Discussion: Knowledge-based advisory system for flexible pavement routine maintenance

Abstract

The authors are to be complimented for a very interesting article, describing the development of a knowledge-based expert system for the maintenance of flexible pavement, and comparing it to available similar systems. The content of the paper is clear and well structured, and the quality of the language and presentation is very good. Traditionally, the highway and road maintenance programs in many American and Canadian agencies had a low priority, and were often left to the local authorities. Cost not being a major concern, the decision-making process was sometimes made with limited practical experience and little technical appropriate expertise. In recent years, the maintenance budget has been increasing rapidly at the expenses of the new construction budget. Consequently, the need for decision support systems to provide the appropriate knowledge to the right persons at the right time is more than urgent (Akhras and Foo 1993; OCDE 1992). The authors rightly pointed out the problem and their paper demonstrated the advantages and benefits of applying-expert system technology to the decision-making process in highway engineering and operations. A few questions and comments need to be addressed and discussed further more. 1. The authors developed two distinctive and separate systems for PMAS: the first one for IBM-PC using Exsys Professional as a development shell, and the second one for Macintosh using Instant Expert Plus. The move from the first development shell to the second was made mainly for the ability of the Instant Expert Plus shell to "associate facts and expressions with figures" and to give "powerful user interface by providing pictures during runs." The writers believe that a brief description of this facility and capability together with illustrative screen displays using graphics and pictures would have been very helpful to the readers in realizing the graphic capability of PMAS. 2. As the authors already know, the first generation of