专题：Tunneling and Rock Mechanics

This cluster of papers focuses on the prediction of Tunnel Boring Machine (TBM) performance in hard rock conditions, utilizing rock mass properties, rock fragmentation, and machine learning techniques. It also includes research on risk assessment, numerical simulation, and the influence of geological conditions on TBM performance.
最新文献
Failure behavior of jointed rock masses containing a circular hole under compressive-shear load: Insights from DIC technique

article Full Text OpenAlex

Hybrid metaheuristic optimized Catboost models for construction cost estimation of concrete solid slabs

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Study on the mechanism and preliminary application of efficient directional rock breaking using a coal-based solid waste non-explosive expansive agent

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Probabilistic digital twins for geotechnical design and construction

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Using sand 3D printing, digital image processing technology and DEM to investigate rock hole-fissure interaction mechanisms

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Thermo-mechanical coupling damage constitutive relation of thermally treated rocks: statistical modeling and verification

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Difference of “whole-process and stages” response law of energy evolution regulated by high energy storage rock modification

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Micro-mechanism of shield-soil interaction during shield tunneling based on DEM-MBD coupling simulation

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Study on the multifactor parametric of a transverse air curtain dust control technology during tunnel boring

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Case Study of the Impacts of Pea-gravel Grouting Defects on Loading responses of TBM Tunnel Segment Lining with Different Surrounding Rocks

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近5年高被引文献
Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration

article Full Text OpenAlex 370 FWCI37.28

Cryogenic minimum quantity lubrication machining: from mechanism to application

article Full Text OpenAlex 270 FWCI25.561

Advancements in material removal mechanism and surface integrity of high speed metal cutting: A review

review Full Text OpenAlex 236 FWCI4.896

Application of artificial intelligence in geotechnical engineering: A state-of-the-art review

review Full Text OpenAlex 235 FWCI16.901

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

article Full Text OpenAlex 227 FWCI22.357

Prediction of cement-based mortars compressive strength using machine learning techniques

article Full Text OpenAlex 211 FWCI19.058

Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning

article Full Text OpenAlex 194 FWCI17.958

Review on design and development of cryogenic machining setups for heat resistant alloys and composites

article Full Text OpenAlex 190 FWCI17.251

Challenges in data-driven site characterization

article Full Text OpenAlex 164 FWCI18.624

Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations

article Full Text OpenAlex 164 FWCI19.391