56 research outputs found
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
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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
Systems Approach to the Concept of Environment
Author Institution: Department of Zoology and Institute of Ecology, University of GeorgiaA systems theory of environment formulates causal interactions between things, including organisms, and their environments in terms of four system theoretical abstract objects. Creaons receive stimuli and implicitly create input environments. Genons react to received causes and generate potential output environments as effects. A holon represents the combined input-output model of an entity consisting of a creaon and a genon. An environ is a creaon and its corresponding input environment, or a genon and its related output environment. The theory is presented in terms of three propositions that: (1) recognize two distinct environments (input and output) associated with things, (2) establish things and their environments as units (environs) to be taken together, and (3) partition systems into input and output environs associated with intrasystem creaons and genons, respectively
Computer Architecture for Object Recognition and Sensing
The notion of using many, most likely different, sensory subsystems in a computer object recognition system immediately provokes several questions:
- How will multiple sensors be used in conjunction?
- What object qualities are best described by which sensor, and how is sensor utilization optimized?
- To what extent does the information provided by each sensor overlap with that provided by others, and how then is this used
Book reviews
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45933/1/357_2005_Article_BF01195682.pd
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
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