China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the incr...China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inventory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models. The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However, although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9), it was not associated with the most plausible representation of landslide susceptibility. The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias. The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility. However, a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g., the Kuenlun Mountains in the northern Tibetan Plateau). The non-linear mixed-effects model(Model 3) reduced the impact of these biases best by introducing bias-describing variables as random effects. Among the three models, Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive performance(median AUROC of spatial cross validation 0.84) compared to the other two models(median AUROCs of 0.81 and 0.79, respectively). We conclude that ignoring landslide inventory-based incompleteness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.展开更多
In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occu...In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of Dd LSM. This research begins with an introduction of the theoretical aspects of Dd LSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent,inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitativeoriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017-2020. The originality of this research is acknowledged by combining together:(a) a recap of important theoretical aspects of Dd LSM;(b) a bibliometric analysis on the topic;(c) a quantitative-oriented review of relevant publications;and(d) a systematic summary of the findings, indicating important aspects and potential developments related to the Dd LSM research topic. The results show that Dd LSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used. Simple classifiers, such as, logistic regression or approaches based on frequency ratio are still popular, despite the upcoming trend of applying machine learning algorithms. When analyzing validation techniques, 38% of the publications were not clear about the validation method used. Within the studies that included validation techniques, the AUROC was the most popular validation metric, being used accounting for 44% of the studies. Finally, it can be concluded that the application of new classification techniques is often cited as a main research scope, even though the most relevant innovation could also lie in tackling data-quality issues and research designs adaptations to fit the input data particularities in order to improve prediction quality.展开更多
The present research aimed to analyze the influence that different contents of titanium(x=0.5,0.6 and 0.7 wt.%)have on the martensitic transformation temperature of a Cu-14Al-4Ni(wt.%)SMA(shape memory alloy).The Cu-14...The present research aimed to analyze the influence that different contents of titanium(x=0.5,0.6 and 0.7 wt.%)have on the martensitic transformation temperature of a Cu-14Al-4Ni(wt.%)SMA(shape memory alloy).The Cu-14Al-4Ni-xTi samples were casted in an arc-melting furnace and rapidly solidified.All samples underwent heat treatment in a tubular furnace at a temperature of 1,100°C for 30 min and water quenched at 25°C.Subsequently,samples were analyzed by SEM(scanning electron microscopy)with EDS(energy dispersive spectroscopy),XRD(X-ray diffraction)and DSC(differential scanning calorimeter).SEM images and XRD patterns showed that the presence of titanium modified the alloy’s microstructure,induced the formation of three titanium rich phases called“X”phase(CuNi2Ti,Cu3Ti and AlCu2Ti)and reduced the presence of the brittle phaseγ2(Cu9Al4)for samples with 0.6 and 0.7 wt.%Ti.The titanium added to the copper based SMA also functioned as a refiner,reducing GS(grain size)up to approximately 80%with the increase of Ti content.DSC results exhibited low enthalpy levels,hysteresis,as well as low start martensitic transformation temperatures.展开更多
This paper aims to verify the Cu9Al4 phase influence on the nanomechanical behavior of the Cu-14Al-4Ni-xTi alloy obtained by rapid solidification with addition of different amounts of Ti.Using the Scanning Electron Mi...This paper aims to verify the Cu9Al4 phase influence on the nanomechanical behavior of the Cu-14Al-4Ni-xTi alloy obtained by rapid solidification with addition of different amounts of Ti.Using the Scanning Electron Microscopy(SEM),Atomic Force Microscopy(AFM),Energy Dispersion Spectroscopy(EDS)and X-Ray Diffraction(XRD),it was possible to perform the samples’microstructural characterization.In addition,the reduction of the Cu9Al4 phase precipitation and the X-phase appearance were verified according to the increase of the titanium percentage added.The nanomechanical behavior was evaluated by nanoindentation tests,which showed a significant decrease of the elastic modules and an increase of the Poisson coefficient’s according to the titanium amount.This research establishes that the reduction of Cu9Al4 phase implies on the increase of the capacity to dissipate energy.Therefore,the high damping capacity combined with the X-phase presence increases the super elasticity and the alloy ductility.展开更多
基金This work was supported primarily by the National Key Research and Development Program of China(Grant Nos.2016YFA0602403,2017YFC1502505)the National Natural Science Funds(Grant No.41271544)+1 种基金the Startup Foundation for Introducing Talent of NUISTthe Second Tibetan Plateau Scientific Expedition and Research Program(Grant Nos.2019QZKK0906,2019QZKK0606)。
文摘China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inventory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models. The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However, although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9), it was not associated with the most plausible representation of landslide susceptibility. The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias. The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility. However, a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g., the Kuenlun Mountains in the northern Tibetan Plateau). The non-linear mixed-effects model(Model 3) reduced the impact of these biases best by introducing bias-describing variables as random effects. Among the three models, Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive performance(median AUROC of spatial cross validation 0.84) compared to the other two models(median AUROCs of 0.81 and 0.79, respectively). We conclude that ignoring landslide inventory-based incompleteness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.
