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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 study aims to investigate the dynamic influences of clustered barriers that hinder electronic procurement technology (EPT) implementation in construction procurement, using the neuro-fuzzy system.

A comprehensive literature review was conducted and 21 barriers to EPT implementation within construction projects were identified. Based on an expert survey, 121 datasets were gathered for this study. Using mean and normalization analysis for the datasets, 15 out of the 21 barriers were deemed to have critical influences in EPT barriers phenomenon. Subsequently, the critical barriers were classified into five groups: human-related; technological risk-related; government-related; industry growth-related; and financial-related. The relationships and influence patterns between the groups of barriers to EPT implementation were analyzed using the neuro-fuzzy system. Furthermore, sensitivity analysis was performed to examine the dynamic influence levels of the barriers within the hindrance level composition.

The results reveal that addressing one barrier group does not reduce the high levels of hindrances experienced in EPT implementation. However, addressing at least two barrier groups mostly tends to reduce the hindrance levels for EPT implementation. Further, this study revealed that addressing some barrier group pairings, such as technological risk-related and government-related barriers, while other barrier groups remained at a high level, still resulted in high levels of hindrances to EPT implementation in construction procurement.

This study provides insights for researchers to help them contribute to the development of theory with contemporary approaches based on the influence patterns of barrier interrelationships.

This study provides a model that would help practitioners and decision makers in construction procurement to understand and effectively determine the complex and dynamic influences of barrier groups to EPT uptake, for the development of suitable mitigation strategies.

This study provides novel insights into the complex influence patterns among grouped barriers concerning EPT adoption in the construction industry. Researchers and practitioners are equipped with knowledge on the influence patterns of barriers. This knowledge aids the development of effective strategies that mitigate the combined groups of barriers, and promote the wider implementation of EPT in the construction industry.

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.

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.

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.

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.

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.

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.

The growing dependence of modern society on telecommunication and information networks and e‐type systems has become inevitable. However, those types of systems are vulnerable to malicious attacks. The speed and automation in network attack techniques continue to increase. An achievable automated attack or unauthorised access to a particular organization network could lead to devastating effects on its reputation and imminent loss of life. In this paper an innovative way is proposed to detect network attacks of a distributed nature such as denial of service (DoS) attacks. The proposed scheme is mainly based on neuro‐fuzzy intelligence in order to learn and determine the fuzzy parameter functions that represent network traffic behaviour. Neuro‐fuzzy agents combine the features of fuzzy logic and neural networks and they have been proposed to overcome the limitations of human expertise in defining fuzzy membership functions, especially for complex environments, such as information networks.

According to UN estimates more than 2,000 people are killed or maimed every month by land‐mines. Although some mechanical solutions to their removal have been proposed, this is still heavily dependent on human manipulation. This study seeks to posit a robotic solution to this extremely hazardous operation.

Examines an active tele‐operated master‐slave robot hand system in which the master and slave hands have completely different structures.

A secure grasping strategy with a neuro‐fuzzy position control is optional, involving robust position control and accurate force control.

To the best of the authors' knowledge, the configuration and control system of the tele‐operation master‐slave robotic hand is novel in the applied robotics research field.

This study aims to short-therm forecasting of power generation output for this purpose, an adaptive neuro-fuzzy inference system (ANFIS) is designed to forecast the output power of power plant based on climate factors considering wind speed and wind direction simultaneously.

Several methods and algorithms have been proposed for systems forecasting in various fields. One of the strongest methods for modeling complex systems is neuro-fuzzy that refers to combinations of artificial neural network and fuzzy logic. When the system becomes more complex, the conventional algorithms may fail for network training. In this paper, an integrated approach, including ANFIS and metaheuristic algorithms, is used for increasing forecast accuracy.

Power generation in power plants is dependent on various factors, especially climate factors. Operating power plant in Iran is very much influenced because of climate variation, including from tropical to subpolar, and severely varying temperature, humidity and air pressure for each region and each season. On the other hands, when wind speed and wind direction are used simultaneously, the training process does not converge, and the forecasting process is unreliable. The real case study is mentioned to show the ability of the proposed approach to remove the limitations.

First, ANFIS is applied for forecasting based on climate factors, including wind speed and wind direction, that have rarely been used simultaneously in previous studies. Second, the well-known and more widely used metaheuristic algorithms are applied to improve the learning process for forecasting output power and compare the results.

The accurate assessment of tool life of any given tool is a great significance in any manufacturing industry. The purpose of this paper is to predict the life of a cutting tool, in order to help decision making of the next scheduled replacement of tool and improve productivity.

This paper reports the use of two soft computing techniques, namely, neuro-fuzzy logic and support vector regression (SVR) techniques for the assessment of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained.

The analysis is carried out using the two soft computing techniques. Tool life values are predicted using aforesaid techniques and these values are compared.

The proposed approaches are relatively simple and can be implemented easily by using software like MATLAB and Weka.

The proposed methodology compares neuro – fuzzy logic and SVR techniques.

The purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system (EHSS). Specifically, the adaptive neuro-fuzzy inference system (ANFIS) is selected to improve the control performance for EHSS.

Two types of input–output data were chosen to train the ANFIS models. The inputs are the desired and actual forces, and the output is the current. The first type is to set a sinusoidal signal for the current to produce the actual driving force, and the desired force is chosen as same as the actual force. The other type is to give a sinusoidal signal for the desired force. Under the action of the PI controller, the actual force tracks the desired force, and the current is the output of the PI controller.

The models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller. The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot, rise time and mean absolute error and show adaptivity to different tracking conditions, including sinusoidal signal tracking and sudden change signal tracking.

This paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.

Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists between the construction industry and economic growth. The consequences of these variations include cost overruns and schedule delays, among others. An accurate forecast of the tender price index is good for controlling the uncertainty associated with its variation. In the present study, the efficacy of using an adaptive neuro-fuzzy inference system (ANFIS) for tender price forecasting is investigated. In addition, the Box–Jenkins model, which is considered a benchmark technique, was used to evaluate the performance of the ANFIS model. The results demonstrate that the ANFIS model is superior to the Box–Jenkins model in terms of the accuracy and reliability of the forecast. The ANFIS could provide an accurate and reliable forecast of the tender price index in the medium term (i.e. over a three-year period). This chapter provides evidence of the advantages of applying nonlinear modelling techniques (such as the ANFIS) to tender price index forecasting. Although the proposed ANFIS model is applied to the tender price index in this study, it can also be applied to a wider range of problems in the field of construction engineering and management.