Fuzzy approach to construction activity estimation

Past experience has shown that variations in production rate value for the same work item is attributed to a wide range of factors. The relationships between these factors and the production rates are often very complex. It is impossible to describe an exact mathematical causal relationship between the qualitative factors(QF) and production rates. Various subjective approaches have been attempted to quantify the uncertainties contained in these causal relationships. This thesis presents one such approach by adopting a fuzzy set theory in conjunction with a fuzzy rule based system that could improve the quantification of the qualitative factors in estimating construction activity durations and costs. A method to generate a Standard Activity Unit Rate(SAUR) is presented. A construction activity can be defined by combining the Design Breakdown Structure, Trade Breakdown Structure and Work Section Breakdown Structure. By establishing the data structure of an activity, it is possible to synthesis the SAUR from published estimating sources in a systematic way. After the SAUR is defined, it is then used as a standard value from which an appropriate Activity Unit Rate(AUR) can be determined. A proto-type fuzzy rule based system called 'Fuzzy Activity Unit Rate Analyser(FAURA)' was developed to formalise a systematic framework for the QF quantification process in determining the most likely activity duration/cost. The compatibility measurement method proposed by Nafarieh and Keller has been applied as an inference strategy for FAURA. A computer program was developed to implement FAURA using Turbo Prolog. FAURA was tested and analysed by using a hypothetical bricklayer's activity in conjunction with five major QF as the input variables. The results produced by FAURA iii show that it can be applied usefully to overcome many of the problems encountered in the QF quantification process. In addition, the analysis shows that a fuzzy rule base approach provides the means to model and study the variability of AUR. Although the domain problem of this research was in estimation of activity duration/cost, the principles and system presented in this study are not limited to this specific area, and can be applied to a wide range of other disciplines involving uncertainty quantification problems. Further, this research highlights how the existing subjective methods in activity duration/cost estimation can be enhanced by utilising fuzzy set theory and fuzzy logic

Fuzzy Knowledge Based Reliability Evaluation and Its Application to Power Generating System

PhDThe method of using Fuzzy Sets Theory(FST) and Fuzzy Reasoning(FR) to aid reliability evaluation in a complex and uncertain environment is studied, with special reference to electrical power generating system reliability evaluation. Device(component) reliability prediction contributes significantly to a system's reliability through their ability to identify source and causes of unreliability. The main factors which affect reliability are identified in Reliability Prediction Process(RPP). However, the relation between reliability and each affecting factor is not a necessary and sufficient one. It is difficult to express this kind of relation precisely in terms of quantitative mathematics. It is acknowledged that human experts possesses some special characteristics that enable them to learn and reason in a vague and fuzzy environment based on their experience. Therefore, reliability prediction can be classified as a human engineer oriented decision process. A fuzzy knowledge based reliability prediction framework, in which speciality rather than generality is emphasised, is proposed in the first part of the thesis. For this purpose, various factors affected device reliability are investigated and the knowledge trees for predicting three reliability indices, i.e. failure rate, maintenance time and human error rate are presented. Human experts' empirical and heuristic knowledge are represented by fuzzy linguistic rules and fuzzy compositional rule of inference is employed as inference tool. Two approaches to system reliability evaluation are presented in the second part of this thesis. In first approach, fuzzy arithmetic are conducted as the foundation for system reliability evaluation under the fuzzy envimnment The objective is to extend the underlying fuzzy concept into strict mathematics framework in order to arrive at decision on system adequacy based on imprecise and qualitative information. To achieve this, various reliability indices are modelled as Trapezoidal Fuzzy Numbers(TFN) and are proceeded by extended fuzzy arithmetic operators. In second approach, the knowledge of system reliability evaluation are modelled in the form of fuzzy combination production rules and device combination sequence control algorithm. System reliability are evaluated by using fuzzy inference system. Comparison of two approaches are carried out through case studies. As an application, power generating system reliability adequacy is studied. Under the assumption that both unit reliability data and load data are subjectively estimated, these fuzzy data are modelled as triangular fuzzy numbers, fuzzy capacity outage model and fuzzy load model are developed by using fuzzy arithmetic operations. Power generating system adequacy is evaluated by convoluting fuzzy capacity outage model with fuzzy load model. A fuzzy risk index named "Possibility Of Load Loss" (POLL) is defined based on the concept of fuzzy containment The proposed new index is tested on IEEE Reliability Test System (RTS) and satisfactory results are obtained Finally, the implementation issues of Fuzzy Rule Based Expert System Shell (FRBESS) are reported. The application of ERBESS to device reliability prediction and system reliability evaluation is discussed

