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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.

The smart open house provides optimal adaptability using sensing, operating, information, and communications technology, in conjunction with open building in-filled components, to perceive user needs and environmental changes, and thereby meet the needs for sustainability and a healthy living environment. These needs are particularly pressing in view of the aged society that will emerge in Taiwan after 2020. Based on the smart open house hypothesis, this study proposes using agent-based smart skins in a smart open house, where an agent-based smart skin is embedded in a lifetime home (or ageless home) with an open system construction. The agent-based smart skin operating mechanism employs fuzzy logic inference and neuro-fuzzy learning to process environmental information from sensing devices and drive skin elements, achieving adaptive action, meeting residents' lifetime use needs, and offering a user experience-oriented smart care capability.

The purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.

This paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).

By applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highest R² value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highest R² with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.

As land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.

In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.

Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC.

The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios.

To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method.

This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method.

This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.

On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator.

On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty.

The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

The purpose of this paper is to provide a framework for measuring the imprecise and subjective “effectiveness” of a major maintenance activity. Such a measure will not only bring objectivity in gauging the effectiveness of maintenance task carried out by the workforce without any intervention from an expert but also help in measuring the slow degradation of the performance of the concerned major equipment/system.

The paper follows a three-step approach. First, identify a set of parameters considered important for estimating the maintenance activity effectiveness. Second, generate a set of data using expert opinions on a fuzzy performance measure of maintenance activity effectiveness (output). Also, find an aggregated estimate of the effectiveness by analysing the consensus among experts. This requires using a part of the “fuzzy multiple attribute decision making” process. Finally, train a neuro-fuzzy inference system based on input parameters and generated output data.

The paper analysed major maintenance activity carried out on diesel engines of a power plant company. Expert opinions were used in selection of key parameters and generation of output (effectiveness measure). The result of a trained adaptive neuro-fuzzy inference system (ANFIS) matched acceptably well with that aggregated through the expert opinions.

In view of unavailability of data, the method relies on training a neuro-fuzzy system on data generated through expert opinion. The data as such are vague and imprecise leading to lack of consensus between experts. This can lead to some amount of error in the output generated through ANFIS.

The originality of the paper lies in presentation of a method to estimate the effectiveness of a maintenance activity.

The purpose of this paper is to develop an adaptive fuzzy controller for STATCOM to damp low-frequency inter-area oscillation over wide operating range using wide area signals in multimachine power system.

In this paper tuneable fuzzy model is proposed where the parameters of the fuzzy inference system are tuned by using the adaptive characteristic of the artificial neural network. Based on back propagation algorithm and method of least square estimation, the fuzzy inference rule base is tweaked according to the data from which they are modelled. The wide area control signals, for the proposed controller, available in the power system are selected on the basis of eigenvalue sensitivity defined in terms of participation factor.

The effectiveness of the proposed controller with wide area signals is tested on two test cases, namely, two area network and IEEE 12 bus benchmark system. The comparative analysis of the proposed adaptive fuzzy controller is carried out with conventional STATCOM controller along with fuzzy-and neural-based supplementary controller all using selected wide area signals. The results show that neural network tuned fuzzy controller leads to better system identification and have enhanced damping characteristics over wide operating range.

In the available literature, numerous researchers have indicated the use of fuzzy logic controller and neural controller along with their hybrid schemes as STATCOM controller for improving the dynamics of the multimachine power system using local signals. The main contribution of the paper is in using the hybrid intelligent control scheme for STATCOM using wide area signals. The advantage of proposed scheme is that the performance of well-designed fuzzy system can be enhanced with the same training data that are used for designing a neural controller thus giving enhanced performance in comparison to individual intelligent control scheme.