Probabilistic soil identification based on cone penetration tests

In geotechnical engineering, soil classification is an essential component in the design process. Field methods such as the cone penetration test (CPT) can be used as less expensive and faster alternatives to sample retrieval and testing. Unfortunately, current soil classification charts based on CPT data and laboratory measurements are too generic, and may not provide an accurate prediction of the soil type. A probabilistic approach is proposed here to update and modify soil identification charts based on site-specific CPT data. The probability that a soil is correctly classified is also estimated. The updated identification chart can be used for a more accurate prediction of the classification of the soil, and can account for prior information available before conducting the tests, site-specific data, and measurement errors. As an illustration, the proposed approach is implemented using CPT data from the Treporti Test Site (TTS) near Venice (Italy) and the National Geotechnical Experimentation Sites (NGES) at Texas A&M University. The applicability of the site-specific chart for other sites in Venice Lagoon is assessed using data from the Malamocco test site, approximately 20 km from TTS.

Comparative study of vision tracking methods for tracking of construction site resources

Vision tracking has significant potential for tracking resources on large scale, congested construction sites, where a small number of cameras strategically placed around the site could replace hundreds of tracking tags. The correlation of vision tracking 2D positions from multiple views can provide the 3D position. However, there are many 2D vision trackers available in the literature, and little information is available on which one is most effective for construction applications. In this paper, a comparative study of various vision tracker categories is carried out, to identify which one is most effective in tracking construction resources. Testing parameters for evaluating categories of trackers are identified, and benefits and limitations of each category are presented. The most promising trackers are tested using a database of construction operations videos. The results indicate the effectiveness of each tracker in relation to each parameter of the test, and the most suitable tracker needed to research effective 3D vision trackers of construction resources.