During implementation of construction projects, durations of activities are affected by various factors. Because of this, both during the planning phase of the project as well as the construction phase, managers try to estimate, or predict, the length of any delays that may occur. Such estimates allow for the ability to take appropriate action in terms of planning and management during the execution of construction works. This paper presents the use of the non-deterministic concept for describing the uncertainty of estimating works duration. The concept uses the theory of fuzzy sets. The author describes a method for fuzzy estimations of construction works duration based on the fact that uncertain data is an inherent factor in the conditions of construction projects. An example application of the method is presented. The author shows a fuzzy estimation for the duration of an activity, taking into consideration the distorting influence caused by malfunctioning construction equipment and delivery delays of construction materials.
The basic element of a project organizing construction works is a schedule. The preparation of the data necessary to specify the timings of the construction completion as indicated in the schedule involves information that is uncertain and hard to quantify. The article presents the methods of building a schedule which includes a fuzzy amount of labour, time standards and number of workers. The proposed procedure allows determining the real deadline for project completion, taking into account variable factors affecting the duration of the individual works.
The basis for a mineral deposit delimitation is a qualitative and quantitative assessment of deposit parameters, qualifying a deposit as an economically valuable object. A conventional approach to the mineral deposit recognition and a deposit detailed parameters qualification in the initial stages of development work in the KGHM were presented in the paper. The goals of such recognition were defined, which through a gradual detailed expansion, resulting from the information inflow, allows for the construction of a more complete decision-making model. The description of the deposit parameters proposed in the article in the context of fuzzy logic, enables a presentation of imprecise statements and data, which may be a complement to a traditional description. Selected non-adjustable and adjustable s-norm and t-norm operators were demonstrated. Operators effects were tested for selected ore quality parameters (copper content and deposit thickness) by constructing adequate membership functions. In a practical application, the use of chosen fuzzy logic operators is proposed for the assessment of the qualitative parameters of copper-silver ore in the exploitation blocks for one of the mines belonging to KGHM Polish Copper S.A. The considerations have been extended by including the possibility of using compensation operators.
The operational mineral deposit reconnaissance tends to evaluate its parameters to conduct safe and profitable production. Particular deposit parameters, important from the point of mineral deposit management, are estimated on the basis of observations carried out by mining geological surveys. These observations usually involve sampling, drilling, laboratory analyses and others. The use of fuzzy description to assess the parameters of the mineral deposit was proposed in the paper. In the fuzzy characteristics, an imprecise descriptive description appeared in place of a particular numerical quantity. This approach was used to description of the ore deposit features (metal content, volume, and metal yield) by assigning them specific characteristic functions, whose distributions were based on basic statistical quantities. Characteristic functions can be used to prepare operational strategies for any configuration of required deposit parameters resulting from the production management needs. For this purpose, selected logical operators of fuzzy sets were used. In the next approach to fuzzy modeling, an opportunity to characterize the deposit in a subjective approach was indicated, where the assessment of the deposit parameters is based on rough, in some way, discretionary observation and evaluation. Such model construction enabled the overall assessment of the deposit from the point of view of any parameters. Through the implementation of appropriate inference rules, adequate fuzzy control planes were obtained, which may also be useful in the context of operational mine strategy planning.
The paper shows methods of analysis and assessment of partnering relations of construction enterprises with the use of questionnaires, statistics, and fuzzy logic. The results were obtained from Polish, Slovak and Ukrainian enterprises. The definition of partnering in the construction industry indicates that it is a qualitative concept. By applying a scale in the questionnaire, and due to mathematical analysis of the data, the final research result, showing the level of partnering relations of construction enterprises, is rendered quantitatively.
The protection of Polish architectural heritage in the former eastern borderlands, accomplished through the conservation and technical securing of historical structures, constitutes one of the main programmes that are implemented by the Ministry of Culture and National Heritage. Currently, many Polish historical buildings in the former eastern borderlands are in a very bad technical condition. The load-bearing systems of these elements, as well as elements of their finish, require immediate emergency securing work. The basic steps that precede conservation work are emergency structural works, which guarantee the durability and stability of the entire historical substance. The specifics and complexity of the problem of the failure of historical buildings often demands an in-depth analysis of a series of factors that are difficult to measure and which are responsible for the cause and effect relationship during the early stage of the technical evaluation of a structure. The analyses of failures of numerous historical structures, for instance that were carried out by the authors, have become the inspiration for the search for effective methods of analysis that would allow for an in-depth analysis of the causes and effects of the failures in question. The DEMATEL method (Decision Making Trial and Evaluation Laboratory) that has been presented in this work, and its fuzzy extension, has lately become one of the more popular methods used in the cause-and-effect analysis of various phenomena. The authors demonstrated how this method works on the example of the evaluation and securing of the load-bearing system of the XVII Collegiate church of the Holy Trinity in the town of Olykha in the Volhynskiy Oblast, Ukraine.
