Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarci...Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarcity,which frequently impedes the generation of accurate and informative landslide susceptibility maps.Addressing this challenge,this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically.Notably,the proposed model,calibrated with the warmup-cosine annealing(WCA)learning rate strategy,demonstrated remarkable predictive capabilities,particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region.This is evidenced by the area under the receiver operating characteristic curve(AUC)values,which exhibited significant improvements of 51.00%,24.40%and 2.15%,respectively,compared to a deep learning model,in contexts where only 1%,5%and 10%of data from the target region were used for retraining.Simultaneously,there were reductions in loss of 16.12%,27.61%and 15.44%,respectively,in these instances.展开更多
[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-d...[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.展开更多
The volatile chemical components of Radix Paeoniae Rubra (RPR) were analyzed by gas chromatography-mass spectrometry with the method of heuristic evolving latent projections and overall volume integration. The results...The volatile chemical components of Radix Paeoniae Rubra (RPR) were analyzed by gas chromatography-mass spectrometry with the method of heuristic evolving latent projections and overall volume integration. The results show that 38 volatile chemical components of RPR are determined, accounting for 95.21% of total contents of volatile chemical components of RPR. The main volatile chemical components of RPR are (Z, Z)-9,12-octadecadienoic acid, n-hexadecanoic acid, 2-hydroxy- benzaldehyde, 1-(2-hydroxy-4-methoxyphenyl)-ethanone, 6,6-dimethyl-bicyclo[3.1.1] heptane-2-methanol, 4,7-dimethyl-benzofuran, 4-(1-methylethenyl)-1-cyclohexene-1-carboxaldehyde, and cyclohexadecane.展开更多
Recycling end-of-life tire rubber as asphalt modifier is known as a sustainable paving technology with merits including enhanced pavement durability,waste tire consumption and noise reduction.However,the criticisms on...Recycling end-of-life tire rubber as asphalt modifier is known as a sustainable paving technology with merits including enhanced pavement durability,waste tire consumption and noise reduction.However,the criticisms on the high construction emissions of asphalt rubber(AR)have limited its application.Warm mix asphalt(WMA)effectively reduces the mixing and compaction temperatures of conventional hot mix asphalt mixtures.The combination of AR and WMA,called warm asphalt rubber(WAR),is a promising paving material which achieves pavement sustainability from principles to practices.Many studies have demonstrated that WMA technologies work effectively with AR pavement in different ways,alleviating the concerns of potential higher emissions of AR by decreasing mixing and paving temperatures.A comprehensive literature review about WAR brings a better understanding of this promising paving technology.The findings of 165 publications were summarized in this review.It summarized the recent developments of WAR in various aspects,including rheological properties,mix design,mixture mechanical performance,field application,construction emission,and asphalt-rubber-WMA additive interaction.It is expected that this review is able to provide extensive information to explore further research development and application of WAR.展开更多
To characterize and recognize the debris flow-related deposits,the physico-mechanical performance of four deposits from the Dongyuege(DYG),Shawa(SW),Jiangjia Gully(JJG),and Gengdi(GD)debris flows in southwest China is...To characterize and recognize the debris flow-related deposits,the physico-mechanical performance of four deposits from the Dongyuege(DYG),Shawa(SW),Jiangjia Gully(JJG),and Gengdi(GD)debris flows in southwest China is investigated through laboratory analyses and tests.The four debris-flow materials can all be remolded into coherent,homogeneous cylinders with high densification and strength–porosity of 25%-36%,mean pore-throat radius of 0.46-5.89μm,median pore-throat radius of 0.43-4.28μm,P-wave velocity of 800-1200 m/s,modulus of elasticity of 28-103 MPa,unconfined compressive strength(UCS)of 220-760 kPa,and cohesion of 65-281 kPa.Based on the comparison in slurryability and formability among debris-flow deposits,granular flow deposits,fluvial deposits,residual lateritic clay and loess,whether a sediment can be cast into competent cylinders for physico-mechanical tests can be regarded as a diagnostic evidence of old debris-flow deposits.The discrepancy in physico-mechanical properties among the four debris-flow deposits suggests that the combination of foregoing physico-mechanical parameters can characterize assembling characteristics of debris flow-related sediments including grain size distribution,mineralogy,and accidental detritus.Four deposited sediments above can be surprisingly classified as hard soil-soft rocks according to UCS,and the hard soil-soft rock behaviors can advance the further understanding of debris flows.展开更多
基金Project(2301DH09002)supported by the Bureau of Planning and Natural Resources,Chongqing,ChinaProject(2022T3051)supported by the Science and Technology Service Network Initiative,ChinaProject(2018-ZL-01)supported by the Sichuan Transportation Science and Technology,China。
文摘Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarcity,which frequently impedes the generation of accurate and informative landslide susceptibility maps.Addressing this challenge,this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically.Notably,the proposed model,calibrated with the warmup-cosine annealing(WCA)learning rate strategy,demonstrated remarkable predictive capabilities,particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region.This is evidenced by the area under the receiver operating characteristic curve(AUC)values,which exhibited significant improvements of 51.00%,24.40%and 2.15%,respectively,compared to a deep learning model,in contexts where only 1%,5%and 10%of data from the target region were used for retraining.Simultaneously,there were reductions in loss of 16.12%,27.61%and 15.44%,respectively,in these instances.
