TY - GEN
T1 - Are we ready for TBM tunneling automation? Two strategies to improve model performance
AU - Mostafa, Saadeldin
AU - Sousa, Rita L.
AU - Klink, Beatriz
AU - Einstein, Herbert
N1 - Publisher Copyright:
© 2023 The Author(s).
PY - 2023
Y1 - 2023
N2 - The single most significant challenge facing the field of tunneling is not being able to know with a fair degree of certainty what lies underneath the surface. Despite the availability continuous machine performance data recorded by TBMs, and despite much previous research, real time forecasting models for ground conditions still do not exist. Without models that predict geology and the related uncertainties, automation of tunneling construction is nearly impossible, since these models are needed for real time optimization of tunneling operation. In this paper we briefly review the existing research and available predictive Machine Learning models that have been developed to forecast ground during TBM construction, and we list their main limitations. Suggestions for future research, are provided to develop more robust and generalizable geological forecasting models. Insights from a case study are used to illustrate two potential solutions to improve ML model prediction performance.
AB - The single most significant challenge facing the field of tunneling is not being able to know with a fair degree of certainty what lies underneath the surface. Despite the availability continuous machine performance data recorded by TBMs, and despite much previous research, real time forecasting models for ground conditions still do not exist. Without models that predict geology and the related uncertainties, automation of tunneling construction is nearly impossible, since these models are needed for real time optimization of tunneling operation. In this paper we briefly review the existing research and available predictive Machine Learning models that have been developed to forecast ground during TBM construction, and we list their main limitations. Suggestions for future research, are provided to develop more robust and generalizable geological forecasting models. Insights from a case study are used to illustrate two potential solutions to improve ML model prediction performance.
UR - http://www.scopus.com/inward/record.url?scp=85160316032&partnerID=8YFLogxK
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U2 - 10.1201/9781003348030-338
DO - 10.1201/9781003348030-338
M3 - Conference contribution
AN - SCOPUS:85160316032
SN - 9781003348030
T3 - Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023
SP - 2807
EP - 2812
BT - Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World- Proceedings of the ITA-AITES World Tunnel Congress, WTC 2023
A2 - Anagnostou, Georgios
A2 - Benardos, Andreas
A2 - Marinos, Vassilis P.
PB - CRC Press/Balkema
T2 - ITA-AITES World Tunnel Congress, ITA-AITES WTC 2023 and the 49th General Assembly of the International Tunnelling and Underground Association, 2023
Y2 - 12 May 2023 through 18 May 2023
ER -