abstract

Purpose

Various factors may influence project finance when a multi-sourced debt financing strategy is used for financing capital investments, in general, and public infrastructure investments, in particular. Traditional indicators lack comprehensive consideration of the influences of many internal and external factors, such as investment structure, financing mode and credit guarantee structure, which exist in the financing decision making of BOT projects. An effective approach is, thus, desired. The paper aims to discuss these issues.

Design/methodology/approach

This paper develops a financial model that uses an interval number to represent the uncertain factors and, subsequently, conducts a standardization of the interval number. Decision makers determine the weight of each objective through the analytic hierarchy process. Through the optimization procedure, project investors and sponsors are provided with a strategy regarding the optimal amount of debt to be raised and the insight on the risk level based on the net present value, as well as the probability of bankruptcy for each different period of debt service.

Findings

By using an example infrastructure project in China and based on the comprehensive evaluation, comparison and ranking of the capital structures of urban public infrastructure projects using the interval number method, the final ranking can help investors to choose the optimal capital structure for investment. The calculation using the interval number method shows that X2 is the optimal capital structure plan for the BOT project of the first stage of Tianjin Binhai Rail Transit Z4 line. Therefore, investors should give priority to selecting a capital contribution ratio of 45 per cent for this investment.

Research limitations/implications

In this paper, some parameters, such as depreciation life, construction period and concession period, are assumed to be deterministic parameters, although the interval number model has been introduced to analyze the uncertainty indicators, such as total investment and passenger flow, of BOT rail transport projects. Therefore, more of the above deterministic parameters can be taken as uncertainty parameters in future research so that calculation results fit actual projects more closely.

Originality/value

This model can be used to make the optimal investment decision for a project by determining the impact of uncertainty factors on the profitability of the project in its lifecycle during the project financial feasibility analysis. Project sponsors can determine the optimal capital structure of a project through an analysis of the irregular fluctuation of the unpredictable factors in project construction such as construction investment, operating cost and passenger flow. The model can also be used to examine the effects of different capital investment ratios on indicators so that appropriate measures can be taken to reduce risks and maximize profit.

Details

Engineering, Construction and Architectural Management, vol. 26 no. 7
Type: Research Article
DOI: https://doi.org/10.1108/ECAM-07-2018-0259
ISSN: 0969-9988

Keywords

  • Optimization
  • Risk management
  • Project management
  • Decision support systems
  • Strategic management
  • Novel model

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Article
Publication date: 31 December 2020

Assessment of national innovation capabilities of OECD countries using trapezoidal interval type-2 fuzzy ELECTRE III method

Geetha Selvaraj and Jeonghwan Jeon

For a nation to become a superpower, it's scientific and technological advancement is essential. Each country is exploring how to improve themselves in terms of science…

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Abstract

Purpose

For a nation to become a superpower, it's scientific and technological advancement is essential. Each country is exploring how to improve themselves in terms of science and technology. The authors analyzed the innovation capabilities of 35 OECD countries that have not recently joined Lithuania.

Design/methodology/approach

In recent years, a lot of research work has been done on trapezoidal interval type-2 fuzzy sets (TIT-2 FS), and many research works have been published. The trapezoidal interval type-2 fuzzy set helps effectively to represent the uncertainty comparatively than the type-1 fuzzy set. Taking advantage of this effectiveness, the authors extend the best multi-criteria decision making method (MCDM) for trapezoidal interval type-2 fuzzy sets. Here, ELimination and Choice Expressing REality III (ELECTRE III) method in the trapezoidal interval type-2 fuzzy set environment is proposed.

Findings

This analysis helps to the OECD countries to develop their level of innovation in the criteria. The authors are making this evaluation for the year 2018 based on the 31 criteria. Application of the proposed method expressed by evaluation of the national innovation capability problem. Based on the obtained results, the top five countries are United States, Switzerland, Canada, Germany and Japan.

Originality/value

The authors collected required data from different available data sources like OECD, IMD, USPTO, ITU and surveyed data reported by KISTEP. After collecting all the data from different sources, the authors calculated the standard values as KISTEP. After converting the standard values into trapezoidal interval type-2 fuzzy values, the authors construct a decision matrix based on these values. Then, the authors determined the possibility mean values and preference. Then, they calculated the concordance and discordance credibility degree values. Finally, they ranked OECD countries by the net credibility degree. The results are computed by using the MATLAB software.

Details

Data Technologies and Applications, vol. no.
Type: Research Article
DOI: https://doi.org/10.1108/DTA-07-2020-0154
ISSN: 2514-9288

Keywords

  • Decision support systems
  • ELECTRE III
  • Interval type-2 fuzzy

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Article
Publication date: 8 August 2016

Risk assessment method for aeroengine multiple failure risk using Monte Carlo simulation

Longbiao Li, Suyi Bi and Youchao Sun

– The purpose of this paper is to develop a method to predict the multi-failure risk of aero engine in service and to evaluate the effectiveness of different corrective actions.

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Abstract

Purpose

The purpose of this paper is to develop a method to predict the multi-failure risk of aero engine in service and to evaluate the effectiveness of different corrective actions.

Design/methodology/approach

The classification of failure risk level, the determination of hazard ratio and the calculation of risk factor and the risk per flight have been proposed. The multi-failure risk assessment process of aero engine has been established to predict the occurrence of failure event and assess the failure risk level. According to the history aero engine failure data, the multi-failure risk, i.e., overheat, blade wounding, pump failure, blade crack, pipe crack and combustor crack, has been predicted considering with and without corrective action. Two corrective actions, i.e., reduce the maintenance interval and redesign the failure components, were adopted to analyze the decreasing of risk level.

Findings

The multi-failure risk of aero engine with or without corrective action can be determined using the present method. The risk level of combustor crack decreases from high-risk level of 1.18×1e−9 without corrective action to acceptable risk level of 0.954×1e−9 by decreasing the maintenance interval from 1,000 to 800 h, or to 0.912×1e−9 using the redesign combustor.

Research limitations/implications

It should be noted that probability of detection during maintenance actions has not been considered in the present analysis, which would affect the failure risk level of aero engine in service.

Social implications

The method in the present analysis can be adapted to other types of failure modes which may cause significant safety or environment hazards, and used to determine the maintenance interval or choose appropriate corrective action to reduce the multi-failure risk level of aero engine.

Originality/value

The maintenance interval or appropriate corrective action can be determined using the present method to reduce the multi-failure risk level of aero engine.

Details

Multidiscipline Modeling in Materials and Structures, vol. 12 no. 2
Type: Research Article
DOI: https://doi.org/10.1108/MMMS-06-2015-0028
ISSN: 1573-6105

Keywords

  • Monte Carlo simulation
  • Risk assessment
  • Risk factor
  • Aero engine
  • Multi-failure

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