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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.

Load forecasting is important to any electrical grid, but for the developing and third-world countries with power shortages, load forecasting is essential. When planed load shedding programs are implemented to face power shortage, a noticeable distortion to the load curves will happen, and this will make the load forecasting more difficult.

In this paper, a new load forecasting model is developed that can detect the effect of planned load shedding on the power consumption and estimate the load curve behavior without the shedding and with different shedding programs. A neuro-Fuzzy technique is used for the model, which is trained and tested with real data taken from one of the 11 KV feeders in Najaf city in Iraq to forecast the load for two days ahead for the four seasons. Load, temperature, time of the day and load shedding schedule for one month before are the input parameters for the training, and the load forecasting data for two days are estimated by the model.

To verify the model, the load is forecasted without shedding by the proposed model and compared to real data without shedding and the difference is acceptable.

The proposed model provides acceptable forecasting with the load shedding effect available and better than other models. The proposed model provides expected behavior of load with different shedding programs an issue helps to select the appropriate shedding program. The proposed model is useful to estimate the real demands by assuming load shedding hours to be zero and forecast the load. This is important in places suffer from grid problems and cannot supply full loads to calculate the peak demands as the case in Iraq.

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.

Tactile sensing is the process of determining physical properties and events through contact with objects in the world. The purpose of this paper is to establish a novel design of an adaptive neuro-fuzzy inference system (ANFIS) for estimation of contact position of a new tactile sensing structure.

The major task is to investigate implementations of carbon-black-filled silicone rubber for tactile sensation; the silicone rubber is electrically conductive and its resistance changes by loading or unloading strains.

The sensor-elements for the tactile sensing structure were made by press-curing from carbon-black-filled silicone rubber. The experimental results can be used as training and checking data for the ANFIS network.

This system is capable to find any change of contact positions and thus indicates state of the current contact location of the tactile sensing structure. The behavior of the use silicone rubber shows strong non-linearity, therefore, the sensor cannot be used for high accurate measurements. The greatest advantage of this sensing material lies in its high elasticity.

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.

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 address the use of passive RFID technology for the development of an autonomous surveillance robot. Passive RFID tags can be used for labelling both valued objects and goal‐positions that the robot has to reach in order to inspect the surroundings. In addition, the robot can use RFID tags for navigational purposes, such as to keep track of its pose in the environment. Automatic tag position estimation is, therefore, a fundamental task in this context.

The paper proposes a supervised fuzzy inference system to learn the RFID sensor model; Then the obtained model is used by the tag localization algorithm. Each tag position is estimated as the most likely among a set of candidate locations.

The paper proves the feasibility of RFID technology in a mobile robotics context. The development of a RFID sensor model is first required in order to provide a functional relationship between the spatial attitude of the device and its responses. Then, the RFID device provided with this model can be successfully integrated in mobile robotics applications such as navigation, mapping and surveillance, just to mention a few.

The paper presents a novel approach to RFID sensor modelling using adaptive neuro‐fuzzy inference. The model uses both Received Signal Strength Indication (RSSI) and tag detection event in order to achieve better accuracy. In addition, a method for global tag localization is proposed. Experimental results prove the robustness and reliability of the proposed approach.

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.