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

Alexander Zöhrer; Vincent Winter; Anika Terbuch; Paul O'Leary; Negin Khalilimotlaghkasmaei

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

Alexander Zöhrer, Vincent Winter, Anika Terbuch, Paul O'Leary, Negin Khalilimotlaghkasmaei

Chair of Automation and Measurement (530)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the ISRM 15th International Congress on Rock Mechanics and Rock Engineering & 72nd Geomechanics Colloquium
Subtitle of host publication	Challenges in Rock Mechanics and Rock Engineering
Place of Publication	Salzburg
Pages	569-574
Number of pages	6
ISBN (Electronic)	978-3-9503898-3-8
Publication status	Published - 9 Oct 2023

15th International ISRM Congress 2023 & 72nd Geomechanics Colloquium
Anika Terbuch (Participant)
11 Oct 2023 → 13 Oct 2023
Activity: Participating in or organising an event › Participation in conference

Cite this

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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.",

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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. p. 569-574.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 -

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

Abstract

Activities

15th International ISRM Congress 2023 & 72nd Geomechanics Colloquium

Cite this