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 and safe TBM excavation.However,abnormal wear has yet to ...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 and safe TBM excavation.However,abnormal wear has yet to be thoroughly investigated,primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear.This study developed a prediction model for abnormal TBM disc cutter wear,considering mixed ground conditions,by employing interpretable machine learning with data augmentation.An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions,and wear data was obtained from 65 cutterhead intervention(CHI)reports covering both mixed ground and hard rock sections.With a balanced training dataset obtained by data augmentation,an extreme gradient boosting(XGB)model delivered acceptable results with an accuracy of 0.94,an F1-score of 0.808,and a recall of 0.8.In addition,the accuracy for each individual disc cutter exhibited low variability.When employing data augmentation,a significant improvement in recall was observed compared to when it was not used,although the difference in accuracy and F1-score was marginal.The subsequent model interpretation revealed the chamber pressure,cutter installation radius,and torque as significant contributors.Specifically,a threshold in chamber pressure was observed,which could induce abnormal wear.The study also explored how elevated values of these influential contributors correlate with abnormal wear.The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters,enhancing the safety and efficiency of TBM operations.展开更多
Excavation-induced deformations of earth-retaining walls(ERWs)can critically affect the safety of surrounding structures,highlighting the need for reliable prediction models to support timely decision-making during co...Excavation-induced deformations of earth-retaining walls(ERWs)can critically affect the safety of surrounding structures,highlighting the need for reliable prediction models to support timely decision-making during construction.This study utilizes traditional statistical ARIMA(Auto-Regressive Integrated Moving Average)and deep learning-based LSTM(Long Short-Term Memory)models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models.The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time,whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within the time series data.This research includes preprocessing of measurement data for inclinometer,performance evaluation based on various time series data lengths and input variable conditions,and demonstrates that the LSTM model offers statistically significant improvements in predictive performance over the ARIMA model.In addition,by combining LSTM with attention mechanism,attention-based LSTM(ATLSTM)is proposed to improve the short-and long-term prediction performance and solve the problem of excavation site domain change.This study presents the advantages and disadvantages of major time series analysis models for the stability evaluation of mud walls using geotechnical inclinometer data from excavation sites,and suggests that time series analysis models can be used effectively through comparative experiments.展开更多
As environmental concerns drive shifts in construction materials,rock is increasingly considered as an alternative to sand,reinforcing the importance of understanding its dynamic properties.This study investigates the...As environmental concerns drive shifts in construction materials,rock is increasingly considered as an alternative to sand,reinforcing the importance of understanding its dynamic properties.This study investigates the effect of water content on the small-strain dynamic properties of basalt samples using free-free laboratory testing.Free-free testing,which requires minimal equipment and preparation,provides an efficient and low-cost method for determining key dynamic properties,including three wave velocities(V_(s),V_(p),and V_(c)),material damping ratios,and Poisson's ratios.These properties are crucial for numerical modeling in earthquake analyses and geotechnical site characterization.The test consists of three components:(1)direct travel-time measurement,(2)torsional resonance testing,and(3)compressional resonance testing.A total of 20 rock samples were tested under systematically controlled water contents,ranging from fully dried to saturated,to quantify the effects on Poisson's ratio and material damping ratios.The results showed significant increases in both parameters with rising water content.The Poisson's ratio increased by up to 320%for aphanitic basalt and 150%for vesicular basalt,while the damping ratio rose up to 30-fold(D_(c,min))and 16-fold(D_(s,min)).These findings highlight the critical need to incorporate consideration of water content when characterizing dynamic rock properties for seismic and geotechnical analyses.The practical applicability of this research lies in improving in situ property interpretation and enhancing seismic design reliability by providing engineers with precise relationships between water content and dynamic rock behavior.展开更多
基金support of the“National R&D Project for Smart Construction Technology (Grant No.RS-2020-KA157074)”funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land,Infrastructure and Transport,and managed by the Korea Expressway Corporation.
文摘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 and safe TBM excavation.However,abnormal wear has yet to be thoroughly investigated,primarily due to the complexity of considering mixed ground conditions and the imbalance in the number of instances between the two types of wear.This study developed a prediction model for abnormal TBM disc cutter wear,considering mixed ground conditions,by employing interpretable machine learning with data augmentation.An equivalent elastic modulus was used to consider the characteristics of mixed ground conditions,and wear data was obtained from 65 cutterhead intervention(CHI)reports covering both mixed ground and hard rock sections.With a balanced training dataset obtained by data augmentation,an extreme gradient boosting(XGB)model delivered acceptable results with an accuracy of 0.94,an F1-score of 0.808,and a recall of 0.8.In addition,the accuracy for each individual disc cutter exhibited low variability.When employing data augmentation,a significant improvement in recall was observed compared to when it was not used,although the difference in accuracy and F1-score was marginal.The subsequent model interpretation revealed the chamber pressure,cutter installation radius,and torque as significant contributors.Specifically,a threshold in chamber pressure was observed,which could induce abnormal wear.The study also explored how elevated values of these influential contributors correlate with abnormal wear.The proposed model offers a valuable tool for planning the replacement of abnormally worn disc cutters,enhancing the safety and efficiency of TBM operations.
基金carried out under the KICT Research Program(Project No.20250285-001,Development of Infrastructure Disaster Prevention Technology Based on Satellites SAR)funded by the Ministry of Science and ICT.
文摘Excavation-induced deformations of earth-retaining walls(ERWs)can critically affect the safety of surrounding structures,highlighting the need for reliable prediction models to support timely decision-making during construction.This study utilizes traditional statistical ARIMA(Auto-Regressive Integrated Moving Average)and deep learning-based LSTM(Long Short-Term Memory)models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models.The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time,whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within the time series data.This research includes preprocessing of measurement data for inclinometer,performance evaluation based on various time series data lengths and input variable conditions,and demonstrates that the LSTM model offers statistically significant improvements in predictive performance over the ARIMA model.In addition,by combining LSTM with attention mechanism,attention-based LSTM(ATLSTM)is proposed to improve the short-and long-term prediction performance and solve the problem of excavation site domain change.This study presents the advantages and disadvantages of major time series analysis models for the stability evaluation of mud walls using geotechnical inclinometer data from excavation sites,and suggests that time series analysis models can be used effectively through comparative experiments.
文摘As environmental concerns drive shifts in construction materials,rock is increasingly considered as an alternative to sand,reinforcing the importance of understanding its dynamic properties.This study investigates the effect of water content on the small-strain dynamic properties of basalt samples using free-free laboratory testing.Free-free testing,which requires minimal equipment and preparation,provides an efficient and low-cost method for determining key dynamic properties,including three wave velocities(V_(s),V_(p),and V_(c)),material damping ratios,and Poisson's ratios.These properties are crucial for numerical modeling in earthquake analyses and geotechnical site characterization.The test consists of three components:(1)direct travel-time measurement,(2)torsional resonance testing,and(3)compressional resonance testing.A total of 20 rock samples were tested under systematically controlled water contents,ranging from fully dried to saturated,to quantify the effects on Poisson's ratio and material damping ratios.The results showed significant increases in both parameters with rising water content.The Poisson's ratio increased by up to 320%for aphanitic basalt and 150%for vesicular basalt,while the damping ratio rose up to 30-fold(D_(c,min))and 16-fold(D_(s,min)).These findings highlight the critical need to incorporate consideration of water content when characterizing dynamic rock properties for seismic and geotechnical analyses.The practical applicability of this research lies in improving in situ property interpretation and enhancing seismic design reliability by providing engineers with precise relationships between water content and dynamic rock behavior.
