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Book part
Publication date: 5 October 2018

Nima Gerami Seresht, Rodolfo Lourenzutti, Ahmad Salah and Aminah Robinson Fayek

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes…

Abstract

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.

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Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

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Book part
Publication date: 5 October 2018

Abstract

Details

Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

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Book part
Publication date: 5 October 2018

Abstract

Details

Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

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Article
Publication date: 24 September 2019

Farman Afzal, Shao Yunfei, Mubasher Nazir and Saad Mahmood Bhatti

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper…

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Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

International Journal of Managing Projects in Business, vol. 14 no. 2
Type: Research Article
ISSN: 1753-8378

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Article
Publication date: 11 February 2019

Sanjay Kumar Behera, Dayal R. Parhi and Harish C. Das

With the development of research toward damage detection in structural elements, the use of artificial intelligent methods for crack detection plays a vital role in…

Abstract

Purpose

With the development of research toward damage detection in structural elements, the use of artificial intelligent methods for crack detection plays a vital role in solving the crack-related problems. The purpose of this paper is to establish a methodology that can detect and analyze crack development in a beam structure subjected to transverse free vibration.

Design/methodology/approach

Hybrid intelligent systems have acquired their own distinction as a potential problem-solving methodology adopted by researchers and scientists. It can be applied in many areas like science, technology, business and commerce. There have been the efforts by researchers in the recent past to combine the individual artificial intelligent techniques in parallel to generate optimal solutions for the problems. So it is an innovative effort to develop a strong computationally intelligent hybrid system based on different combinations of available artificial intelligence (AI) techniques.

Findings

In the present research, an integration of different AI techniques has been tested for accuracy. Theoretical, numerical and experimental investigations have been carried out using a fix-hinge aluminum beam of specified dimension in the presence and absence of cracks. The paper also gives an insight into the comparison of relative crack locations and crack depths obtained from numerical and experimental results with that of the results of the hybrid intelligent model and found to be in good agreement.

Originality/value

The paper covers the work to verify the accuracy of hybrid controllers in a fix-hinge beam which is very rare to find in the available literature. To overcome the limitations of standalone AI techniques, a hybrid methodology has been adopted. The output results for crack location and crack depth have been compared with experimental results, and the deviation of results is found to be within the satisfactory limit.

Details

International Journal of Structural Integrity, vol. 10 no. 2
Type: Research Article
ISSN: 1757-9864

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Book part
Publication date: 5 October 2018

Aminah Robinson Fayek and Rodolfo Lourenzutti

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of…

Abstract

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

Details

Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

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Book part
Publication date: 5 October 2018

Olubukola Tokede, Adam Ayinla and Sam Wamuziri

The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the…

Abstract

The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the different concept selection methods that were considered for drilling operations at the Trym field in Norway. The construction of drilling rigs is a capital-intensive process, and it involves high levels of economic risk. These risks can be broadly categorised as aleatoric (i.e. those related to chance) and epistemic (i.e. those related to knowledge). Evaluating risks in the investment appraisal process tends to be a complicated process. Project risks are evaluated using Monte Carlo simulation (MCS) and are based on the fuzzy analytic hierarchy process (AHP). MCS provides a useful means of evaluating variabilities (i.e. aleatoric risks) in oil drilling operations. However, many of the economic risks in oil drilling processes are unanticipated, and, in some cases, are not readily expressible in quantitative values. The fuzzy AHP is therefore used to appraise the qualitatively defined indirect revenues comprising risks that affect future flexibilities, schedule certainty and health and safety performance. Both the Monte Carlo technique and the fuzzy AHP technique found that a cumulative revenue variation of up to 30% is possible in any of the considered drilling options. The fuzzy AHP technique estimates that the chances of profitability being less than NOK 1 billion over a five-year period is 0.5%, while the Monte Carlo technique estimates suggest a more conservative proportion of 10%. Overall, the fuzzy AHP technique is easy to use and flexible, and it demonstrates increased robustness and improved predictability.

Details

Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

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Article
Publication date: 21 May 2021

Mohammad Raoufi and Aminah Robinson Fayek

This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of…

Abstract

Purpose

This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of construction crew performance.

Design/methodology/approach

The developed methodology uses fuzzy logic, Monte Carlo simulation and agent-based modeling to simulate the behavior of construction crews and predict their performance. Both random and subjective uncertainties are considered in model variables.

Findings

The developed methodology was implemented on a real case involving the parametric study of construction crew performance to assess its applicability and suitability for this context.

