Learn More
We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic(More)
One of the common endeavours in engineering applications is outlier detection, which aims to identify inconsistent records from large amounts of data. Although outlier detection schemes in data mining discipline are acknowledged as a more viable solution to efficient identification of anomalies from these data repository, current outlier mining algorithms(More)
Construction equipment constitutes a significant portion of investment in fixed assets by large contractors. To make the right decisions on equipment repair, rebuilding, disposal, or equipment fleet optimization to maximize the return of investment, the contractors need to predict the residual value of heavy construction equipment to an acceptable level of(More)
Reliability and availability of the equipment or plants used in construction and civil engineering field is significant issue for all stakeholders. Unexpected breakdown and repairs could cause serious consequences such as extra cost and project period extension. Therefore, it is necessary to study the reliability of the construction equipment and predict(More)
Construction equipment management and performance data are valuable assets for large contractors that need such historical data for decision making about resource allocation and equipment replacement; however, with large amounts of accumulated data, traditional data analysis based on a transactional system becomes increasingly inefficient. This paper(More)
Surveys found that large contractors replace approximately 10% of their equipment fleet units annually in North America. Cost minimization model is a commonly accepted method for equipment replacement which helps to identify these equipment units whose total owning and operating cost reaches their minimum point as candidates for replacement. While the model(More)
This paper presents a time series analysis based on General Regression Neural Networks (GRNN) models to address the prediction of construction equipment maintenance costs. The results show that GRNN can model the behaviour and predict the maintenance costs for different equipment categories and fleet with satisfactory accuracy. The paper also discusses the(More)
  • 1