Hongqin Fan

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We present a novel resolution-based outlier notion and a nonparamet-ric 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)
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)
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)
Equipment logistics, maintenance, and repair are important aspects of construction equipment management. A well-managed equipment fleet helps reduce downtime, as well as total maintenance and repair costs. With quickly growing fleets of equipment, large contractors tend to divert the maintenance and repair of equipment from equipment managers to project(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)
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)
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