Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision.This study proposes a new model to estimate the disc cut...Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision.This study proposes a new model to estimate the disc cutter life(Hf)by integrating a group method of data handling(GMDH)-type neural network(NN)with a genetic algorithm(GA).The efficiency and effectiveness of the GMDH network structure are optimized by the GA,which enables each neuron to search for its optimum connections set from the previous layer.With the proposed model,monitoring data including the shield performance database,disc cutter consumption,geological conditions,and operational parameters can be analyzed.To verify the performance of the proposed model,a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model.The results indicate that the hybrid model predicts disc cutter life with high accuracy.The sensitivity analysis reveals that the penetration rate(PR)has a significant influence on disc cutter life.The results of this study can be beneficial in both the planning and construction stages of shield tunneling.展开更多
When pumping is conducted in confined aquifer inside excavation pit(waterproof curtain),the direction of the groundwater seepage outside the excavation changes from horizontal to vertical owing to the existence of the...When pumping is conducted in confined aquifer inside excavation pit(waterproof curtain),the direction of the groundwater seepage outside the excavation changes from horizontal to vertical owing to the existence of the curtain barrier.There is no analytical calculation method for the groundwater head distribution induced by dewatering inside excavation.This paper first analyses the mechanism of the blocking effects from a close barrier in confined aquifer.Then,a simple equation based on analytical solution is proposed to calculate groundwater heads inside and outside of the excavation pit with waterproof curtain(hereafter refer to close barrier)in a confined aquifer.The distribution of groundwater head is derived according to two conditions:(i)pumping with a constant water head,and(ii)pumping with a constant flow rate.The proposed calculation equation is verified by both numerical simulation and experimental results.The comparisons demonstrate that the proposed model can be applied in engineering practice of excavation.展开更多
This study proposes an approach that considers mitigation strategies in predicting landslide susceptibility through machine learning(ML)and geographic information system(GIS)techniques.ML models,such as random forest(...This study proposes an approach that considers mitigation strategies in predicting landslide susceptibility through machine learning(ML)and geographic information system(GIS)techniques.ML models,such as random forest(RF),logistic regression(LR),and support vector classification(SVC)are incorporated into GIS to predict landslide susceptibilities in Hong Kong.To consider the effect of mitigation strategies on landslide susceptibility,non-landslide samples were produced in the upgraded area and added to randomly created samples to serve as ML models in training datasets.Two scenarios were created to compare and demonstrate the efficiency of the proposed approach;Scenario I does not considering landslide control while Scenario II considers mitigation strategies for landslide control.The largest landslide susceptibilities are 0.967(from RF),followed by 0.936(from LR)and 0.902(from SVC)in Scenario II;in Scenario I,they are 0.986(from RF),0.955(from LR)and 0.947(from SVC).This proves that the ML models considering mitigation strategies can decrease the current landslide susceptibilities.The comparison between the different ML models shows that RF performed better than LR and SVC,and provides the best prediction of the spatial distribution of landslide susceptibilities.展开更多
基金The research work was funded by“The Pearl River Talent Recruitment Program”in 2019(2019CX01G338)Guangdong Province and the Research Funding of Shantou University for New Faculty Member(NTF19024-2019),China.
文摘Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision.This study proposes a new model to estimate the disc cutter life(Hf)by integrating a group method of data handling(GMDH)-type neural network(NN)with a genetic algorithm(GA).The efficiency and effectiveness of the GMDH network structure are optimized by the GA,which enables each neuron to search for its optimum connections set from the previous layer.With the proposed model,monitoring data including the shield performance database,disc cutter consumption,geological conditions,and operational parameters can be analyzed.To verify the performance of the proposed model,a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model.The results indicate that the hybrid model predicts disc cutter life with high accuracy.The sensitivity analysis reveals that the penetration rate(PR)has a significant influence on disc cutter life.The results of this study can be beneficial in both the planning and construction stages of shield tunneling.
基金“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338)Guangdong Province and the Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019)the National Natural Science Foundation of China(NSFC)(Grant No.41807235).
文摘When pumping is conducted in confined aquifer inside excavation pit(waterproof curtain),the direction of the groundwater seepage outside the excavation changes from horizontal to vertical owing to the existence of the curtain barrier.There is no analytical calculation method for the groundwater head distribution induced by dewatering inside excavation.This paper first analyses the mechanism of the blocking effects from a close barrier in confined aquifer.Then,a simple equation based on analytical solution is proposed to calculate groundwater heads inside and outside of the excavation pit with waterproof curtain(hereafter refer to close barrier)in a confined aquifer.The distribution of groundwater head is derived according to two conditions:(i)pumping with a constant water head,and(ii)pumping with a constant flow rate.The proposed calculation equation is verified by both numerical simulation and experimental results.The comparisons demonstrate that the proposed model can be applied in engineering practice of excavation.
基金funding by the National Natural Science Foundation of China(Grant No.42007416)the Hong Kong Polytechnic University Strategic Importance Fund(ZE2T)and Project of Research Institute of Land and Space(CD78).
文摘This study proposes an approach that considers mitigation strategies in predicting landslide susceptibility through machine learning(ML)and geographic information system(GIS)techniques.ML models,such as random forest(RF),logistic regression(LR),and support vector classification(SVC)are incorporated into GIS to predict landslide susceptibilities in Hong Kong.To consider the effect of mitigation strategies on landslide susceptibility,non-landslide samples were produced in the upgraded area and added to randomly created samples to serve as ML models in training datasets.Two scenarios were created to compare and demonstrate the efficiency of the proposed approach;Scenario I does not considering landslide control while Scenario II considers mitigation strategies for landslide control.The largest landslide susceptibilities are 0.967(from RF),followed by 0.936(from LR)and 0.902(from SVC)in Scenario II;in Scenario I,they are 0.986(from RF),0.955(from LR)and 0.947(from SVC).This proves that the ML models considering mitigation strategies can decrease the current landslide susceptibilities.The comparison between the different ML models shows that RF performed better than LR and SVC,and provides the best prediction of the spatial distribution of landslide susceptibilities.
