TY - GEN
T1 - Segmenting and Classifying Repetitive Construction Process Time Series Using Small Amount of Labeled Data
AU - Zhang, Mingxi
AU - Vogel-Heuser, Birgit
AU - Pantforder, Dorothea
AU - Kruger, Marius
AU - Semel, Matthias
AU - Regler, Hans
AU - Vicaria, Alejandra
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85208232409&partnerID=8YFLogxK
U2 - 10.1109/CASE59546.2024.10711826
DO - 10.1109/CASE59546.2024.10711826
M3 - Conference contribution
AN - SCOPUS:85208232409
T3 - IEEE International Conference on Automation Science and Engineering
SP - 3035
EP - 3042
BT - 2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024
PB - IEEE Computer Society
T2 - 20th IEEE International Conference on Automation Science and Engineering, CASE 2024
Y2 - 28 August 2024 through 1 September 2024
ER -