@article{ART003306466},
author={Hyun Seung Son and Kwangmoon Cho},
title={Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={1},
pages={191-203}
TY - JOUR
AU - Hyun Seung Son
AU - Kwangmoon Cho
TI - Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 1
PB - The Korea Internet of Things Society
SP - 191
EP - 203
SN - 2466-0078
AB - In concrete block manufacturing, strength and water absorption tests require curing periods and destructive testing, which incur substantial time and cost and make it difficult to provide real-time quality feedback for all produced units. To address this limitation, this paper integrates process logs collected from the mixer, production-related equipment, the tilting machine, and the curing chamber at an hourly resolution, and constructs an integrated dataset by linking them with measured test results.
Using a multi-output regression framework, we compared candidate models including GBDT, RF, ET, and XGB. The results show that, for Facility A, ET achieved strong performance with an average R² of 0.8846 (average MAE of 0.1661), while for Facility B, GBDT achieved high performance with an average R² of 0.9558 (average MAE of 0.0729). Moreover, SHAP analysis confirmed that operating-condition variables such as vibration time, mortar feeding time, and temperature fluctuations were relatively more influential for Facility A, whereas input-quantity and production-state variables such as production count, hopper weighing values, and mixing count were relatively more influential for Facility B. By integrating PLC process data with quality test data and combining predictive modeling with explainable analysis, this paper provides practical evidence to support proactive quality prediction and the prioritization of process-control factors.
KW - Machine Learning;SHAP;Concrete Block;Compressive Strength Prediction;PLC data
DO -
UR -
ER -
Hyun Seung Son and Kwangmoon Cho. (2026). Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data. Journal of Internet of Things and Convergence, 12(1), 191-203.
Hyun Seung Son and Kwangmoon Cho. 2026, "Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data", Journal of Internet of Things and Convergence, vol.12, no.1 pp.191-203.
Hyun Seung Son, Kwangmoon Cho "Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data" Journal of Internet of Things and Convergence 12.1 pp.191-203 (2026) : 191.
Hyun Seung Son, Kwangmoon Cho. Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data. 2026; 12(1), 191-203.
Hyun Seung Son and Kwangmoon Cho. "Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data" Journal of Internet of Things and Convergence 12, no.1 (2026) : 191-203.
Hyun Seung Son; Kwangmoon Cho. Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data. Journal of Internet of Things and Convergence, 12(1), 191-203.
Hyun Seung Son; Kwangmoon Cho. Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data. Journal of Internet of Things and Convergence. 2026; 12(1) 191-203.
Hyun Seung Son, Kwangmoon Cho. Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data. 2026; 12(1), 191-203.
Hyun Seung Son and Kwangmoon Cho. "Machine Learning and SHAP-Based Prediction of Concrete Block Strength and Water Absorption: A Case Study using Production Process PLC Data" Journal of Internet of Things and Convergence 12, no.1 (2026) : 191-203.
