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Aiming at the problems of poor accuracy and limitation in strength assessment of spot welding vehicle body caused by uncertain factors, such as key component size and nugget diameter, the numerical models of strength uncertainty analysis of spot-welded joints were constructed based on evidence theory and fuzzy theory.

Evidence theory and fuzzy theory are used to deal with the uncertainty of design parameter, and differential evolution algorithms are used to calculate the propagation process of uncertainty in this model. Furthermore, efficient relationship between the strength of welded joints and each design parameter is constructed by using response surface proxy model, which effectively avoids the problem of repeated complex finite element analysis in uncertainty analysis.

The results show that the constructed uncertainty numerical model is effective for the multiple uncertainties and give interval results under different probabilities and affiliations, which can more effectively evaluate the strength of the welded body structure to avoid overly conservative estimates for deterministic design.

The evidence theory is improved and combined with differential evolution algorithm and response surface method to effectively improve the computational efficiency. Based on the improved evidence theory and fuzzy algorithm, the numerical models for the uncertainty analysis of solder joint strength of welded structures are constructed and their feasibility is verified.

The purpose of this paper is to describe the transfer of experiences to improve the basis for overcoming the dilemma of trying to achieve analyses and systems that are both good and simple. The quality of decisions relating to projects depends on how well the assumed basis for the project actually fit the reality of the situation in which the consequences occur. Good value and cost estimations support good decisions about projects insofar as the assumptions on which they are based mirror the reality, and the decision makers can understand the analysis.

The paper uses a longitudinal case study and qualitative analysis. Data relating to a large number of cases have become available to the authors through many years of research and consulting activities. Through joint experience and discussion the patterns are analysed. This paper is descriptive with respect to the challenges and empirical examples. The analysis itself ends with a rather normative conclusion.

There is a dilemma embedded in the processes used to analyse uncertainty and risks associated with projects. On the one hand, an important task is to reduce the complexity of a given situation to render the issues sufficiently simple for them to be understood and assessed. On the other hand, the models and assumptions upon which an analysis is based have to be sufficiently precise and detailed in order to make sense. The same dilemma is found when considering actions to address risks and uncertainties, as well as in designing management systems. It is concluded that the dilemma is real. Solutions have to be found among both good and simple options.

The paper does not answer questions on “how to” and does not dig deep into theoretical perspectives on the current dilemma. More research to understand all aspects of the issue is needed.

Uncertainty analysis and management systems have to be good (precise enough) and at the same time simple (practical). There is no value unless it is used. Practical examples in the paper are intended to help practitioners identify alternative options.

The dilemma of good and simple has not been explicitly addressed before in light of practical experience and theory. The value added is increased awareness of an important problem in analytical processes.

The purpose of this paper is to propose a unified optimization design method and apply it to handle the brake squeal instability involving various uncertainties in a unified framework.

Fuzzy random variables are taken as equivalent variables of conventional uncertain variables, and a unified response analysis method is first derived based on level-cut technique, Taylor expansion and central difference scheme. Next, a unified reliability analysis method is developed by integrating the unified response analysis and fuzzy possibility theory. Finally, based on the unified reliability analysis method, a unified reliability-based optimization model is established, which is capable of optimizing uncertain responses in a unified way for different uncertainty cases.

The proposed method is extended to perform squeal instability analysis and optimization involving various uncertainties. Numerical examples under eight uncertainty cases are provided and the results demonstrate the effectiveness of the proposed method.

Most of the existing methods of uncertainty analysis and optimization are merely effective in tackling one uncertainty case. The proposed method is able to handle the uncertain problems involving various types of uncertainties in a unified way.

The article sets out to discuss and present a solution to the fact that various qualitative risk analyses of the same problem can reach significantly different conclusions.

By reviewing a common risk analysis approach and identifying where the possible problems arise, the authors propose ways to overcome the problems based on what they have found in the literature in general.

There are ways to greatly reduce the problems, but this requires a risk analysis approach in which information quality and consistency are the subject of greater focus.

The definitions used, Monte Carlo methods and the analytical hierarchy process are well tested in countless applications. Hence, the authors believe that this work possesses no major limitations.

The approach has only been applied to theoretical situations; real‐life situations are needed to address possible practical limitations.

The paper illustrates the importance of distinguishing between “uncertainty”, “risk” and “capabilities” and the associated implications. It also shows how this can be done in a logically consistent way using the analytical hierarchy process so that the problem of inconsistency is reduced, and how the analysis can be used to systematically improve itself. The proposed risk analysis is a novel approach that has, to the authors' knowledge, never been thought of before.

The purpose of this paper is to present a fault tree (FT)-based approach for quantitative risk analysis in the construction industry that can take into account both objective and subjective uncertainties.

In this research, the identified basic events (BEs) are first categorized based on the availability of historical data into probabilistic and possibilistic. The probabilistic and possibilistic events are represented by probability distributions and fuzzy numbers, respectively. Hybrid uncertainty analysis is then performed through a combination of Monte Carlo simulation and fuzzy set theory. The probability of occurrence of the top event is finally calculated using the proposed FT-based hybrid uncertainty analysis method.

