Performance prediction of tunnel boring machine through developing high accuracy equations: A case study in adverse geological condition

Samaei, Masoud; Ranjbarnia, Masoud; Nourani, Vahid; Naghadehi, Masoud Zare

doi:10.1016/j.measurement.2019.107244

Cited by 24 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the essential tasks in the excavation of tunnels with TBMs is the reliable estimation of the performance needed for planning, cost control and other decisionmaking regarding the feasibility of tunnelling projects [2]. However, according to results of the extensive review on the literature performed by Samaei et al [49], there is no comprehensive agreement on the quantitative or qualitative influence of various variables on the TBM performance assessment, but the degree of accuracy in its prediction has been improved in recent years through using various algorithms such as ANN, support vector machine, fuzzy and neuro-fuzzy. For this reason, reliable prediction models for ROP have been attractive subjects for tunnel engineers.…”

Section: Resultsmentioning

confidence: 99%

“…A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunnelling projects [68,69]. With respect to TBM drilling in hard rocks, the most important aspect of its operation is the prediction of its rate of penetration (ROP) [49]. However, predicting the tunnel construction duration for long tunnels in complex geological and geotechnical conditions is not an easy task because of high uncertainty.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Gökçeoğlu

2021

SN Appl. Sci.

View full text Add to dashboard Cite

One of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, α angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Gökçeoğlu

2021

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Farrokh et al (2012Farrokh et al ( , 2020, Hassanpour et al (2011), andMichael et al (2000), established correlation models between different rock mass parameters, such as uniaxial compression strength and joint spacing, and tunneling parameters using a large number of engineering data. Samaei et al (2020), Koopialipoor et al (2020), ), Feng et al (2021 2021) developed a step-by-step intelligent decision support system, prioritize the tunneling rate and work safety pressure to obtain the optimal score of TBM operation performance. Guo et al (2022) proposed a TBM assisted driving method that can automatically determine the optimal tunneling parameters of TBM under different rock grades and excavatability grades based on the classification of surrounding rock grades based on random forests.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based operating parameter decision-making method for optimal penetration rate

Zhu

2024

Preprint

View full text Add to dashboard Cite

TBM has become one of the most important equipment for underground excavation due to its effectiveness on both time and economic. However, the efficiency and safety of TBM excavation is highly dependent on the driver's experience. This study proposes a novel deep learning-based intelligent decision-making model for TBM operating parameters. The model consists of a deep learning based TBM operating parameters and TBM performance mapping algorithm and operating parameters decision-making method. The proposed model takes the historical mechanical data of the TBM as input and can suggest optimal operating parameters for TBM excavation in real time. The mapping algorithm can predict the performance of the TBM at any given operating parameter with an average percentage error accuracy of 4.06% for PR and 4.65% for torque. The results show that the proposed decision-making method for operating parameters can increase the PR for about 10% in high excavatability regions and increase about 3% PR in low excavatability regions while reduce energy cost and presumably reduce cutter weariness. This study also analyzes the logic behind the selection of operating parameters by the TBM driver and why the proposed method can achieve better TBM performance than manually driven TBMs.

show abstract

“…Tunnel boring machines (TBMs) have been widely used in urban tunnel projects for roads, subways, and underground pipe corridors due to their high efficiency 1 – 3 . However, the complex and varied geological conditions could directly affect the tunneling efficiency of TBMs.…”

Section: Introductionmentioning

confidence: 99%

A case study on TBM cutterhead temperature monitoring and mud cake formation discrimination method

Xia

Lian

et al. 2021

Sci Rep

View full text Add to dashboard Cite

The mud cake is easily formed during the tunnel boring machine (TBM) excavation in clay soils or rocks containing clay minerals. Mud cake will lead to soil disturbance of tunnel face, clogging cutterhead and even affect the construction efficiency and personnel safety. In this study, a discrimination method of mud cake formation based on cutterhead temperature was proposed. An online monitoring system was designed and installed on the slurry balance TBM. The results show that: (a) the cutterhead temperature data can be reliably detected and transmitted by the system; (b) in a tunneling cycle, the temperature at some positions of the cutterhead will increase first and then decrease; (c) during the field test, the temperature variation is around 2.5 °C under the normal condition, but the temperature variation will increase more than 50 °C due to the mud cake or geological change; (d) compared with the cooling rate, mud cake formation can be accurately discriminated.

show abstract

Performance prediction of tunnel boring machine through developing high accuracy equations: A case study in adverse geological condition

Cited by 24 publications

References 37 publications

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Deep learning based operating parameter decision-making method for optimal penetration rate

A case study on TBM cutterhead temperature monitoring and mud cake formation discrimination method

Contact Info

Product

Resources

About