Probabilistic assessment of tunnel construction performance based on data

Olga Špačková; Jiří Šejnoha; Daniel Straub

doi:10.1016/j.tust.2013.02.006

Probabilistic assessment of tunnel construction performance based on data

Olga Špačková, Jiří Šejnoha, Daniel Straub

Research output: Contribution to journal › Article › peer-review

51 Scopus citations

Abstract

A probabilistic model for estimating tunnel construction time is learnt with data from past tunnel projects. The model is based on the Dynamic Bayesian Network technique. The model inputs are determined through an analysis of data from three tunnels built by means of the conventional tunneling method. The data motivate the development of a novel probability distribution to describe the excavation performance. In addition, the probability of construction failure events and the delay caused by such failures are estimated using databases available in the literature. The model is applied to a case study, in which it is demonstrated how observations from the tunnel construction process can be included to continuously update the prediction of construction time.

Original language	English
Pages (from-to)	62-78
Number of pages	17
Journal	Tunnelling and Underground Space Technology
Volume	37
DOIs	https://doi.org/10.1016/j.tust.2013.02.006
State	Published - Aug 2013
Externally published	Yes

Keywords

Construction time
Dynamic Bayesian Networks
Failure rate
Tunnel construction risk
Tunneling performance

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10.1016/j.tust.2013.02.006

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@article{cbfdff8bf8d841998fb96e6d7d07f6bc,

title = "Probabilistic assessment of tunnel construction performance based on data",

abstract = "A probabilistic model for estimating tunnel construction time is learnt with data from past tunnel projects. The model is based on the Dynamic Bayesian Network technique. The model inputs are determined through an analysis of data from three tunnels built by means of the conventional tunneling method. The data motivate the development of a novel probability distribution to describe the excavation performance. In addition, the probability of construction failure events and the delay caused by such failures are estimated using databases available in the literature. The model is applied to a case study, in which it is demonstrated how observations from the tunnel construction process can be included to continuously update the prediction of construction time.",

keywords = "Construction time, Dynamic Bayesian Networks, Failure rate, Tunnel construction risk, Tunneling performance",

author = "Olga {\v S}pa{\v c}kov{\'a} and Ji{\v r}{\'i} {\v S}ejnoha and Daniel Straub",

note = "Funding Information: The first and second authors are funded by Project No. TA01030245 of the Technology Agency of the Czech Republic and Project No. 1M0579 (CIDEAS research center) of the Ministry of Education, Youth and Sports of the Czech Republic. Additional support by DAAD and Bayhost is gratefully acknowledged. ",

year = "2013",

month = aug,

doi = "10.1016/j.tust.2013.02.006",

language = "English",

volume = "37",

pages = "62--78",

journal = "Tunnelling and Underground Space Technology",

issn = "0886-7798",

publisher = "Elsevier Ltd.",

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T1 - Probabilistic assessment of tunnel construction performance based on data

AU - Špačková, Olga

AU - Šejnoha, Jiří

AU - Straub, Daniel

N1 - Funding Information: The first and second authors are funded by Project No. TA01030245 of the Technology Agency of the Czech Republic and Project No. 1M0579 (CIDEAS research center) of the Ministry of Education, Youth and Sports of the Czech Republic. Additional support by DAAD and Bayhost is gratefully acknowledged.

PY - 2013/8

Y1 - 2013/8

N2 - A probabilistic model for estimating tunnel construction time is learnt with data from past tunnel projects. The model is based on the Dynamic Bayesian Network technique. The model inputs are determined through an analysis of data from three tunnels built by means of the conventional tunneling method. The data motivate the development of a novel probability distribution to describe the excavation performance. In addition, the probability of construction failure events and the delay caused by such failures are estimated using databases available in the literature. The model is applied to a case study, in which it is demonstrated how observations from the tunnel construction process can be included to continuously update the prediction of construction time.

AB - A probabilistic model for estimating tunnel construction time is learnt with data from past tunnel projects. The model is based on the Dynamic Bayesian Network technique. The model inputs are determined through an analysis of data from three tunnels built by means of the conventional tunneling method. The data motivate the development of a novel probability distribution to describe the excavation performance. In addition, the probability of construction failure events and the delay caused by such failures are estimated using databases available in the literature. The model is applied to a case study, in which it is demonstrated how observations from the tunnel construction process can be included to continuously update the prediction of construction time.

KW - Construction time

KW - Dynamic Bayesian Networks

KW - Failure rate

KW - Tunnel construction risk

KW - Tunneling performance

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DO - 10.1016/j.tust.2013.02.006

M3 - Article

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SN - 0886-7798

VL - 37

SP - 62

EP - 78

JO - Tunnelling and Underground Space Technology

JF - Tunnelling and Underground Space Technology

ER -

Probabilistic assessment of tunnel construction performance based on data

Abstract

Keywords

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