Modelling TBM performance with artificial neural networks

Benardos, Andreas; Kaliampakos, Dimitris

doi:10.1016/j.tust.2004.02.128

Cited by 209 publications

(61 citation statements)

References 8 publications

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“…ANN approach has been used widely in geotechnical and geomechanical engineering problems [30][31][32][33][34][35][36][37][38][39][40][41][42]. Due to above mentioned defects of numerical, experimental and analytical methods in design of segmental tunnel lining, application of ANN methods seems to be a new alternative solution as prediction tools.…”

Section: Introductionmentioning

confidence: 99%

Stress Analysis of Segmental Tunnel Lining Using Artificial Neural Network

Rastbood¹,

Gholipour²,

Majdi³

2017

Period. Polytech. Civil Eng.

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Section: Introductionmentioning

confidence: 99%

Stress Analysis of Segmental Tunnel Lining Using Artificial Neural Network

Rastbood¹,

Gholipour²,

Majdi³

2017

Period. Polytech. Civil Eng.

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“…Risks resulting from construction failures are commonly analyzed separately using techniques such as fault tree or event tree analysis, decision trees or risk matrices (Benardos and Kaliampakos, 2004;Shahriar et al, 2008;Hong et al, 2009;Aliahmadi et al, 2011;Jurado et al, 2012). In Špačková (2012), tunnel construction failures are modeled by means of a Poisson process.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of tunnel construction performance based on data

Špačková

Šejnoha

Straub

2013

Tunnelling and Underground Space Technology

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A probabilistic model for estimating tunnel excavation 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 excavation time.

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“…Other applications of ANNs in geotechnical engineering include earth retaining structures [56], dams [57,58], blasting [59], mining [60], environmental geotechnics [61], rock mechanics [62][63][64][65][66][67], site characterization [68], tunnels and underground openings [69][70][71][72][73][74], slope stability and landslides [71,[75][76][77][78][79], and deep excavation [80].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Shahin

Jaksa

Maier

2009

Advances in Artificial Neural Systems

118

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Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

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Modelling TBM performance with artificial neural networks

Cited by 209 publications

References 8 publications

Stress Analysis of Segmental Tunnel Lining Using Artificial Neural Network

Stress Analysis of Segmental Tunnel Lining Using Artificial Neural Network

Probabilistic assessment of tunnel construction performance based on data

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

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