Rainfall often causes adverse impact on productivity and completion of highway construction projects. Therefore, adequate estimates of the effect of rain are mandatory for construction contractors to establish reliable schedules. Based on the analytical model introduced in the Part 1 of this paper, a fuzzy-based and knowledge-based intelligent scheduling system for estimating rainfall effect on productivity and duration of highway activities is presented here. This system contains a knowledge base, database, and fuzzy logic models. The knowledge base consists of fuzzy if-then rules for use to assess the impact of rain-related variables on the duration of twenty typical highway activities. The database includes historical daily rainfall data over the past 20 years for nine major cities across Taiwan where precipitation data is available. The fuzzy inference mechanism was developed for use in assessing the chance to work by considering the impact of rain. A case study involving a highway construction project implemented in two geographic areas with different rainfall environments is presented to illustrate the salient features of the fuzzy reasoning knowledge-based scheduling system (FRESS). System performance was tested by experts and potential users alike with satisfactory results. FRESS allows users to simulate experts' judgment and assists contractors to better estimate activity durations for projects in geographical locations having rainfall data.
|Number of pages||11|
|Journal||Journal of Intelligent and Fuzzy Systems|
|Publication status||Published - 2005|
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Artificial Intelligence