A fuzzy reasoning knowledge-based system for assessing rain impact in highway construction scheduling: Part 1. Analytical model

Nang-Fei Pan, Fabian C. Hadipriono, Earl Whitlatch

研究成果: Article

6 引文 (Scopus)

摘要

Rainfall is regarded as a major uncertainty factor that has adverse impacts on productivity and duration of highway construction activities. In practice, given the location, type, start date, and original duration of the activities, a common approach for construction schedulers to assess the effect of rain is by adding a certain percentage of time to tasks. However, this method depends mainly on the experience and subjective judgment of the schedulers, who may be unfamiliar with the rainfall pattern and its impact on productivity of the operations, and thus, oftentimes produces inaccurate results. This paper presents a model that uses historical daily rainfall data and experts' knowledge, and employs fuzzy set concept for assessing the impact of rain on project completion. Variables considered by the model include soil drainage, exposure level, and adverse consequence on productivity. The result of this study is a tool for use in estimating indirect rain impact on highway construction activities, and as such it can be used to make the decision to work or not to work. Examples of how to use the model are illustrated.

原文English
頁(從 - 到)157-167
頁數11
期刊Journal of Intelligent and Fuzzy Systems
16
發行號3
出版狀態Published - 2005 九月 30

指紋

Fuzzy Reasoning
Knowledge-based Systems
Knowledge based systems
Analytical Model
Rain
Analytical models
Rainfall
Scheduling
Productivity
Scheduler
Inaccurate
Date
Fuzzy Sets
Completion
Soil
Percentage
Model
Fuzzy sets
Uncertainty
Drainage

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

引用此文

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