A fuzzy logic approach to road project selection: A case study of Indonesia
Abstract
There has been a growing concern about the appropriateness of the existing project selection system for dealing with current situations in road improvement programming in Indonesia. Current situations are characterized by following interrelated problems: (1) funds needed to eliminate the current backlog of deteriorated roads far exceed available funds; (2) the high race of road deterioration; (3) lack of reliable data; (4) frequent project implementation delays; (5) significant adverse impacts of poor road conditions on other development activities; (6) involvement of many complex factors including social, economic, political, and environmental factors, where most of these factors are not completely defined, cannot be precisely measured, or are qualitative in nature; and (7) involvement of subjective judgments in project selection practice. To deal with these situations, the study proposed appropriate analytical tools by considering the road improvement programming process in a comprehensive manner, incorporating an assessment of present degree of deterioration and implementation requirements, recognizing needs for short term programming, and developing a simple data acquisition system. The analytical tools were developed based on a fuzzy logic approach. A fuzzy logic employs approximate, rather than exact, modes of reasoning. A basic concept in fuzzy logic that plays a key role in many of its applications is the use of linguistic variables. A linguistic variable enables decision makers to incorporate subjectivity, experience, and knowledge in an intuitive and natural way. Also, it enables field data collectors to undertake consistently subjective and approximate assessments. The data acquisition system used in the proposed methodology was developed to consider not only technical aspects such as pavement condition and geometric elements, but also broad considerations such as impacts on the surrounding community, implementation readiness, policy-related factors, and a control measure for implementing agencies. The components of the analytical tools included the following: (1) computation of the relative importance of contributing factors by using the Analytical Hierarchy Process (AHP); (2) assessment of the values of contributing factors from their corresponding indicators by using a knowledge-based expert system; and (3) ranking of candidate projects by employing fuzzy sets theory.
Degree
Ph.D.
Advisors
Sinha, Purdue University.
Subject Area
Civil engineering|Artificial intelligence
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