Application of hybrid machine learning based quality control in daily site management

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Standard

Application of hybrid machine learning based quality control in daily site management. / Zöhrer, Alexander; Winter, Vincent; Terbuch, Anika et al.
Proceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium : Challenges in Rock Mechanics and Rock Engineering. Salzburg, 2023. S. 569-574.

Publikationen: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Konferenzband

Harvard

Zöhrer, A, Winter, V, Terbuch, A, O'Leary, P & Khalilimotlaghkasmaei, N 2023, Application of hybrid machine learning based quality control in daily site management. in Proceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium : Challenges in Rock Mechanics and Rock Engineering. Salzburg, S. 569-574.

APA

Zöhrer, A., Winter, V., Terbuch, A., O'Leary, P., & Khalilimotlaghkasmaei, N. (2023). Application of hybrid machine learning based quality control in daily site management. In Proceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium : Challenges in Rock Mechanics and Rock Engineering (S. 569-574).

Vancouver

Zöhrer A, Winter V, Terbuch A, O'Leary P, Khalilimotlaghkasmaei N. Application of hybrid machine learning based quality control in daily site management. in Proceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium : Challenges in Rock Mechanics and Rock Engineering. Salzburg. 2023. S. 569-574

Author

Zöhrer, Alexander ; Winter, Vincent ; Terbuch, Anika et al. / Application of hybrid machine learning based quality control in daily site management. Proceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium : Challenges in Rock Mechanics and Rock Engineering. Salzburg, 2023. S. 569-574

Bibtex - Download

@inproceedings{42b5fb78ab3640d191b5ab39d5e29c36,
title = "Application of hybrid machine learning based quality control in daily site management",
abstract = "This paper presents a system that combines KPI with autoencoders to implement a hybrid machine learning system. The goal here is to investigate workflows which permit the site manager to use the hybrid machine learning systems as a decision support tool. The workflows are explained by means of case studies, demonstrating the application of the hybrid system to detect both element as well as site related quality issues. In addition to that, the detection of anomalies regarding execution efficiency assist the project manager to optimize the sequence of work on site.",
author = "Alexander Z{\"o}hrer and Vincent Winter and Anika Terbuch and Paul O'Leary and Negin Khalilimotlaghkasmaei",
year = "2023",
month = oct,
day = "9",
language = "English",
pages = "569--574",
booktitle = "Proceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Application of hybrid machine learning based quality control in daily site management

AU - Zöhrer, Alexander

AU - Winter, Vincent

AU - Terbuch, Anika

AU - O'Leary, Paul

AU - Khalilimotlaghkasmaei, Negin

PY - 2023/10/9

Y1 - 2023/10/9

N2 - This paper presents a system that combines KPI with autoencoders to implement a hybrid machine learning system. The goal here is to investigate workflows which permit the site manager to use the hybrid machine learning systems as a decision support tool. The workflows are explained by means of case studies, demonstrating the application of the hybrid system to detect both element as well as site related quality issues. In addition to that, the detection of anomalies regarding execution efficiency assist the project manager to optimize the sequence of work on site.

AB - This paper presents a system that combines KPI with autoencoders to implement a hybrid machine learning system. The goal here is to investigate workflows which permit the site manager to use the hybrid machine learning systems as a decision support tool. The workflows are explained by means of case studies, demonstrating the application of the hybrid system to detect both element as well as site related quality issues. In addition to that, the detection of anomalies regarding execution efficiency assist the project manager to optimize the sequence of work on site.

M3 - Conference contribution

SP - 569

EP - 574

BT - Proceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium

CY - Salzburg

ER -