Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method

Bouayad, Djamila; Emeriault, Fabrice

doi:10.1016/j.tust.2017.03.011

Cited by 93 publications

(32 citation statements)

References 14 publications

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“…The original monitored TD and TV data storing in Table 3 and Table 4 in the supplement DatasetForTraining.xlsx file consist of several orientational values. The use of PCA is to reduce the number of interrelated variables [ 2 , 3 ].…”

Section: Data Descriptionmentioning

confidence: 99%

Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements

et al. 2020

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The dataset presented in this article pertains to records of shield tunneling-induced ground settlements in Guangzhou Metro Line No. 9. Field monitoring results obtained from both the two tunnel lines are put on display. In total, 17 principal variables affecting ground settlements are tabulated, which can be divided into two categories: geological condition parameters and shield operation parameters. Shield operation parameters are specifically provided in time series. Another value of the dataset is the consideration of karst encountered in the shield tunnel area including the karst cave height, the distance between karst cave and tunnel invert, and the karst cave treatment scheme. The dataset can be used to enrich the database of settlement caused by shield tunneling as well as to train artificial intelligence-based ground settlement prediction models. The dataset presented herein were used for the article titled “Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements” (Zhang et al., 2020).

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Section: Data Descriptionmentioning

confidence: 99%

Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements

et al. 2020

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“…PCA provides a few linear combinations of the variables that can be utilized to summarize the data without losing much information during the analysis. For more information about the structure and implementation of PCA, some other references [39,52] can be considered.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…AI models have been widely applied by many researchers in several tunneling projects [27][28][29][30][31][32]. Typical AI models include artificial neural network (ANN) [33][34][35], fuzzy logic (FL) model [36], Genetic algorithm (GA) [37,38], and adaptive neuro-fuzzy inference system (ANFIS) [39,40]. Minh et al [41] developed the fuzzy logic model as an alternative method that was more accurate in comparison with four statistical regression models to predict the TBM performance.…”

Section: Introductionmentioning

confidence: 99%

Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

et al. 2019

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The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

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“…AI-based models emerged two decades ago to serve as an acceptable solution to several geotechnical problems; many comprehensive reviews have summarised the effectiveness of using AI models in widespread applications. To estimate the TBM performance, some AI models have been developed, including artificial neural networks (ANN) [18]- [20], fuzzy logic (FL) [21], and adaptive neurofuzzy inference systems (ANFIS) [22], [37]. ANN and non-linear multiple regression models have been used for estimating tunnel boring machine performance as a function of rock properties.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Shield Performance During Tunneling via Incorporating Improved Particle Swarm Optimization Into ANFIS

et al. 2020

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This paper proposes a new computational model to predict the earth pressure balance (EPB) shield performance during tunnelling. The proposed model integrates an improved particle swarm optimization (PSO) with adaptive neurofuzzy inference system (ANFIS) based on the fuzzy C-mean (FCM) clustering method. In particular, the proposed model uses shield operational parameters as inputs and computes the advance rate as the output. Prior to modeling, critical operational parameters are identified through principle component analysis (PCA). The hybrid model is applied to the prediction of the shield performance in the tunnel section of Guangzhou Metro Line 9 in China. The prediction results indicate that the improved PSO-ANFIS model shows high accuracy in predicting the EPB shield performance in terms of the multiobjective fitness function [i.e. root mean square error (RMSE) = 0.07, coefficient of determination (R 2) = 0.88, variance account (VA) = 0.84 for testing datasets, respectively]. The good agreement between the actual measurements and predicted values demonstrates that the proposed model is promising for predicting the EPB shield tunnel performance with good accuracy.

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Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method

Cited by 93 publications

References 14 publications

Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements

Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements

Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

Prediction Model of Shield Performance During Tunneling via Incorporating Improved Particle Swarm Optimization Into ANFIS

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