Segmenting and Classifying Repetitive Construction Process Time Series Using Small Amount of Labeled Data

Mingxi Zhang, Birgit Vogel-Heuser, Dorothea Pantforder, Marius Kruger, Matthias Semel, Hans Regler, Alejandra Vicaria

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Repetitive construction processes, as an essential element of construction industry, still rely intensively on manual execution and on-site decision-making. Within the proposal for integrating Cyber-Physical-System (CPS) in construction, time series analysis of sensor data has great potential to enhance construction project efficiency and support decision-making. However, owing to variable boundary conditions among construction projects, acquiring segmented and labeled training data for time series analysis models requires extensive human effort at the early stages of each construction project, with limited data reusability. We propose a Dynamic Time Warping-based (DTW) ensemble model for segmenting and assigning labels, which are predefined by experts as reference labels, for repetitive construction process through classification, requiring only small amount of labeled training data. The model is validated through a case study involving the Kelly Drilling process in two construction projects, achieving an average accuracy close to 90%. Minor errors occur only at subprocess transition points, in accordance with the error pattern in manual segmentation and labeling efforts. The proposed model addresses the challenge of the large human effort in acquisition of sufficient labeled segmented data in CPS in context construction under flexibility requirements.

Original languageEnglish
Title of host publication2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024
PublisherIEEE Computer Society
Pages3035-3042
Number of pages8
ISBN (Electronic)9798350358513
DOIs
StatePublished - 2024
Event20th IEEE International Conference on Automation Science and Engineering, CASE 2024 - Bari, Italy
Duration: 28 Aug 20241 Sep 2024

Publication series

NameIEEE International Conference on Automation Science and Engineering
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference20th IEEE International Conference on Automation Science and Engineering, CASE 2024
Country/TerritoryItaly
CityBari
Period28/08/241/09/24

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