چكيده به لاتين
Abstract:
Pavement condition data is a major component of network-level Pavement Management Systems (PMS). The quality of this data is important not only in assessing the current condition of the network but also in the prediction of future condition and the planning of future maintenance and rehabilitation (M&R) activities. While there is a general agreement in the literature that the quality of condition data affects PMS outputs, little work has been done to quantify these effects. In this thesis to enhance the quality of pavement condition data at the network level using conditional probability known as a statistical technique, likely error in pavement condition data has been detected. In this technique, the probability of a certain PCI is calculated on the condition of the occurrence of a specified IRI according to available data set which the smaller probability will be higher chance of data error. After that, the effect of this likely error on network-level pavement maintenance program has been investigated. This process has been done on pavement condition data set includes pavement condition index and international roughness index during the 830 lane-km of the road network of the Fars province in 2009. The results show that the probability that roughness index (IRI) of pavement sections be in good condition and at the same time pavement condition index (PCI) be in poor or very poor condition, is very low and in other words, there will be a very high probability of error in this part of the data. According to results, removing only 3 percent of total network suspicious data, leading to a 18 percent decrease in network budget and road network service life will be faced with an increase of 35 percent. Also by increasing network budget, the impact of likely errors in the network will be declined and while the likely errors in the low budget present themselves more effective.
Keywords: Pavement Management, Pavement Condition Data, Data Quality, Likely Error, Error Detection
