Decision Support Models for Asset Management of Low-Volume Roads

2011 ◽  
Vol 2205 (1) ◽  
pp. 181-188 ◽  
Author(s):  
A. Veeraragavan ◽  
P Murali Krishna
Keyword(s):  
Decision Support ◽  
Asset Management ◽  
Low Volume Roads ◽  
Decision Support Models ◽  
Low Volume

Cost-Effective Structures for Off-System Bridges

2003 ◽  
Vol 1819 (1) ◽  
pp. 397-404
Author(s):  
Francesco M. Russo ◽  
Terry J. Wipf ◽  
F. Wayne Klaiber
Keyword(s):  
Asset Management ◽  
Cost Effective ◽  
The United States ◽  
Federal Aid ◽  
Rural And Urban ◽  
Low Volume Roads ◽  
Bridge Strengthening ◽  
Maintenance And Rehabilitation ◽  
Low Volume ◽  
Local Agencies

Nearly half of the 587,000 bridges in excess of 6.1 m (20 ft) long on public roads in the United States are located off the Federal Aid System, are on local rural and urban roads or rural minor collectors, and are classified as off-system bridges. Approximately one-third of the off-system bridges are structurally deficient or functionally obsolete. The majority of states with large bridge populations are rural states with large percentages of structures owned by counties and other local agencies. Many of these bridges are on low-volume roads. Given the size of the deficient bridge population and the concentration of these bridges largely on locally owned networks, a recent NCHRP synthesis (NCHRP 32-08, Cost Effective Structures for Off-System Bridges) has explored the current practices regarding the maintenance, rehabilitation, and replacement of off-system bridges. The administrative aspects of off-system bridge ownership and management were explored, including sources and problems of funding, bridge asset management, design policies for off-system bridges, exploration of the environmental process, and discussions of interagency partnering and coordination problems. A significant body of work exists on effective bridge maintenance and rehabilitation. A significant discussion of bridge strengthening, including specific techniques used on low-volume-road bridges, was also examined. Concerning bridge replacements, the literature and owner survey were used to provide information on successful concepts currently being used. In addition to successful bridge replacement options, discussion of the use of standard plans, design aids, and software is promoted as leading to standard and efficient low-volume-road bridge replacements.

Study & Experimental Analysis of Pervious Concrete In Low Volume Roads

2018 ◽  
pp. 172-176
Author(s):  
Suraj Pinate ◽  
Hitesh Sonawane ◽  
Jayesh Barhate ◽  
Mayur Chaudhari ◽  
Utkarsha Dhok ◽  
...  
Keyword(s):  
Experimental Analysis ◽  
Pervious Concrete ◽  
Low Volume Roads ◽  
Low Volume

Models of decision support in socio-cyber-physical systems

2019 ◽  
pp. 55-70 ◽  
Author(s):  
A. V. Smirnov ◽  
T. V. Levashova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Situation Awareness ◽  
Cyber Physical Systems ◽  
Emergent Behavior ◽  
Emergent Properties ◽  
Decision Making Process ◽  
Physical Systems ◽  
Decision Support Models ◽  
Common Goals

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.

A Decision Support Tool for Wastewater Catchment Management

2007 ◽  
Vol 2 (2) ◽  
Author(s):  
S.E. Walters ◽  
D. Savic ◽  
R.J. Hocking
Keyword(s):  
Decision Support ◽  
Asset Management ◽  
Capital Investment ◽  
Management Strategies ◽  
Sewage Treatment ◽  
Decision Support Tool ◽  
Catchment Management ◽  
Clean Water ◽  
Support Tool ◽  
South West

The water industry over the years has primarily focussed on upgrading and investing in clean water provision. However, as research into the science and management of clean water services has progressed rapidly, wastewater provision and services has been slower. Focus, though, is now shifting within Industry and Research into wastewater services. The water regulator, Ofwat, for England and Wales demands the Sewerage Undertakers demonstrate efficient management of wastewater systems in order to obtain funding for Capital Investment projects. South West Water, a Water Service Provider and Sewerage Undertaker located in the South West of England, identified a need gap in their asset management strategies for wastewater catchments. This paper will introduce the production of a Decision Support Tool, DST, to help SWW proactively manage their Wastewater Catchments, examining Sewage Treatment Works, Pumping Stations and Networks. The paper will discuss some concepts within the DST, its production, testing and a brief case study. The DST provides a framework for prioritising catchments to optimise investment choices and actions. The Tool ranks catchments utilising Compromise Programming, CP, as well as AHP Pair-wise comparisons for preference weights. The DST incorporates Asset models, a Whole life Costing Module, as well as a Decay and Intervention Module.

Comparison of Superpave and Marshall Mixtures for Low-Volume Roads and Shoulders

1998 ◽  
Vol 1609 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Affan Habib ◽  
Mustaque Hossain ◽  
Rajesh Kaldate ◽  
Glenn Fager
Keyword(s):  
Mix Design ◽  
River Sand ◽  
Low Volume Roads ◽  
Superpave Gyratory Compactor ◽  
Asphalt Content ◽  
Low Volume ◽  
Project Site ◽  
Axle Loads ◽  
Crushed Limestone ◽  
Asphalt Film

Superpave and Marshall mix designs using local aggregates were done to study the suitability of the Superpave mix design as compared with the Marshall mix design for low-volume roads, especially shoulders. The project site was Kansas Route 177 in northeast Kansas. Three locally available aggregates, crushed limestone and coarse and fine river sands, were used in this study. Five blends with varying proportions of coarse and fine river sands were selected. Mix samples were compacted in the Superpave gyratory compactor with the applicable number of gyrations and were compacted with the Marshall hammer by using 50 blows per face. Bulk densities of the compacted samples and maximum specific gravities of loose samples also were measured for each blend. The results show that the Superpave mix design for low-volume roads and shoulders results in lower estimated asphalt content than does the Marshall method. The required asphalt content increases as the proportion of coarse river sand increases in the mix. Superpave requirements for the voids filled with asphalt (VFA) for low-volume traffic, that is, less than 0.3 million equivalent single-axle loads, appeared to be too high. High asphalt film thicknesses were computed for the mixtures that did not meet the Superpave VFA requirements. Lowering the design number of gyrations (Ndes) for compaction of samples would result in increased asphalt requirement for the Superpave mixture with a given gradation.

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