Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI)

Wang, Kunyu; Zhang, Limao; Fu, Xianlei

doi:10.1016/j.autcon.2022.104730

Cited by 27 publications

(4 citation statements)

References 35 publications

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“…The trained data is initially converted to fuzzified data using the fuzzification phase; the function is expressed as eqn. (7) (7) where indicates the label's fuzzy membership values' center, left, and right spreads, respectively, and a stands for a linguistic value (e.g., medium value, high value, low value, etc.). The triangular Membership function is executed in this layer.…”

Section: Wherementioning

confidence: 99%

“…Hence, to minimize such losses, it is crucial to enhance the precise and efficient diagnosis and forecast of unfavorable geological conditions flaws, such as faults, karst caves, and groundwater penetration. This study investigates how data-driven AI approaches are used to determine the kind of geological conditions encountered, which might lessen the likelihood of jamming and geohazard [7][8]. For this, a number of the most well-liked AI methods that have been described in the literature were taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

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Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

Xuefeng Zhao

2024

jes

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When tunneling, detecting abrupt changes in geological circumstances can be difficult. In recent years, the proliferation of tunneling characteristics has been strongly related to the surrounding geology. These parameters offer significant potential for using data-driven artificial intelligence (AI) approaches to infer patterns from information without reference to known consequences. This research introduces the Simulated Fire Hawk Optimizer-based Deep Action Selection Network (SFHO-DASN) model, which uses a Simulated Fire Hawk Optimizer to anticipate the geological conditions needed for tunneling. The optimized hybrid neural network technique can anticipate geological conditions effectively, as demonstrated by a case study of the constructed model. It is especially significant for rock types, water intrusion, karst caverns, and surface subsidence, for which the predicted accuracy is greater than 95%. These findings imply that the geological circumstances behind the tunnel face could be reliably and correctly predicted by a DASN that has received the proper training. This procedure's most significant advantage is its ability to adjust every scored parameter's weighting in response to variations in geological circumstances. The accuracy performance of the proposed neural network outperforms the conventional neural network, as indicated by the area under the curve (AUC) and performance analysis. A proposed model for geology prediction can attain predictive accuracy with a small number of tunneling parameters, according to an analysis of the feature relevance of each tunneling parameter. The suggested approach ought to be more practical for proposals about tunnel support architecture in the East Asian geological region and for future tunnel building.

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Section: Wherementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

Xuefeng Zhao

2024

jes

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“…At present, artificial intelligence is being gradually applied to the construction of TBM tunnel [22][23][24]. Case-based reasoning (CBR) is an intelligent method emerging in the field of artificial intelligence in recent years.…”

Section: The Introductionmentioning

confidence: 99%

Development and Application of Adaptive Evaluation System for TBM Tunneling Based on Case-Based Reasoning

et al. 2023

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The excavation adaptability evaluation decision process for the TBM (tunnel boring machine) in long and large tunnels under complex geological conditions is an uncertain and fuzzy problem affected by multiple factors. Aiming to shift the status quo of TBMs tunneling adaptability evaluation and the ineffective use of related accident cases, the TBM tunneling adaptability evaluation method based on case-based reasoning is proposed and researched. The case-based reasoning method can use existing engineering experience and knowledge to answer newly encountered problems, and can accurately and efficiently evaluate the adaptability of TBM tunneling. Based on the nearest neighbor method, this paper establishes the calculation formula of TBM tunneling adaptability similarity. Based on the statistical analysis method, the evaluation indicators that play a relatively important role in the system are selected, and the TBM tunneling adaptability evaluation index system is constructed. The analytic hierarchy process (AHP) is used to determine the weight of the evaluation indicators at each level. According to the characteristics of TBM tunneling adaptability evaluation, the overall design of the case-based reasoning-based TBM tunneling adaptability evaluation decision system CBR-TBMEAEDS (case-based reasoning-TBM excavation adaptive evaluation decision system) is proposed, and the TBM tunneling adaptability evaluation case is expressed The case-based reasoning method and modification method were designed, and CBR-TBMEAEDS was developed. The system can be used to evaluate the adaptability of TBM to the constructed case library, and the evaluation results are consistent with the actual situation.

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“…Salah satu metode umum yang sering digunakan dalam pelaksanaan proyek konstruksi yaitu Metode Tunnel Boring Machine (TBM). Metode ini menggunakan mesin bor otomatis yang disebut TBM untuk menggali terowongan dengan presisi dan kecepatan tinggi (Wang et al, 2023). Metode ini umumnya digunakan dalam pembangunan terowongan bawah tanah seperti pada proyek-proyek Mass Rapid Transit (MRT) atau proyek infrastruktur lainnya, TBM yang di gunakan salah satunya pada proyek pembangunan MRT Jakarta.…”

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Efektivitas Tunnel Borring Machine (Tbm) Tipe Earth Pressure Balance (Epb) Dalam Pelaksanaan Proyek MRT Jakarta Fase 2a Cp 201 Monas-Harmnoni

Nanda,

Priyono,

Irfani

2023

JKTS

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Metode Tunnel Boring Machine (TBM) tipe Earth Pressure Balance (EPB) adalah metode yang efisien dan efektif dalam penggalian terowongan. Metode ini menawarkan kecepatan, efisiensi, presisi, dan akurasi yang tinggi dalam proyek-proyek terowongan. Dalam metode TBM EPB, pemasangan ring segmen dilakukan dengan rata-rata 5 ring per hari, memungkinkan pengembangan terowongan secara cepat dan efisien. Diameter mesin TBM yang besar dan desain segmen yang kuat memungkinkan penggalian terowongan yang lebih lebar dan lebih cepat daripada metode konvensional. Fitur Earth Pressure Balance (EPB) pada TBM memungkinkan pengendalian tekanan tanah dengan efektif, mencegah kegagalan struktur tanah di sekitarnya. Metode TBM EPB juga menekankan pada presisi dan akurasi. Pengendalian tekanan tanah yang efektif dan desain segmen yang tepat menjaga stabilitas dan keakuratan dimensi terowongan. Dalam hal pengurangan dampak lingkungan, metode ini mengelola limbah dengan baik, mengendalikan debu, dan mengoptimalkan penggunaan air. Keselamatan pekerja juga menjadi prioritas dengan perlindungan yang sesuai dan pemeliharaan rutin pada mesin TBM. Dalam penanganan tanah dan air, metode EPB memastikan aliran tanah yang digali dikelola dengan hati-hati untuk menghindari kebocoran air dan keruntuhan. Kolaborasi antara ahli geologi, insinyur, dan tim konstruksi menjadi kunci keberhasilan proyek dengan metode TBM EPB

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Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI)

Cited by 27 publications

References 35 publications

Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

Development and Application of Adaptive Evaluation System for TBM Tunneling Based on Case-Based Reasoning

Efektivitas Tunnel Borring Machine (Tbm) Tipe Earth Pressure Balance (Epb) Dalam Pelaksanaan Proyek MRT Jakarta Fase 2a Cp 201 Monas-Harmnoni

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