The efficiency of the proposed method is demonstrated by implementing in a real steel structure project. A quantitative risk assessment is performed for weld cracks, taking into account of both types of uncertainties. An importance analysis is finally performed to evaluate the contribution of each BE to the probability of occurrence of weld cracks and adopt appropriate response strategies.

In this research, the impact of objective (aleatory) dependence between the occurrences of different BEs and subjective (epistemic) dependence between estimates of the epistemically uncertain probabilities of some BEs are not considered. Moreover, there exist limitations to the application of fuzzy set rules, which were used for aggregating experts’ opinions and ranking purposes of the BEs in the FT model. These limitations can be investigated through further research.

It is believed that the proposed hybrid uncertainty analysis method presents a robust and powerful tool for quantitative risk analysis, as both types of uncertainties are taken into account appropriately.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

Real estate development appraisal is a quantification of future expectations. The appraisal model relies upon the valuer/developer having an understanding of the future in terms of the future marketability of the completed development and the future cost of development. In some cases the developer has some degree of control over the possible variation in the variables. However, other variables are totally dependent upon the vagaries of the market at the completion date. To try to address the risk of a different outcome to the one expected the developer will often carry out a sensitivity analysis on the development. This paper aims to look at the processes for so doing and the role of the decision maker in the analysis.

Traditional sensitivity analysis has generally only looked at the best and worst scenarios and has focused on the anticipated or expected outcomes. This does not take into account uncertainty and the range of outcomes that can happen. A fuller analysis should include examination of the uncertainties in each of the components of the appraisal and account for the appropriate distributions of the variables. This requires a standardised approach and the use of a generic forecasting software package.

There are numerous risks involved in the development of real estate. By allowing the decision maker to contribute to the assessment of these risks, the analysis provides the decision maker with a greater understanding of the critical variables and their impact upon the viability of the final scheme.

This analysis shows that developers can get a better understanding of the upside and downside risks associated with their project.

Considering that uncertain factors widely exist in engineering practice, an adaptive collocation method (ACM) is developed for the structural fuzzy uncertainty analysis.

ACM arranges points in the axis of the membership adaptively. Through the adaptive collocation procedure, ACM can arrange more points in the axis of the membership where the membership function changes sharply and fewer points in the axis of the membership where the membership function changes slowly. At each point arranged in the axis of the membership, the level-cut strategy is used to obtain the cut-level interval of the uncertain variables; besides, the vertex method and the Chebyshev interval uncertainty analysis method are used to conduct the cut-level interval uncertainty analysis.

The proposed ACM has a high accuracy without too much additional computational efforts.

A novel ACM is developed for the structural fuzzy uncertainty analysis.

The purpose of this paper is to explore new ways and lay a solid foundation to solve the problem of reliability growth analysis of major aerospace equipment with various uncertainty data through propose new concepts of general uncertainty data (GUD) and general uncertainty variable (GUV) and build the operation system of GUVs.

The characteristics of reliability growth data of major aerospace equipment and the limitations of current reliability growth models have been analyzed at first. The most commonly used uncertainty system analysis methods of probability statistics, fuzzy mathematics, grey system theory and rough set theory have been introduced. The concepts of GUD and GUV for reliability growth data analysis of major aerospace equipment are proposed. The simplified form of GUV based on the “kernel” and the degree of uncertainty of GUV is defined. Then an operation system of GUVs is built.

(1) The concept of GUD; (2) the concept of GUV; (3) The novel operation rules of GUVs with simplified form.

The method exposed in this paper can be used to integrate complex reliability growth data of major aerospace equipment. The reliability growth models based on GUV can be built for reliability growth evaluation and forecasting of major aerospace equipment in practice. The reliability evaluation example of a solid rocket motor shows that the concept and idea proposed in this paper are feasible. The research of this paper opens up a new way for the analysis of complex uncertainty data of reliability growth of major aerospace equipment. Moreover, the operation of GUVs could be extended to the case of algebraic equation, differential equation and matrix which including GUVs.

The new concepts of GUD and GUV are given for the first time. The novel operation rules of GUVs with simplified form were constructed.

This work develops a longitudinal analysis of perceived environmental uncertainty applying the Rasch methodology (1960). The environmental uncertainty is defined as an individual's perceived inability to predict the environment accurately (Milliken, 1987). The study focuses on analysing the state uncertainty from the perspective of the information and under the cognitive approach to the business reality.

Rasch measurement theory (1960) is applied, specifically the differential item functioning analysis based on the responses to a survey of SMEs.

The main sources of uncertainty for all the SMEs in the sample are two sectors in their general environment: economic and political-legal ones. These segments are the only ones in the environment that generate uncertainty that in 2016 is significantly different from that in 2019, being lower in the latter year.

This is a pioneering analysis of uncertainty both for its longitudinal nature and the methodology applied.