Showing 2 results for Fuzzy Model
U. H Issa, A. Ahmed,
Volume 12, Issue 2 (4-2014)
Abstract
Driven Precast Reinforced Concrete Piles (DPRCP) is extensively used as a foundation for bridges constructed over canals
in Egypt in order to avoid the diversion of water canals. The objectives of this research include identifying the main activities
of DPRCP execution in the bridge-construction industry in Egypt and the risk factors affecting them. In addition, assessment of
the effects of these risk factors on the quality of activities of DPRCP. Four activities are identified in order to execute the
process of construction of DPRCP. These activities include: preparing and casting piles, positioning piles and steering the
driving machine, handling piles, and driving piles. Thirty one risk factors affecting the DPRCP activities execution are
identified. A survey was executed in Egypt concerning probabilities of occurrence of these factors and their impacts on the
quality of activities of DPRCP. In addition, a new membership function is introduced to represent the quality of activities and
used in a fuzzy model for factors assessment. Results showed that the proposed membership function can be used effectively to
assess the quality of activities associated with the construction of DPRCP. A list of risk factors is highlighted to show the most
critical risk factors that help in preparing the quality management plan for the upcoming similar projects. The gentile
distribution of data obtained for the different activities proved that the investigated risk factors for the DPRCP in this study
are significant.
M. Effati, M. A. Rajabi, F. Samadzadegan, Sh. Shabani,
Volume 12, Issue 3 (9-2014)
Abstract
Road transportation by way of automobiles is a very convenient means of transportation. Today, the most detrimental consequence of developing transportation systems in a country is traffic accident that places a huge financial burden on society. This paper investigates the role of information systems in transportation safety that leads to improved planning and operation of the transportation system through the application of new technologies. Current methods for identification of segments of roads with high potential of accident are based on statistical approaches. Since there are not accident records for newly built roads, these methods cannot be used for regional roads that are recently built. This paper presents a GIS based Neuro-Fuzzy modeling for identification of road hazardous zones. The results of proposed approach are compared with statistical methods. It is shown that this method is a cheaper but at the same time robust means of analyzing the level of hazard associated with each road segment under consideration, specially when data are uncertain and incomplete.