The computational intelligence tool has major contribution to analyse the properties of materials without much experimentation. The B4C particles are used to improve the quality of the strength of materials. With respect to the percentage of these particles used in the micro and nano, composites may fix the mechanical properties. The different combinations of input parameters determine the characteristics of raw materials. The load, content of B4C particles with 0%, 2%, 4%, 6%, 8% and 10% will determine the wear behaviour like CoF, wear rate etc. The properties of materials like stress, strain, % of elongation and impact energy are studied. The temperature based CoF and wear rate is analysed. The temperature may vary between 30°C, 100°C and 200°C. In addition, the CoF and wear rate of materials are predicted with respect to load, weight % of B4C and nano hexagonal boron nitride %. The intelligent tools like Neural Networks (BPNN, RBNN, FL and Decision tree) are applied to analyse these characteristics of micro / nano composites with the inclusion of B4C particles and nano hBN % without physically conducting the experiments in the Lab. The material properties will be classified with respect to the range of input parameters using the computational model.
Fuzzy logic determination of the material hardening parameters based on the Heyer’s method was applied in this research. As the fuzzy input variables, the length of two measuring bases and the maximum force registered in the Heyer’s test were used. Firstly, the numerical experiment (the simulation of the fuzzification of the input data) with the assumed disturbance of input variables was performed. Next, on the basis of experimental investigations (eleven samples made from the same material), the membership functions associated with the input data were created. After that, the fuzzy analysis was examined. Fuzzy material hardening constants obtained by means of the α-level optimization and the extension principle methods were compared. Discrete values of the hardening data are found in the defuzzification process, by application of the mass center method.
The idea of using the Cloud of Things is becoming more critical for e-government, as it is considered to be a useful mechanism of facilitating the government’s work. The most important benefit of using the Cloud of Things concept is the increased productivity that the e-governments would achieve; which eventually would lead to significant cost savings; which in turn would have a highly anticipated future impact on egovernments. E-government’s diversity goals face many challenges; trust is one of the major challenges that it is facing when deploying the Cloud of Things. In this study, a new trust framework is proposed which supports trust with the Internet of Things devices interconnected to the cloud; to support the services that are provided by e-government to be delivered in a trusted manner. The proposed framework has been applied to a use case study to ensure its trustworthiness in a real mission. The results show that the proposed trust framework is useful to ensure achieving a trusted environment for the Cloud of Things for it to continue providing and gathering the data needed for the services that are offered by users through E-government.
In the paper issues related to the design of a robust adaptive fuzzy estimator for a drive system with a flexible joint is presented. The proposed estimator ensures variable Kalman gain (based on the Mahalanobis distance) as well as the estimation of the system parameters (based on the fuzzy system). The obtained value of the time constant of the load machine is used to change the values in the system state matrix and to retune the parameters of the state controller. The proposed control structure (fuzzy Kalman filter and adaptive state controller) is investigated in simulation and experimental tests.
Currently commercialization of electric vehicle (EV) is based to minimize the time of starting and acceleration. To undergo this problem multi-input multi-output fuzzy logic controller (MIMO-FLC) affect on propelled traction system forming MMS process was proposed. This paper introduces a MIMO-FLC applied on speeds of electric vehicle, the electric drive consists of two directing wheels and two rear propulsion wheels equipped with two light weight induction motors. The EV is powered by two motors of 37 kilowatts each one, delivering a 476 Nm total torque. Its high torque (476Nm) is instantly available to ensure responsive acceleration performance in built-up areas. Acceleration and steering are ensured by an electronic differential system which maintains robust control for all cases of vehicle behavior on the road. It also allows controlling independently every driving wheel to turn at different speeds in any curve. Direct torque control based on space vector modulation (DTC-SVM) is proposed to achieve the tow rear driving wheel control. The MIMO-FLC control technique is simulated in MATLAB SIMULINK environment. The simulation results have proved that the MIMO-FLC method decreases the transient oscillations and assure efficiency comportment in all type of road constraints, straight, slope, descent and curved road compared to the single input single output fuzzy controller (SISO-FLC).
