Efficient fault tree analysis using binary decision diagrams

The Binary Decision Diagram (BDD) method has emerged as an alternative to conventional techniques for performing both qualitative and quantitative analysis of fault trees. BDDs are already proving to be of considerable use in reliability analysis, providing a more efficient means of analysing a system, without the need for the approximations previously used in the traditional approach of Kinetic Tree Theory. In order to implement this technique, a BDD must be constructed from the fault tree, according to some ordering of the fault tree variables. The selected variable ordering has a crucial effect on the resulting BDD size and the number of calculations required for its construction; a bad choice of ordering can lead to excessive calculations and a BDD many orders of magnitude larger than one obtained using an ordering more suited to the tree. Within this thesis a comparison is made of the effectiveness of several ordering schemes, some of which have not previously been investigated. Techniques are then developed for the efficient construction of BDDs from fault trees. The method of Faunet reduction is applied to a set of fault trees and is shown to significantly reduce the size of the resulting BDDs. The technique is then extended to incorporate an additional stage that results in further improvements in BDD size. A fault tree analysis strategy is proposed that increases the likelihood of obtaining a BDD for any given fault tree. This method implements simplification techniques, which are applied to the fault tree to obtain a set of concise and independent subtrees, equivalent to the original fault tree structure. BDDs are constructed for each subtree and the quantitative analysis is developed for the set of BDDs to obtain the top event parameters and the event criticality functions

A fault tree analysis strategy using binary decision diagrams

The use of Binary Decision Diagrams (BDDs) in fault tree analysis provides both an accurate and efficient means of analysing a system. There is a problem however, with the conversion process of the fault tree to the BDD. The variable ordering scheme chosen for the construction of the BDD has a crucial effect on its resulting size and previous research has failed to identify any scheme that is capable of producing BDDs for all fault trees. This paper proposes an analysis strategy aimed at increasing the likelihood of obtaining a BDD for any given fault tree, by ensuring the associated calculations are as efficient as possible. The method implements simplification techniques, which are applied to the fault tree to obtain a set of 'minimal' subtrees, equivalent to the original fault tree structure. BDDs are constructed for each, using ordering schemes most suited to their particular characteristics. Quantitative analysis is performed simultaneously on the set of BDDs to obtain the top event probability, the system unconditional failure intensity and the criticality of the basic events

City of Hampton, Virginia Shoreline Inventory Report Methods and Guidelines

The data inventory developed for the Shoreline Inventory is based on a three‑tiered shoreline assessment approach. In most cases this assessment characterizes conditions that can be observed from a small boat navigating along the shoreline. The three tiered shoreline assessment approach divides the shorezone into three regions: 1) the immediate riparian zone, evaluated for land use; 2) the bank, evaluated for height, stability, cover and natural protection; and 3) the shoreline, describing the presence of shoreline structures for shore protection and recreational purposes. Hand-held GPS units are used to log features observed in the field. Three GIS coverages are developed from the GPS field files. The first describes land use and bank conditions (hamp_lubc). The second reports shoreline structures that are collected as arcs or lines (hamp_sstru). The final coverage includes all structures that are represented as points (hamp_astru). The coverages use a shoreline basemap generated in-house from the Virginia Base Mapping Program’s high resolution digital terrain model from 2009. The shoreline is re‑coded to reflect features and attributes observed in the field. The metadata file accompanies the coverages and defines attribute accuracy, data development, and any use restrictions that pertain to data

Delaware Shoreline Inventory: Appoquinimink River, Blackbird Creek, St. Jones River

This shoreline inventory is developed as a tool for assessing conditions along primary shoreline in three watersheds that discharge into Delaware Bay. Field data were collected between September 11-13, 2007. Conditions are reported for three zones within the immediate riparian river area: riparian land use, bank and buffers, and the shoreline. A series of maps, tabular data, and GIS files are posted to a website and available electronically to serve as a resource to all managers and planners within the three watersheds. The survey provides a baseline to which future conditions can be compared and tracked through time. Access report via Download tab

Summary Tables: City of Hampton, Virginia Shoreline Inventory Report

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes)

Summary Tables: Prince William County, Virginia Shoreline Inventory Report

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes)

Prince William County, Virginia Shoreline Inventory Report Methods and Guidelines

The data inventory developed for the Shoreline Inventory is based on a three‑tiered shoreline assessment approach. In most cases this assessment characterizes conditions that can be observed from a small boat navigating along the shoreline. The three tiered shoreline assessment approach divides the shorezone into three regions: 1) the immediate riparian zone, evaluated for land use; 2) the bank, evaluated for height, stability, cover and natural protection; and 3) the shoreline, describing the presence of shoreline structures for shore protection and recreational purposes. Hand-held GPS units are used to log features observed in the field. Three GIS coverages are developed from the GPS field files. The first describes land use and bank conditions (pwill_lubc). The second reports shoreline structures that are collected as arcs or lines (pwill_sstru). The final coverage includes all structures that are represented as points (pwill_astru). The coverages use a shoreline basemap generated in-house from the Virginia Base Mapping Program’s high resolution digital terrain model from 2009. The shoreline is re‑coded to reflect features and attributes observed in the field. The metadata file accompanies the coverages and defines attribute accuracy, data development, and any use restrictions that pertain to data

Fairfax County and the City of Alexandria, Virginia Shoreline Inventory Report Methods and Guidelines

The data inventory developed for the Shoreline Inventory is based on a three‑tiered shoreline assessment approach. In most cases this assessment characterizes conditions that can be observed from a small boat navigating along the shoreline. The three tiered shoreline assessment approach divides the shorezone into three regions: 1) the immediate riparian zone, evaluated for land use; 2) the bank, evaluated for height, stability, cover and natural protection; and 3) the shoreline, describing the presence of shoreline structures for shore protection and recreational purposes. Hand-held GPS units are used to log features observed in the field. Three GIS coverages are developed from the GPS field files. The first describes land use and bank conditions (alex_lubc; fairfax_lubc). The second reports shoreline structures that are collected as arcs or lines (alex_sstru; fairfax_sstru). The final coverage includes all structures that are represented as points (alex_astru; fairfax_astru). The coverages use a shoreline basemap generated in-house from the Virginia Base Mapping Program’s high resolution digital terrain model from 2009. The shoreline is re‑coded to reflect features and attributes observed in the field. The metadata file accompanies the coverages and defines attribute accuracy, data development, and any use restrictions that pertain to data

Summary Tables: 2012 Prince William County, Virginia Shoreline Inventory

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes). This particular set of Summary Tables includes tidal marshes only as an amendment to the 2010 Prince William County Summary Tables. Dominant plant community types were primarily determined during 2012 field surveys from shallow-draft boats moving along the shoreline. Land-based surveys were performed in some locations