Search results

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.

How does environmental uncertainty affect the process of starting new hybrid organizations? Our comparative analysis of the formation of two “green” banks – with hybrid goals linked to banking and environmental logics – reveals that shifts in their strategic orientations resulted from attempts to align uncertain and changing resource environments with the composition and goals of the organizations’ top leadership. While the initial idea and goals of the founders were similar, the organizations they established ended up with divergent strategic orientations and senior leadership groups.

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.

Most projects are facing delays, and accelerating the pace of project progress is a necessity. Project managers are responsible for completing the project on time with minimum cost and with maximum quality. This study provides a trade-off between time, cost, and quality objectives to optimize project scheduling.

The current paper presents a new resource-constrained multi-mode time–cost–quality trade-off project scheduling model with lags under finish-to-start relations. To be more realistic, crashing and overlapping techniques are utilized. To handle uncertainty, which is a source of project complexity, interval-valued fuzzy sets are adopted on several parameters. In addition, a new hybrid solution approach is developed to cope with interval-valued fuzzy mathematical model that is based on different alpha-levels and compensatory methods. To find the compatible solution among conflicting objectives, an arithmetical average method is provided as a compensatory approach.

The interval-valued fuzzy sets approach proposed in this paper is denoted to be scalable, efficient, generalizable and practical in project environments. The results demonstrated that the crashing and overlapping techniques improve time–cost–quality trade-off project scheduling model. Also, interval-valued fuzzy sets can properly manage expressions of the uncertainty of projects which are realistic and practical. The proposed mathematical model is validated by solving a medium-sized dataset an adopted case study. In addition, with a sensitivity analysis approach, the solutions are compared and the model performance is confirmed.

This paper introduces a new continuous-based, resource-constrained, and multi-mode model with crashing and overlapping techniques simultaneously. In addition, a new hybrid compensatory solution approach is extended based on different alpha-levels to handle interval-valued fuzzy multi-objective mathematical model of project scheduling with influential uncertain parameters.

This paper aims to propose a novel statistic energy analysis method with fuzzy parameters to study the dynamic and acoustic responses of coupled system with fuzzy parameters, which can expand the applied range of statistic energy analysis method in engineering to some extent.

On the basis of the property of membership level, the uncertain fuzzy parameters are expressed as the interval forms. Interval mathematics and interval expansion principle are adopted to solve the problem with interval parameters. At last, two numerical examples, which include a two-plate coupled system and a single-partition sound-insulation system, are carried out to demonstrate the feasibility and validity of the presented method.

Interval mathematics and interval expansion principle are adopted to solve the problem with interval parameters.

By integrating the interval analysis, optimization technique and Taylor expansion method, two non-probabilistic, set-theoretical statistical energy analyses are proposed for predicting the dynamical and acoustical response of the complex coupled system with uncertain parameters in high-frequency domain.

Selecting the optimal supplier is a challenging managerial decision that involves several dimensions that vary over time. Despite the considerable attention devoted to this issue, knowledge is required to be updated and analyzed in this field. This paper reveals new opportunities to advance supplier selection (SS) research from a multidimensional perspective. Moreover, this study aims to formalise SS knowledge to enable the appropriate selection of sustainable, resilient and circular criteria.

This study is developed in two stages: first, a systematic literature review is conducted to select relevant papers. Descriptive and thematic analyses are employed to analyze criteria, solving approaches and case studies. Second, a criterion knowledge-based framework is developed and validated by experts to be implemented as ontology using Protégé software.

Evaluating the viability of suppliers need further studies to integrate other criteria and to align SS objectives with research advancement. Artificial intelligence tools are needed to revolutionize and optimize the traditional techniques used to solve this problem. Literature lucks frameworks for specific sectors. The proposed ontology provides a consistent criteria knowledge base.

For academics, the results of this study highlight opportunities to improve the viable SS process. From a managerial perspective, the proposed ontology can assist managers in selecting the appropriate criteria. Future works can enrich the proposed ontology and integrate this knowledge base into an information system.

This study contributes to promoting knowledge about viable SS. Capitalizing the knowledge base of criteria in a computer-interpretable manner supports the digitalization of this critical decision.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

This paper aims to develop a method for evaluating the failure probability and global sensitivity of multiple failure modes based on convex-probability hybrid uncertainty.

The uncertainty information of the input variable is considered as convex-probability hybrid uncertainty. Moment-independent variable global sensitivity index based on the system failure probability is proposed to quantify the effect of the input variable on the system failure probability. Two-mode sensitivity indices are adopted to characterize the effect of each failure mode on the system failure probability. The method based on active learning Kriging (ALK) model with a truncated candidate regions (TCR) is adopted to evaluate the systems failure probability, as well as sensitivity index and this method is termed as ALK-TCR.

The results of five examples demonstrate the effectiveness of the sensitivity index and the efficiency of the ALK-TCR method in solving the problem of multiple failure modes based on the convex-probability hybrid uncertainty.

Convex-probability hybrid uncertainty is considered on system reliability analysis. Moment-independent variable sensitivity index based on the system failure probability is proposed. Mode sensitivity indices are extended to hybrid uncertain reliability model. An effective global sensitivity analysis approach is developed for the multiple failure modes based on convex-probability hybrid uncertainty.

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.

Given the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem in order to distribute first aid relief items in the post disaster phase, where routes are subject to disruption.

To cope with such kind of uncertainty, the demand rate of relief items is considered as a random fuzzy variable and a robust scenario-based possibilistic-stochastic programming model is elaborated. The results are presented and reported on a real case study of earthquake, along with sensitivity analysis through some important parameters.

The results show that the demand satisfaction level in the proposed model is significantly higher than the traditional scenario-based stochastic programming model.

In reality, in the occurrence of a disaster, demand rate has a mixture nature of objective and subjective and should be represented through possibility and probability theories simultaneously. But so far, in studies related to this domain, demand parameter is not considered in hybrid uncertainty. The worth of considering hybrid uncertainty in this study is clarified by supplementing the contribution with presenting a robust possibilistic programming approach and disruption assumption on roads.