文摘[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
基金Project(20235020) supported by the National Natural Science Foundation of China
文摘The volatile chemical components of Radix Paeoniae Rubra (RPR) were analyzed by gas chromatography-mass spectrometry with the method of heuristic evolving latent projections and overall volume integration. The results show that 38 volatile chemical components of RPR are determined, accounting for 95.21% of total contents of volatile chemical components of RPR. The main volatile chemical components of RPR are (Z, Z)-9,12-octadecadienoic acid, n-hexadecanoic acid, 2-hydroxy- benzaldehyde, 1-(2-hydroxy-4-methoxyphenyl)-ethanone, 6,6-dimethyl-bicyclo[3.1.1] heptane-2-methanol, 4,7-dimethyl-benzofuran, 4-(1-methylethenyl)-1-cyclohexene-1-carboxaldehyde, and cyclohexadecane.
基金Project(51808228)supported by the National Natural Science Foundation of ChinaProject(OE514/10-1)supported by the German Research Foundation。
文摘Recycling end-of-life tire rubber as asphalt modifier is known as a sustainable paving technology with merits including enhanced pavement durability,waste tire consumption and noise reduction.However,the criticisms on the high construction emissions of asphalt rubber(AR)have limited its application.Warm mix asphalt(WMA)effectively reduces the mixing and compaction temperatures of conventional hot mix asphalt mixtures.The combination of AR and WMA,called warm asphalt rubber(WAR),is a promising paving material which achieves pavement sustainability from principles to practices.Many studies have demonstrated that WMA technologies work effectively with AR pavement in different ways,alleviating the concerns of potential higher emissions of AR by decreasing mixing and paving temperatures.A comprehensive literature review about WAR brings a better understanding of this promising paving technology.The findings of 165 publications were summarized in this review.It summarized the recent developments of WAR in various aspects,including rheological properties,mix design,mixture mechanical performance,field application,construction emission,and asphalt-rubber-WMA additive interaction.It is expected that this review is able to provide extensive information to explore further research development and application of WAR.
基金Project(41931294)supported by the National Natural Science Foundation of ChinaProjects(U1502232,U1033601)supported by the National Natural Science Foundation of China-Yunnan Joint Fund。
文摘To characterize and recognize the debris flow-related deposits,the physico-mechanical performance of four deposits from the Dongyuege(DYG),Shawa(SW),Jiangjia Gully(JJG),and Gengdi(GD)debris flows in southwest China is investigated through laboratory analyses and tests.The four debris-flow materials can all be remolded into coherent,homogeneous cylinders with high densification and strength–porosity of 25%-36%,mean pore-throat radius of 0.46-5.89μm,median pore-throat radius of 0.43-4.28μm,P-wave velocity of 800-1200 m/s,modulus of elasticity of 28-103 MPa,unconfined compressive strength(UCS)of 220-760 kPa,and cohesion of 65-281 kPa.Based on the comparison in slurryability and formability among debris-flow deposits,granular flow deposits,fluvial deposits,residual lateritic clay and loess,whether a sediment can be cast into competent cylinders for physico-mechanical tests can be regarded as a diagnostic evidence of old debris-flow deposits.The discrepancy in physico-mechanical properties among the four debris-flow deposits suggests that the combination of foregoing physico-mechanical parameters can characterize assembling characteristics of debris flow-related sediments including grain size distribution,mineralogy,and accidental detritus.Four deposited sediments above can be surprisingly classified as hard soil-soft rocks according to UCS,and the hard soil-soft rock behaviors can advance the further understanding of debris flows.
