Application of deep neural networks in predicting the penetration rate of tunnel boring machines

Koopialipoor, Mohammadreza; Tootoonchi, Hossein; Armaghani, Danial Jahed; Mohamad, Edy Tonnizam; Hedayat, Ahmadreza

doi:10.1007/s10064-019-01538-7

Cited by 120 publications

(24 citation statements)

References 40 publications

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“…As widely suggested in the literature by various researchers, [33,[55][56][57][58], 80% of data was allocated to training and the remaining part was allocated to testing. A variety of algorithms have been introduced in the literature for the purpose of training ANN, among which a key one is the Levenberg-Marquardt (LM) method [100][101][102][103][104]. It can be effectively applied to problems that may arise in the civil engineering and mining contexts [60][61][62].…”

Section: Initial Modelmentioning

confidence: 99%

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

et al. 2020

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Seepage is one of the most challenging issues in some procedures such as design, construction, and operation of embankment or earth fill dams. The purpose of this research is to develop a new solution based on governing equations to solve the seepage problem in an effective way. Therefore, by implementing the equations in the programming environment, more than 24,000 models were designed to be applicable to different conditions. Input data included different parameters such as slopes in upstream and downstream, embankment width, soil permeability coefficient, height, and freeboard. With the use of this big data, a new process was developed to provide simple mathematical models for the seepage rate analysis. The study first used intelligent models to simulate the seepage behavior. Finally, the accuracy of the models was optimized using a new metaheuristic algorithm. This led to the ultimate flexibility of the final model presented as a new solution capable of evaluating different conditions. Finally, using the best model, new mathematical relationships were developed based on this methodology. This new solution can be used as a proper alternative to the governing equations of seepage rate estimation. Another advantage of the proposed model is its high flexibility that can be well applied to engineering design in this field, which was not possible using the initial equations.

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Section: Initial Modelmentioning

confidence: 99%

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

et al. 2020

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“…The application of artificial neural networks to predict tunnel boring machine performance is for example an emerging research topic; see e.g. the articles by Koopialipoor et al [13,14].…”

Section: Development Of Risk Management Procedures For Rock Engineeringmentioning

confidence: 99%

Principles of structured risk management in rock engineering

Spross

2020

RakMek

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This article, based on a keynote lecture given at the Finnish Rock Mechanics Day 2019, discusses how structured risk management can be implemented to rock engineering projects. The suggested procedure is based on ISO 31000 and a recently published methodology for practical implementation of the standard to geotechnical engineering projects. The main message is that structured risk management is a key tool to achieve high-quality rock engineering structures. A key component for many projects will be the use of the observational method to cost-effectively reduce the lack of knowledge of the ground conditions during construction of the facility.

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“…For the structural design and vibration reduction optimization of the cutterhead system. Liu, 14 Hasanpour et al, 15,16 and Afrasiabi et al 17–23 were devoted to studying various performance parameters that affect the work of TBM, such as rock parameters and machine parameters to establish prediction theoretical models of TBM performance. Methods such as “penalty factor” and “pso-ann hybrid model” were proposed to estimate the performance of TBM, which enriched the theoretical model of intelligent prediction.…”

Section: Introductionmentioning

confidence: 99%

Research on rock breaking mechanism and load characteristics of TBM cutter based on discrete element method

Ling

Cheng

Tong

et al. 2021

Advances in Mechanical Engineering

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Tunnel boring machine (TBM) is a large-scale tunnel engineering equipment, which has unparalleled advantages in safety and work efficiency. The cutter is subjected to complex and variable random impact loads, resulting in damage to bearings, cutter rings, and cutter shafts. Therefore, based on the discrete element simulation platform and experiment, this paper established the cutter rock simulation model based on the experimental data, and analyzed the rock breaking process, cutter load magnitude, and variation law. The research results show that when the cutter pole diameter decreases from 200 mm, 100 mm, and 0 mm in sequence, the rolling force increases accordingly, and the maximum increase of the rolling force is 56%. Meanwhile the lateral force decreases and the maximum average decrease value is 26%. Moreover, the rolling force showed a downward trend with the increase of the cutter spacing. When the cutter spacing is increased from 80 mm to 120 mm, the average rolling force decreases by 51%, and the maximum value rises slightly, increasing by 2%. And, the lateral force tends to increase, with an increase of 17% and the maximum lateral force value increase was 29%. The results can provide load input reference for TBM cutter layout, cutterhead structure design, and performance evaluation.

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Application of deep neural networks in predicting the penetration rate of tunnel boring machines

Cited by 120 publications

References 40 publications

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

Principles of structured risk management in rock engineering

Research on rock breaking mechanism and load characteristics of TBM cutter based on discrete element method

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