Inferring Construction Activities from Structural Responses Using Support Vector Machines

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...

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Bibliographic Details
Published inISARC. Proceedings of the International Symposium on Automation and Robotics in Construction Vol. 35; pp. 1 - 8
Main Authors Harichandran, Aparna, Raphael, Benny, Varghese, Koshy
Format Conference Proceeding
LanguageEnglish
Published Waterloo IAARC Publications 01.01.2018
Subjects
Construction
Construction equipment
Construction methods
Construction sites
Data acquisition
Machine learning
Post-tensioning
Schedules
Subways
Support vector machines
Tensioning
Training
Urban rail
Viaducts
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Summary: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.