On-site data collection during construction activities help in evaluating productivity rates and preparing more accurate schedules. One of the challenges here is in collecting data automatically such that activity start times and durations can be computed reliably. This paper proposes a methodology to infer construction activities that are being performed on site using the structural responses collected from construction equipments. This methodology is applied to the case of a launching girder, an equipment used in the construction of viaducts in metro rail projects. There are four stages involved in the construction of a viaduct; Auto launching, Segment lifting, Post tensioning and Span lowering. Strain values from the launching girder are used to predict the stages of construction using machine learning techniques. Support Vector Machines are used to classify the strain data into one of the four classes corresponding the stage of construction. Data from a typical construction cycle is used for training. Using the model generated by the training data, subsequent activities can be inferred.

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On friendly stages like Facebook, it is well known and pleasurable to share photographs among companions, yet it likewise places different members in a similar picture in peril when the photographs are delivered online without the consent from them. To tackle this issue, as of late, the analysts have planned some fine-grained admittance control systems for photographs shared on the social stage. The uploader will label every member in the photograph, then, at that point they will get inward messages and arrange their own security control procedures. These techniques ensure their protection in photographs by obscuring out the essences of members. Notwithstanding, there is still some deformity in these procedures because of the capricious labeling practices of the uploader. Noxious clients can without much of a stretch control unapproved labeling cycles and afterward distribute the photographs, which the members need them to be classified in online media.

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