Browsing by Subject "Automated data collection"
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Item Assessment of automated technologies in Texas for pavement distress identification, texture, and cross slope measurement(2014-05) Burton, Maria Christina; Prozzi, Jorge AlbertoAutomated technologies can be beneficial for collecting data on the condition of pavements. As opposed to a traditional manual survey of the road, automated data collection can provide a safer alternative that is objective, repeatable, and consistent, while traveling at highway speeds. Though the automated method is preferred, it still needs to be reliable enough to accurately model the current pavement performance. The Texas Department of Transportation (TxDOT) initiated a project to allow an independent assessment of the accuracy and repeatability of new automated distress data measurements. In this study, 20 550-ft. pavement sections were tested with automated data collection technologies. The sections were located in Austin and Waco Districts. The accuracy and repeatability was evaluated for cracking and other distress measurements, cross slope measurements, and texture measurements. Known manual methods were used as a reference, and a 3D system developed by TxDOT was compared with three systems of other vendors (Dynatest, Fugro, and Waylink-OSU). With the data provided for the texture and cross slope, an additional investigation was done to evaluate hydroplaning potential. This thesis reports in the latter investigation.Item Rapid and contextual activity analysis : a semi-automated activity category, time, location, and video data collection and analysis methodology(2015-08) Kim, Jung Yeol; Caldas, Carlos H.; Borcherding, John D; Leite, Fernanda; Grauman, Kristen; Zhang, ZhanminThe performance of construction projects is significantly impacted by on-site labor and the productivity thereof. Despite the benefits from technological advancements in recent decades, construction projects are still labor intensive, and labor is one of the most flexible and largest cost factors in a construction project. Thus, a major concern of construction project management has been labor productivity and its improvement. To improve it, labor productivity must be measured and analyzed. One way of doing so is through activity analysis - known as an extension of traditional work sampling. Activity analysis measures the efficiency of the workers' time usage at a construction site. Increasing labor efficiency usually has a positive relationship with higher construction labor productivity. Therefore, activity analysis is considered a major labor performance assessment technique in this research. The objective of this research is to develop a semi-automated data collection and analysis methodology to enable fast and contextual activity analysis. More specifically, this research focuses on the man-machine balanced on-site data collection and the automated data analysis with abundant contextual information to support the interpretation of analysis results for labor productivity improvement study. The prototype of the proposed methodology is implemented and validated with actual datasets from different construction sites. The prototype system proves capable of collecting data efficiently at construction sites and to analyze it in an automatic fashion. This system is shown to provide abundant contextual information related to the activity analysis results. A project manager can quickly and easily find issues related to their high or low labor performance with various scenarios. The indexed videos also successfully provide information about what/how construction workers were performing work at that point. This information can support productivity improvement planning and expedite the continuous evaluation and improvement process of activity analysis to improve labor productivity.