Semantics of fuzzy quantifiers

The aim of this thesis is to discuss the semantics of FQs (fuzzy quantifiers), formal semantics in particular. The approach used is fuzzy semantic based on fuzzy set theory (Zadeh 1965, 1975), i.e. we explore primarily the denotational meaning of FQs represented by membership functions. Some empirical data from both Chinese and English is used for illustration. A distinguishing characteristic of the semantics of FQs like about 200 students and many students as opposed to other sorts of quantifiers like every student and no students, is that they have fuzzy meaning boundaries. There is considerable evidence to suggest that the doctrine that a proposition is either true or false has a limited application in natural languages, which raises a serious question towards any linguistic theories that are based on a binary assumption. In other words, the number of elements in a domain that must satisfy a predicate is not precisety given by an FQ and so a proposition con¬ taining one may be more or less true depending on how closely numbers of elements approximate to a given norm. The most significant conclusion drawn here is that FQs are compositional in that FQs of the same type function in the same way to generate a constant semantic pattern. It is argued that although basic membership functions are subject to modification depending on context, they vary only with certain limits (i.e. FQs are motivated—neither completely predicated nor completely arbitrary), which does not deny compositionality in any way. A distinctive combination of compositionality and motivation of FQs makes my formal semantic framework of FQs unique in the way that although some specific values, such as a norm, have to be determined pragmatically, semantic and inferential patterns are systematic and predictable. A number of interdisciplinary implications, such as semantic, general linguistic, logic and psychological, are discussed. The study here seems to be a somewhat troublesome but potentially important area for developing theories (and machines) capable of dealing with, and accounting for, natural languages

Unsupervised and Supervised Fuzzy Neural Network Architecture, with Applications in Machine Vision Fuzzy Object Recognition and Inspection

Mechanical Engineerin

Fuzzy expert systems in civil engineering

Imperial Users onl

Fuzzy control and its application to a pH process

In the chemical industry, the control of pH is a well-known problem that presents difficulties due to the large variations in its process dynamics and the static nonlinearity between pH and concentration. pH control requires the application of advanced control techniques such as linear or nonlinear adaptive control methods. Unfortunately, adaptive controllers rely on a mathematical model of the process being controlled, the parameters being determined or modified in real time. Because of its characteristics, the pH control process is extremely difficult to model accurately. Fuzzy logic, which is derived from Zadeh's theory of fuzzy sets and algorithms, provides an effective means of capturing the approximate, inexact nature of the physical world. It can be used to convert a linguistic control strategy based on expert knowledge, into an automatic control strategy to control a system in the absence of an exact mathematical model. The work described in this thesis sets out to investigate the suitability of fuzzy techniques for the control of pH within a continuous flow titration process. Initially, a simple fuzzy development system was designed and used to produce an experimental fuzzy control program. A detailed study was then performed on the relationship between fuzzy decision table scaling factors and the control constants of a digital PI controller. Equation derived from this study were then confirmed experimentally using an analogue simulation of a first order plant. As a result of this work a novel method of tuning a fuzzy controller by adjusting its scaling factors, was derived. This technique was then used for the remainder of the work described in this thesis. The findings of the simulation studies were confirmed by an extensive series of experiments using a pH process pilot plant. The performance of the tunable fuzzy controller was compared with that of a conventional PI controller in response to step change in the set-point, at a number of pH levels. The results showed not only that the fuzzy controller could be easily adjusted to provided a wide range of operating characteristics, but also that the fuzzy controller was much better at controlling the highly non-linear pH process, than a conventional digital PI controller. The fuzzy controller achieved a shorter settling time, produced less over-shoot, and was less affected by contamination than the digital PI controller. One of the most important characteristics of the tunable fuzzy controller is its ability to implement a wide variety of control mechanisms simply by modifying one or two control variables. Thus the controller can be made to behave in a manner similar to that of a conventional PI controller, or with different parameter values, can imitate other forms of controller. One such mode of operation uses sliding mode control, with the fuzzy decision table main diagonal being used as the variable structure system (VSS) switching line. A theoretical explanation of this behavior, and its boundary conditions, are given within the text. While the work described within this thesis has concentrated on the use of fuzzy techniques in the control of continuous flow pH plants, the flexibility of the fuzzy control strategy described here, make it of interest in other areas. It is likely to be particularly useful in situations where high degrees of non-linearity make more conventional control methods ineffective