Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

doi:10.1515/eng-2017-0012

Cited by 38 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhou et al [68] applied six machine learning methods for the prediction of ROP and found that the comprehensive performance of the particle swarm optimizationextreme gradient boosting hybrid model-is superior to the other five models. Minh et al [42] used uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW) and the alpha angle (α) between the tunnel axis and the planes of weakness for predicting ROP. Minh et al [42] suggested that the fuzzy logic as well as other artificial intelligences can also be used as very good alternatives to predict ROP.…”

Section: Introductionmentioning

confidence: 99%

“…Minh et al [42] used uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW) and the alpha angle (α) between the tunnel axis and the planes of weakness for predicting ROP. Minh et al [42] suggested that the fuzzy logic as well as other artificial intelligences can also be used as very good alternatives to predict ROP. Jung et al [28] predicted the ground conditions ahead of the tunnel face regardless of site conditions considering the operational data of the shield TBM acquired during the tunnel excavation stage.…”

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: Introductionmentioning

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

“…Several machine learning models have been used to predict AR by utilizing great amount of recorded data by TBM during tunnelling, including artificial neural networks (ANN) [11,12], support vector regression (SVR) [6,13], random forest (RF) [14,15], fuzzy logic [16,17], and classification and regression tress [18,19]. Among them, two models, SVR and RF, became the most widely adopted for AR prediction due to their robustness and high accuracy.…”

Section: Introductionmentioning

confidence: 99%

EPBM Advance Rate Prediction Using Hybrid Feature Selection and Support Vector Regression Modeling

Huang¹,

Esmaeilpour²,

Dastpak³

et al. 2023

Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022)

View full text Add to dashboard Cite

Advance rate (AR) prediction is crucial for optimal mechanized tunneling performance. However, the type of input features used when developing AR prediction models vary greatly from study to study. In this paper, a hybrid automatic feature selection method is presented and demonstrated through the development of a support vector regression (SVR) model for AR prediction in Earth pressure balance machine (EPBM) tunnel construction. EPBM datasets are collected from a tunnel project in the city of Porto, Portugal. Irrelevant features whose values are constant for most of the time were first removed via constant and quasi-constant detection method (CQD). Sequential forward selection (SFS) was then performed to determine the best subset of features to develop the best performed model. The results showed that the SVR model successfully predicted AR using the selected features with squared correlation coefficient (R 2 ) of 0.919 and 0.884 for training and testing, respectively. The efficiency of the feature selection method is demonstrated by comparing the results of the SVR model with feature selection and the one without. It is proved that proposed method helps improve the accuracy of the predictions by 8% and 17% for training and testing, respectively.

show abstract

“…In predicting FPI values, Feng et al [27] and Adoko and Yagiz [28] tried to solve this problem by proposing deep learning and FIS techniques, respectively. A group of other authors tried to solve performance of TBM using other single intelligent approaches like group modeling of data handling, genetic-based and neuro-fuzzy [2,9,[29][30][31]. On the other hand, some other scholars developed advanced intelligent techniques for this problem which are based on a combination of at least two different intelligent models [1,10,14,32,33].…”

Section: Introductionmentioning

confidence: 99%

A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance

et al. 2022

View full text Add to dashboard Cite

Numerous studies have reported the effective use of artificial intelligence approaches, particularly artificial neural networks (ANNs)-based models, to tackle tunnelling issues. However, having a high number of model inputs increases the running time and related mistakes of ANNs. The principal component analysis (PCA) approach was used in this work to select input factors for predicting tunnel boring machine (TBM) performance, specifically advance rate (AR). A reliable and precise forecast of TBM AR is desirable and critical for mitigating risk throughout the tunnel building phase. The developed PCAs (a total of four PCAs) were used with the artificial bee colony (ABC) method to predict TBM AR. To assess the created PCA-ANN-ABC model’s capabilities, an imperialist competitive algorithm-ANN and regression-based methods for estimating TBM AR were also suggested. To evaluate the artificial intelligence and statistical models, many statistical evaluation metrics were evaluated and generated, including the coefficient of determination (R2). The findings indicate that the PCA-ANN-ABC model (with R2 values of 0.9641 for training and 0.9558 for testing) is capable of predicting AR values with a high degree of accuracy, precision, and flexibility. The modelling approach utilized in this study may be used to other comparable studies involving the solution of engineering challenges.

show abstract

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Cited by 38 publications

References 9 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

EPBM Advance Rate Prediction Using Hybrid Feature Selection and Support Vector Regression Modeling

A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance

Contact Info

Product

Resources

About