Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques

2011 ◽  
Vol 20 (8) ◽  
pp. 1143-1155 ◽  
Author(s):  
Mani Golparvar-Fard ◽  
Jeffrey Bohn ◽  
Jochen Teizer ◽  
Silvio Savarese ◽  
Feniosky Peña-Mora
Author(s):  
Cibi Pranav ◽  
Yi-Chang (James) Tsai

High friction surface treatment (HFST) is used to improve friction on curved roadways, especially on curves that have a history of wet pavement crashes. Observations on the long-term performance monitoring of HFST sections at the National Center for Asphalt Technology (NCAT) Test Track showed friction (skid number, SN) dropped significantly at the end of service life of HFST, creating unsafe driving conditions. There is no clear, observed friction deterioration trend to predict the friction drop when using friction performance measures like SN. Therefore, there is an urgent need to explore and develop supplementary HFST safety performance measures (such as aggregate loss) that can correlate to friction deterioration and provide predictable, cost-effective, and easily measurable results. The objectives of this paper are to (i) analyze the correlation between HFST aggregate loss percentage area and friction value using a dynamic friction tester (DFT), and (ii) study the characteristics of HFST deterioration associated with aggregate loss, at the NCAT Test Track and at selected HFST curve sites in Georgia (using 2D imaging and high-resolution 3D laser scanning). Results show a strong correlation between HFST aggregate loss percentage area and DFT friction coefficient. Where friction measurement is used as the primary safety performance measure, it is recommended that HFST aggregate loss be used as a supplementary performance measure for monitoring the HFST safety performance deterioration. Aggregate loss can be easily identified by characteristics such as color and texture change. Preliminary texture analyses of 3D HFST surface profiles show lower mean profile depth (MPD) and ridge-to-valley depth (RVD) texture indicators can also identify loss of aggregate spots on HFST surface.


Author(s):  
Idelfonso Tafur Monroy ◽  
Eduward Tangdiongga

2018 ◽  
Vol 7 (2.7) ◽  
pp. 652 ◽  
Author(s):  
Mandava Geetha Bhargava ◽  
P Vidyullatha ◽  
P Venkateswara Rao ◽  
V Sucharita

In most construction and Infrastructure management projects, it is important to ensure and maintain the performance, safety as well as quality in the work to execute the construction in expected period , for monitoring the above parameters i.e. Performance, Safety, Quality and as well as Security, requires data to analyze, determine and test the algorithms, due to eternal increase amount of captured data thorough modern improvements in  technology i.e. devices, camera equipped vehicles, Sensors, etc. accommodates an innovative scope to capture present status of construction sites at a less cost analogized to more alternative techniques such as laser scanning technique. Vast endeavours on documenting as-built status, nevertheless, stay at retrieving the visual data and updating Building Information Model (BIM). Hundreds of images and videos are captured but most of the data becomes scrap without proper localize with plan document and time. To take full benefits of visual data for construction status analytics where performance analytics is also included in it, three aspects (reliable, relevance and speed) of capturing, analysing and reporting visual data are captious and tracking development in construction sites needs two direction communication between field crew and management so that performances and changes issues related to task management, completion and outlook can be convey effectively. This paper deals with the investigation of current techniques for influence with help of arising BIM and big data in performance monitoring at construction from reliable, relevance and speed. 


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