Estimation of tunnelling-induced settlement by modern intelligent methods

Ahangari, Kaveh; Moeinossadat, Sayed Rahim; Behnia, Danial

doi:10.1016/j.sandf.2015.06.006

Cited by 93 publications

(30 citation statements)

References 22 publications

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“…These linguistic terms can be organized by Gaussian membership function. The below formulation expresses the Gaussian function (Ahangari, Moeinossadat, & Behnia, 2015):…”

Section: Overview Of Anfis Strategymentioning

confidence: 99%

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Razavi

Sabaghmoghadam

Bemani

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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An extensive variety of chemical engineering processes include the transfer of heat energy. Since increasing the effective contact surface is known as one of the popular manners to improve the efficiency of heat transfer, the attention to the nanofluids has been attracted. Due to the difficulty and high cost of an experimental study, researchers have been attracted to fast computational methods. In this work, Adaptive neuro-fuzzy inference system and least square support vector machine algorithms have been applied as a comprehensive predictive tool to forecast the nanofluids thermal conductivity in terms of diameter, temperature, the thermal conductivity of the base fluid, the thermal conductivity of nanoparticle and volume fraction. To this end, a large and comprehensive experimental databank contains 1109 data points have been collected from reliable sources. The particle swarm optimization is utilized to reach the best structures of the proposed algorithms. A comprehensive statistical and graphical investigations are carried out to prove the accuracy and ability of proposed models. In addition, the comparisons outputs indicate that the least square support vector machine algorithm has the best performance among the existing correlations and Adaptive neuro-fuzzy inference system algorithms for forecasting thermal conductivity of different nanofluids.

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“…These linguistic terms can be organized by Gaussian membership function. The below formulation expresses the Gaussian function (Ahangari, Moeinossadat, & Behnia, 2015):…”

Section: Overview Of Anfis Strategymentioning

confidence: 99%

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Razavi

Sabaghmoghadam

Bemani

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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“…Unlike GP and GA techniques that have been widely applied in the field of rock mechanics and mining engineering (Baykasoglu et al 2008;Ozbek et al 2013;Güllü 2012;Ahangari et al 2015;Dindarloo 2015a), GEP is not yet a well-established technique in the mentioned fields. GEP is the developed version of GP and GA and can surmount their shortcomings such as difficulties of applying genetic operators on trees (Ferreira 2001;Baykasoglu et al 2008;Teodorescu and Sherwood 2008).…”

Section: Gene Expression Programmingmentioning

confidence: 99%

“…Their study represented a good agreement between the measured UCS and predicted by GEP model. Ahangari et al (2015) proposed two models, i.e., GEP and ANFIS to estimate settlement induced by tunneling and indicated the superiority of their developed GEP model compared to ANFIS predictive model in settlement prediction. More specifically, in the field of ground vibration prediction, a GEP technique was employed and proposed for prediction of the frequency of the adjacent ground vibrations in the study conducted by Dindarloo (2015a).…”

Section: Introductionmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

145

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Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

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“…Suwansawat et al [60] evaluated the potential and the limitations of artificial neural networks (ANN) for predicting surface settlements caused by Earth pressure balance (EPB) shield tunneling and to develop optimal neural network models for this objective. Ahangari et al [61] created a database from previous research [62][63][64][65] and studied the capability of adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) methods for settlement prediction. However, those methods belong to the fuzzy solutions without mechanism analysis, training neural networks requires a lot of data from databases or numerical simulation, and there are problems such as difficult convergence or slow convergence speed.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Predicting the Greenfield Stratum Movements Caused by Shield Tunnel Construction

Tan

Zhang

et al. 2019

Applied Sciences

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Shield tunneling is becoming the preferred construction scheme for metro construction because of its advantages of fast construction speed and small disturbance. However, limited by process defects, the stratum movements induced by the construction of shield tunnels still affects the safety of nearby underground structures and aboveground buildings. Therefore, the reliable prediction of stratum movements is important. Described in this paper is an analysis method of the Greenfield stratum movements (Greenfield is an area of land that has not yet had buildings on it, stratum movements means the movement of various soil layers) caused by shield tunnel construction combining an elastic half-space model of mirror source-sink method with the use of modified analytical method. Based on the theoretical formula in this paper, not only can the curve of surface settlement trough be calculated, but also the three-dimensional displacement field of deep soil can be obtained. By comparing vertical and horizontal contour maps of Greenfield stratum movements, good consistency between theoretical formula results and centrifugal test results are shown. This solves the defects and limitations of existing two-dimensional formulas; furthermore, based on this, it is convenient to evaluate the effect on the other skewed underground structures through the elastic foundation beam and other similar methods; therefore, this paper can provide a wide guidance and service for the design and construction of underground engineering in the future.

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Estimation of tunnelling-induced settlement by modern intelligent methods

Cited by 93 publications

References 22 publications

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

An Improved Method for Predicting the Greenfield Stratum Movements Caused by Shield Tunnel Construction

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