Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Spatial Clustering for Determining Economical Highway Pavement Let Projects
This paper explores new methods that can reduce pavement preservation costs by incorporating information technology. One way to reduce costs and improve efficiency is to group adjacent pavement projects into a single let project composed of a series of pavement segments, each segment being equal to or less than 1 mile. If distresses vary from location to location and can be grouped into sub-projects that have uniform distress conditions, a more appropriate rehabilitation treatment method can be used for each sub-project instead of a single, more costly treatment for the entire project. This paper presents a spatial search algorithm using fuzzy c-mean clustering to determine the most economical let project termini by minimizing the pavement condition variations in each let project while being subject to the constraints of minimal project scope (i.e. length), cost and barriers, such as bridges. This paper presents preliminary results using hypothetical highway pavement condition data to demonstrate the capability of the developed algorithm. The benefits of using the developed algorithm are summarized, and recommendations for future research are discussed.
Spatial Clustering for Determining Economical Highway Pavement Let Projects
This paper explores new methods that can reduce pavement preservation costs by incorporating information technology. One way to reduce costs and improve efficiency is to group adjacent pavement projects into a single let project composed of a series of pavement segments, each segment being equal to or less than 1 mile. If distresses vary from location to location and can be grouped into sub-projects that have uniform distress conditions, a more appropriate rehabilitation treatment method can be used for each sub-project instead of a single, more costly treatment for the entire project. This paper presents a spatial search algorithm using fuzzy c-mean clustering to determine the most economical let project termini by minimizing the pavement condition variations in each let project while being subject to the constraints of minimal project scope (i.e. length), cost and barriers, such as bridges. This paper presents preliminary results using hypothetical highway pavement condition data to demonstrate the capability of the developed algorithm. The benefits of using the developed algorithm are summarized, and recommendations for future research are discussed.
Spatial Clustering for Determining Economical Highway Pavement Let Projects
Tsai, Yichang (James) (Autor:in) / Yang, Chientai (Autor:in) / Wang, Zhaohua (Autor:in)
GeoCongress 2006 ; 2006 ; Atlanta, Georgia, United States
GeoCongress 2006 ; 1-6
21.02.2006
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Spatial Clustering for Determining Economical Highway Pavement Let Projects
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