基金support to the first author from CNPq,the National Council of Technological and Scientific Development—Brazil(Process number 234815/2014-0)。
文摘In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of Dd LSM. This research begins with an introduction of the theoretical aspects of Dd LSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent,inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitativeoriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017-2020. The originality of this research is acknowledged by combining together:(a) a recap of important theoretical aspects of Dd LSM;(b) a bibliometric analysis on the topic;(c) a quantitative-oriented review of relevant publications;and(d) a systematic summary of the findings, indicating important aspects and potential developments related to the Dd LSM research topic. The results show that Dd LSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used. Simple classifiers, such as, logistic regression or approaches based on frequency ratio are still popular, despite the upcoming trend of applying machine learning algorithms. When analyzing validation techniques, 38% of the publications were not clear about the validation method used. Within the studies that included validation techniques, the AUROC was the most popular validation metric, being used accounting for 44% of the studies. Finally, it can be concluded that the application of new classification techniques is often cited as a main research scope, even though the most relevant innovation could also lie in tackling data-quality issues and research designs adaptations to fit the input data particularities in order to improve prediction quality.
基金The authors would like to thank the Federal Institute of Science and Technology of Bahia,the University of São Paulo,the University of Brasilia and the PRPGI for all the support to carry out this research.
文摘The present research aimed to analyze the influence that different contents of titanium(x=0.5,0.6 and 0.7 wt.%)have on the martensitic transformation temperature of a Cu-14Al-4Ni(wt.%)SMA(shape memory alloy).The Cu-14Al-4Ni-xTi samples were casted in an arc-melting furnace and rapidly solidified.All samples underwent heat treatment in a tubular furnace at a temperature of 1,100°C for 30 min and water quenched at 25°C.Subsequently,samples were analyzed by SEM(scanning electron microscopy)with EDS(energy dispersive spectroscopy),XRD(X-ray diffraction)and DSC(differential scanning calorimeter).SEM images and XRD patterns showed that the presence of titanium modified the alloy’s microstructure,induced the formation of three titanium rich phases called“X”phase(CuNi2Ti,Cu3Ti and AlCu2Ti)and reduced the presence of the brittle phaseγ2(Cu9Al4)for samples with 0.6 and 0.7 wt.%Ti.The titanium added to the copper based SMA also functioned as a refiner,reducing GS(grain size)up to approximately 80%with the increase of Ti content.DSC results exhibited low enthalpy levels,hysteresis,as well as low start martensitic transformation temperatures.
基金The authorswould like to thankthe Federal Institute of Science and Technology of Bahia,the University of Sao Paulo and the University of Brasilia for all the support to carry out this research.
文摘This paper aims to verify the Cu9Al4 phase influence on the nanomechanical behavior of the Cu-14Al-4Ni-xTi alloy obtained by rapid solidification with addition of different amounts of Ti.Using the Scanning Electron Microscopy(SEM),Atomic Force Microscopy(AFM),Energy Dispersion Spectroscopy(EDS)and X-Ray Diffraction(XRD),it was possible to perform the samples’microstructural characterization.In addition,the reduction of the Cu9Al4 phase precipitation and the X-phase appearance were verified according to the increase of the titanium percentage added.The nanomechanical behavior was evaluated by nanoindentation tests,which showed a significant decrease of the elastic modules and an increase of the Poisson coefficient’s according to the titanium amount.This research establishes that the reduction of Cu9Al4 phase implies on the increase of the capacity to dissipate energy.Therefore,the high damping capacity combined with the X-phase presence increases the super elasticity and the alloy ductility.
