The widespread adoption of tunnel boring machines (TBMs) has led to an increased focus on disc cutter wear, including both normal and abnormal types, for efficient safe TBM excavation. However, wear yet be thoroughly investigated, primarily due the complexity considering mixed ground conditions imbalance in number instances between two types wear. This study developed a prediction model conditions, by employing interpretable machine learning with data augmentation. An equivalent elastic...

The present study compares and analyzes three risk analysis models that are applicable to shield tunnel boring machine (TBM) tunneling, thus proposes an improved matrix model based on the causal networks sustainable projects. advantages disadvantages of compared, structured by analyzing relationship between factors events. Based comparison results, network-based (CN-Matrix model), which complements exploits existing models, is proposed in this paper. Furthermore, suggests a means modifying...

Risk management plays a vital role in ensuring the safety and efficiency of tunnel construction by considering various factors, including uncertainties associated with concurrent adverse sources. One key aspect risk is prioritizing hazardous zones to devise an optimal countermeasure plan within time cost constraints. This study developed advanced model, combining analytic hierarchy process (AHP) fuzzy set theory (FST). The model derived impact using AHP probability FST. By selectively causal...

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Risk management is essential for minimizing delays and potential casualties during tunnel boring machine (TBM) tunneling. However, current methods encounter difficulties in effectively addressing uncertainties expert elicitation ensuring their interpretability. This study proposes an interpretable risk approach TBM A sophisticated framework elicitation, integrated with fuzzy set theory, introduced to handle these effectively. Impact, probability, distributions were determined, enabling a...

<title>Abstract</title> Ground settlement management is crucial in tunnel boring machine (TBM) tunneling. Previous studies on predicting ground have required substantial assumptions or information, making it challenging to explicitly determine their predictive criteria. This study developed an intelligent TBM operation support system for management, by combining learning and statistical analysis. Initially, measured settlements were categorized into three classes: heaving, normal, large...

During tunnel construction, risk assessment is essential to prevent accidents and reduce construction cost time for efficient safe excavation. However, conventional models do not consider the vagueness of human cognition or conditional probability during analysis process. This paper proposes a new model in combination analytic hierarchy process (AHP) fuzzy set theory (FST) (i.e., AHP-FST model). Specifically, FST was adopted reflect vagueness. The implementation developed includes...

Predicting anomalies ahead of a tunnel face is utmost importance to ensure construction safety and efficiency during excavation. This study aimed develop an optimization system investigate the characteristics in front face. The developed this can estimate distance between anomaly face, thickness anomaly, electrical resistivity using inverse analysis based on modified harmony search algorithm with measured resistivity. To verify efficacy system, laboratory chamber experiments were conducted...

Risk management is essential for ensuring the safety and efficiency of tunnel boring machine (TBM) tunneling. However, conventional studies may face challenges in reflecting vagueness induced by uncertainties expert surveys. Therefore, this study proposes a novel risk matrix model TBM tunneling that introduces concept class certainty. The proposed employs fuzzy set theory (FST) to determine impact probability certainties. analysis results are then integrated with weighted constructed...

In order to ensure construction efficiency and stability during tunnel excavation, it is essential predict geological risks ahead of faces. this study, a risk prediction model was developed based on machine learning (ML) algorithm. The database used implement the ML synthetically acquired from series finite-element (FE) numerical analyses, which could simulate electrical resistivity surveys excavation. FE helped obtain data representing various risky ground conditions (such as typical fault...

Predicting anomalies ahead of a tunnel face is essential to ensure construction safety and efficiency during tunneling. This study proposes an optimization system estimate the characteristics face. The developed can location, thickness electrical resistivity anomaly using inverse analysis based on modified harmony search algorithm with measurement. To verify efficacy system, laboratory chamber tests were conducted by simulating ground formations faults. experimental results indicated that...

The tunneling process of tunnel boring machines (TBMs) in urban areas was evaluated by analyzing the deformation measured at ground surface. TBM operators use excavation records to determine operational parameters and prevent undesired movement, such as collapse or heaving. A two-stage machine learning prediction framework proposed simulating feedback workflow. This is capable predicting surface deformations upcoming zones. predictive model implemented using a random forest model, which...

A novel method for identifying the cone-to-jet transition region (CTR) is proposed to characterize electrospray dynamics, and thus Taylor cone, a fundamental principle of ion extraction produce µN thrust nanosatellites’ application. numerical model developed validated against droplet’s diameter with proper characterization CTR based on hydrodynamic pressure gradient at cone-jet axis. The results show that reduced when electric potential increases flow rate decreases. Moreover, current...