Publications / 2020 Proceedings of the 37th ISARC, Kitakyushu, Japan

A Robust Framework for Identifying Automated Construction Operations

Aparna Harichandran, Benny Raphael and Abhijit Mukherjee
Pages 473-480 (2020 Proceedings of the 37th ISARC, Kitakyushu, Japan, ISBN 978-952-94-3634-7, ISSN 2413-5844)

Machine learning techniques have been successfully implemented for the identification of various construction activities using sensor data. However, there are very few studies on activity recognition in the automated construction of low-rise residential buildings. Automated construction is faster than conventional construction, with minimal human involvement. This requires high accuracy of identification for monitoring its operations. This paper discusses the development and testing of machine learning classifiers to identify normal automated construction operations with high precision. The framework developed in this work involves decomposing the activity recognition problem into a hierarchy of learning tasks in which activities at the lower levels have more details. The top recognition level divides the equipment states into two classes: ?Idle' and ?Operations'. The second recognition level divides the ?operations' into major classes depending on the top-level activities performed by the equipment. The third recognition level further divides the activities into subclasses and so on. Since the number of classes and the similarity between them increase with the recognition level, identification becomes extremely difficult. The identification framework developed in this study classifies operations belonging to the parent class at each level in the hierarchy. The efficacy of this framework is demonstrated with a case study of a top-down modular construction system. In this construction system, the modules of a structural frame are assembled and lifted starting with the top floor followed by the ones below. The accelerometer data collected during top-down construction is used to identify the construction operations. The proposed framework shows superior performance over conventional identification using a flat list of classes.

Keywords: Automated Construction; Construction Monitoring; Machine Learning; Accelerometer