Research limitations/implications

This parametric study demonstrates a practical application for the hybrid FMCABS methodology. Though findings from this study are limited to the context of construction crew motivation and performance, the applicability of the developed methodology extends beyond the construction domain.

Practical implications

This paper will help construction practitioners to predict and improve crew performance by taking into account both random and subjective uncertainties.

Social implications

This paper will advance construction modeling by allowing for the assessment of social interactions among crews and their effects on crew performance.

Originality/value

The developed hybrid FMCABS methodology represents an original contribution, as it allows agent-based models to simultaneously process all types of variables (i.e. deterministic, random and subjective) in the same simulation experiment while accounting for interactions among different agents. In addition, the developed methodology is implemented in a novel and extensive parametric study of construction crew performance.

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Article
Publication date: 6 March 2019

Saleeshya P.G. and Binu M.

Lean implementation is a strategic decision. The capacity of organisation to be “Lean” can be identified before lean implementation by assessing leanness of an…

Abstract

Purpose

Lean implementation is a strategic decision. The capacity of organisation to be “Lean” can be identified before lean implementation by assessing leanness of an organisation. This study aims to attempt developing a holistic leanness assessment tool for assessing organisational leanness.

Design/methodology/approach

A neuro-fuzzy leanness assessment model for assessing the leanness of a manufacturing system is presented. The model is validated academically and industrially by conducting a case study.

Findings

Neuro-fuzzy hybridisation helped assess the leanness accurately. Fuzzy logic helped to perform the leanness assessment more realistically by accounting ambiguity and vagueness in organisational functioning and decision-making processes. Neural network increased the learning capacity of assessment model and increased the accuracy of leanness index.

Research limitations/implications

The industrial case study in the paper shows the results in telecom equipment manufacturing industry. This may not represent entire manufacturing sector. The generic nature of the model developed in this research ensures its wide applicability.

Practical implications

The neuro-fuzzy hybrid model for assessing leanness helps to identify the potential of an organisation to become “Lean”. The organisational leanness index developed by the study helps to monitor the effectiveness and impact of lean implementation programmes.

Originality/value

The leanness assessment models available in literature lack depth and coverage of leanness parameters. The model developed in this research assesses leanness of an organisation by accounting for leanness aspects of inventory management, industrial scheduling, organisational flexibility, ergonomics, product, process, management, workforce, supplier relationship and customer relationship with the help of neuro-fuzzy hybrid modelling.

Details

International Journal of Lean Six Sigma, vol. 10 no. 1
Type: Research Article
ISSN: 2040-4166

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Article
Publication date: 24 August 2010

Tushar Jain, Srinivasan Alavandar, Singh Vivekkumar Radhamohan and M.J. Nigam

The purpose of this paper is to propose a novel algorithm which hybridizes the best features of three basic algorithms, i.e. genetic algorithm, bacterial foraging, and…

Abstract

Purpose

The purpose of this paper is to propose a novel algorithm which hybridizes the best features of three basic algorithms, i.e. genetic algorithm, bacterial foraging, and particle swarm optimization (PSO) as genetically bacterial swarm optimization (GBSO). The implementation of GBSO is illustrated by designing the fuzzy pre‐compensated PD (FPPD) control for two‐link rigid‐flexible manipulator.

Design/methodology/approach

The hybridization is carried out in two phases; first, the diversity in searching the optimal solution is increased using selection, crossover, and mutation operators. Second, the search direction vector is optimized using PSO to enhance the convergence rate of the fitness function in achieving the optimality. The FPPD controller design objective was to tune the PD controller constants, normalization, and denormalization factors for both the joints so that integral square error, overshoots, and undershoots are minimized.

Findings

The proposed algorithm is tested on a set of mathematical functions which are then compared with the basic algorithms. The results showed that the GBSO had a convergence rate better than the other algorithms, reaching to the optimal solution. Also, an approach of using fuzzy pre‐compensator in reducing the overshoots and undershoots for loading‐unloading and circular trajectories had been successfully achieved over simple PD controller. The results presented emphasize that a satisfactory tracking precision could be achieved using hybrid FPPD controller with GBSO.

Originality/value

Simulation results were reported and the proposed algorithm indeed has established superiority over the basic algorithms with respect to set of functions considered and it can easily be extended for other global optimization problems. The proposed FPPD controller tuning approach is interesting for the design of controllers for inherently unstable high‐order systems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 3 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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