The quality of the squeeze castings is significantly affected by secondary dendrite arm spacing, which is influenced by squeeze cast input parameters. The relationships of secondary dendrite arm spacing with the input parameters, namely time delay, pressure duration, squeeze pressure, pouring and die temperatures are complex in nature. The present research work focuses on the development of input-output relationships using fuzzy logic approach. In fuzzy logic approach, squeeze cast process variables are expressed as a function of input parameters and secondary dendrite arm spacing is expressed as an output parameter. It is important to note that two fuzzy logic based approaches have been developed for the said problem. The first approach deals with the manually constructed mamdani based fuzzy system and the second approach deals with automatic evolution of the Takagi and Sugeno’s fuzzy system. It is important to note that the performance of the developed models is tested for both linear and non-linear type membership functions. In addition the developed models were compared with the ten test cases which are different from those of training data. The developed fuzzy systems eliminates the need of a number of trials in selection of most influential squeeze cast process parameters. This will reduce time and cost of trial experimentations. The results showed that, all the developed models can be effectively used for making prediction. Further, the present research work will help foundrymen to select parameters in squeeze casting to obtain the desired quality casting without much of time and resource consuming.
The interrelation between fuzzy logic and cluster renewal approaches for heat transfer modeling in a circulating fluidized bed (CFB) has been established based on a local furnace data. The furnace data have been measured in a 1296 t/h CFB boiler with low level of flue gas recirculation. In the present study, the bed temperature and suspension density were treated as experimental variables along the furnace height. The measured bed temperature and suspension density were varied in the range of 1131–1156 K and 1.93–6.32 kg/m3, respectively. Using the heat transfer coefficient for commercial CFB combustor, two empirical heat transfer correlation were developed in terms of important operating parameters including bed temperature and also suspension density. The fuzzy logic results were found to be in good agreement with the corresponding experimental heat transfer data obtained based on cluster renewal approach. The predicted bed-to-wall heat transfer coefficient covered a range of 109–241 W/(m2K) and 111–240 W/(m2), for fuzzy logic and cluster renewal approach respectively. The divergence in calculated heat flux recovery along the furnace height between fuzzy logic and cluster renewal approach did not exceeded ±2%.
A solar photovoltaic (PV) system has been emerging out as one of the greatest potential renewable energy sources and is contributing significantly in the energy sector. The PV system depends upon the solar irradiation and any changes in the incoming solar irradiation will affect badly on the output of the PV system. The solar irradiation is location specific and also the atmospheric conditions in the surroundings of the PV system contribute significantly to its performance. This paper presents the cumulative assessment of the four MPPT techniques during the partial shading conditions (PSCs) for different configurations of the PV array. The partial shading configurations like series-parallel, bridge link, total cross tied and honeycomb structure for an 8#2;4 PV array has been simulated to compare the maximum power point tracking (MPPT) techniques. The MPPT techniques like perturb and observe, incremental conductance, extremum seeking control and a fuzzy logic controller were implemented for different shading patterns. The results related to the maximum power tracked, tracking efficiency of each of the MPPT techniques were presented in order to assess the best MPPT technique and the best configuration of the PV array for yielding the maximum power during the PSCs.
Solar energy is widely available in nature and electricity can be easily extracted using solar PV cells. A fuel cell being reliable and environment friendly becomes a good choice for the backup so as to compensate for continuously varying solar irradiation. This paper presents simple control schemes for power management of the DC microgrid consisting of PV modules and fuel cell as energy sources and a hydrogen electrolyzer system for storing the excess power generated. The supercapacitor bank is used as a short term energy storage device for providing the energy buffer whenever sudden fluctuations occur in the input power and the load demand. A new power control strategy is developed for a hydrogen storage system. The performance of the system is assessed with and without the supercapacitor bank and the results are compared. A comparative study of the voltage regulation of the microgrid is presented with the controller of the supercapacitor bank, realized using a traditional PI controller and an intelligent fuzzy logic controller.
This paper presents methods for optimal test frequencies search with the use of heuristic approaches. It includes a short summary of the analogue circuits fault diagnosis and brief introductions to the soft computing techniques like evolutionary computation and the fuzzy set theory. The reduction of both, test time and signal complexity are the main goals of developed methods. At the before test stage, a heuristic engine is applied for the principal frequency search. The methods produce a frequency set which can be used in the SBT diagnosis procedure. At the after test stage, only a few frequencies can be assembled instead of full amplitude response characteristic. There are ambiguity sets provided to avoid a fault tolerance masking effect.
The study presents the results of research aimed at the construction of a model of the relationship between the physical properties of metal and the types of toughening treatment and modifiers used in the modification of BA1044 alloy. Samples of melts were subjected to four variants of the heat treatment and to five types of modification. Studies of the samples consisted in measurements of five physical parameters. Consequently, it was necessary to seek a relationship between the nine input parameters and five output parameters. With this number of the variables and a limited number of samples, searching for the relationships by way of statistical methods was obviously impossible, so it was decided to create an approximate model through the use of fuzzy logic. This study describes the process of creating a model and presents the results of some simulation experiments that confirm the validity of the correct approach.