Use of soft computing techniques for tunneling optimization of tunnel boring machines

Shahrour, Isam; Zhang, Wengang

doi:10.1016/j.undsp.2019.12.001

Cited by 57 publications

(9 citation statements)

References 50 publications

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“…. Isam and Wengang [5] investigated the applications of soft computing techniques in TBM performance prediction, and concluded that soft computing methods shows good performance in dealing with complex relationships among TBM parameters.…”

Section: Literature Review a Data Analysis In Tbm And Tunnel Engmentioning

confidence: 99%

“…As megaprojects with enormous uncertainties, tunnel construction needs to record data from every aspect and subsystem to reduce risk. The number of monitoring parameters can be more than one hundred, and the relationships among them are difficult to understand and model manually [5]. When critical parameters need to be predicted and analyzed, it is difficult to determine all related variables from the set of massive parameters.…”

Section: Introductionmentioning

confidence: 99%

“…(4) Improvements have been made to existed DM algorithms based on TBM characteristics to improve performance. (5) The three DM algorithms are not only designed to solve single problems, they are also combined to form a hybrid DM framework to improve data mining process. The extracted association rules are used for parameter selection in subsequent algorithms, and the classified formation is applied to improve data quality in rate of penetration (ROP) prediction.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Data Mining Method for Tunnel Engineering Based on Real-Time Monitoring Data From Tunnel Boring Machines

Leng

Lin

et al. 2020

IEEE Access

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Tunnel engineering is one of the typical megaprojects given its long construction period, high construction costs and potential risks. Tunnel boring machines (TBMs) are widely used in tunnel engineering to improve work efficiency and safety. During the tunneling process, large amount of monitoring data has been recorded by TBMs to ensure construction safety. Analysis of the massive real-time monitoring data still lacks sufficiently effective methods and needs to be done manually in many cases, which brings potential dangers to construction safety. This paper proposes a hybrid data mining (DM) approach to process the realtime monitoring data from TBM automatically. Three different DM techniques are combined to improve mining process and support safety management process. In order to provide people with the experience required for on-site abnormal judgement, association rule algorithm is carried out to extract relationships among TBM parameters. To supplement the formation information required for construction decisionmaking process, a decision tree model is developed to classify formation data. Finally, the rate of penetration (ROP) is evaluated by neural network models to find abnormal data and give early warning. The proposed method was applied to a tunnel project in China and the application results verified that the method provided an accurate and efficient way to analyze real-time TBM monitoring data for safety management during TBM construction. INDEX TERMS Data mining, monitoring data, tunnel boring machine (TBM), tunnel construction, underground structure.

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Section: Literature Review a Data Analysis In Tbm And Tunnel Engmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Data Mining Method for Tunnel Engineering Based on Real-Time Monitoring Data From Tunnel Boring Machines

Leng

Lin

et al. 2020

IEEE Access

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“…Recently, machine learning has emerged as a promising technique for predictive assessment in geotechnical engineering, in general [25][26][27][28][29][30][31], and in tunnelling, in particular [32][33][34][35][36][37][38]. Some promising research domains for machine learning in tunnelling are the geological prognosis ahead of the face, the interpretation of monitoring results, automation and maintenance [32].…”

Section: Introductionmentioning

confidence: 99%

“…Some promising research domains for machine learning in tunnelling are the geological prognosis ahead of the face, the interpretation of monitoring results, automation and maintenance [32]. At present, however, research appears to be focussed on the following topics: prediction of TBM operational parameters [34,[39][40][41][42][43][44][45][46], penetration rate [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], porewater pressure [64], ground settlement [65][66][67], disc cutter replacement [68][69][70], jamming risk [71,72] and geological classification [73][74][75][76]). Few authors estimated the face support pressure of TBMs with machine learning [35,52].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the face support pressure of tunnel boring machines

Soranzo¹,

Guardiani²,

Wu³

2023

Preprint

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In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.

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Development of urban underground space in coastal cities in China: A review

Peng

Liu

Wang

et al. 2023

Deep Underground Science and Engineering

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The development of urban underground space is a critical way to alleviate the lack of urban resources, and improve environmental conditions as well as residents' quality of life, which has important practical significance. As the most densely populated and economically developed areas, coastal cities face many challenges in underground space development owing to their unique environmental geological conditions. This study examined the current situation of underground space development, academic research, technology application, evaluation methods, planning, and legislation in coastal cities in China. On this basis, it analyzed the future development trends, and presented reasonable suggestions for the critical scientific and technical problems faced by the development of underground space in coastal cities. The key suggestions are as follows: it is necessary to define and clarify the critical geological issues, distributions, and hazards that affect underground space development and major engineering construction in coastal cities; the land‐sea integrated detection technology for sea crossing bridges and submarine tunnels needs to be studied; in evaluating the suitability of underground space in coastal cities, the land and sea area evaluation factors should be integrated; and the evaluation system of land‐sea integration needs to be established.

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Use of soft computing techniques for tunneling optimization of tunnel boring machines

Cited by 57 publications

References 50 publications

A Hybrid Data Mining Method for Tunnel Engineering Based on Real-Time Monitoring Data From Tunnel Boring Machines

A Hybrid Data Mining Method for Tunnel Engineering Based on Real-Time Monitoring Data From Tunnel Boring Machines

Reinforcement learning for the face support pressure of tunnel boring machines

Development of urban underground space in coastal cities in China: A review

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