Forest fire accidents caused by distribution line faults occur frequently,resulting in heavy impacts on people’s safety and social and economic development.Currently,there are few risk assessments for forest fires in...Forest fire accidents caused by distribution line faults occur frequently,resulting in heavy impacts on people’s safety and social and economic development.Currently,there are few risk assessments for forest fires induced by over-head distribution lines,and existing assessment methods may have difficulties in data acquisition.On this basis,a novel as-sessment framework based on an analytic hierarchy process,a Bayesian network and a Fussel-Vesely importance metric is proposed in this paper.The framework combines field research and historical operation and maintenance data to assess the regional-scale risk of forest fires induced by overhead distribution lines to derive the probability of forest fires and to identify high-risk lines and key hazard events in the assessment region.Finally,taking the southern Anhui region as an ex-ample,the annual fire probability of forest fires induced by overhead distribution lines in the southern Anhui region is 5.88%,and rectification measures are proposed.This study provides management with a complete assessment framework that optimizes the difficulty of data collection and allows for additional targeted corrective measures to be proposed for the entire region and route on the basis of the assessment results.展开更多
The residual elastic energy index is a scientific evaluation index for rockburst proneness.In laboratory test,it is sometimes difficult to obtain the post-peak curve or to test the rock sample several times,which make...The residual elastic energy index is a scientific evaluation index for rockburst proneness.In laboratory test,it is sometimes difficult to obtain the post-peak curve or to test the rock sample several times,which makes it impossible to calculate the residual elastic energy index accurately.Based on 241 sets of experimental data and four input indexes of density,elastic modulus,peak intensity and peak input strain energy,this study proposed a machine learning model combining k-means clustering algorithm and random forest regression model:cluster forest(CF)model.The research employed a stratified sampling method on the dataset to ensure the representativeness and balance of the samples.Subsequently,grid search and five-fold cross-validation were utilized to optimize the model’s hyperparameters,aiming to enhance its generalization capability and prediction accuracy.Finally,the performance of the optimal model was evaluated using a test set and compared with five other commonly used models.The results indicate that the CF model outperformed the other models on the testing set,with a mean absolute error of 6.6%,and an accuracy of 93.9%.The results of sensitivity analyses reveal the degree of influence of each variable on rockburst proneness and the applicability of the CF model when the input parameters are missing.The robustness and generalization ability of the model were verified by introducing experimental data from other studies,and the results confirmed the reliability and applicability of the model.Therefore,the model not only effectively simplifies the acquisition of the residual elastic energy index,but also shows excellent performance and wide applicability.展开更多
Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with dam...Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with damping layer.However,the traditional numerical methods suffer from the complex modelling and time-consuming problems.Therefore,a prediction model named the random forest regressor(RFR)is proposed based on 240 numerical simulation results of the mechanical response of tunnel lining.In addition,circle mapping(CM)is used to improve Archimedes optimization algorithm(AOA),reptile search algorithm(RSA),and Chernobyl disaster optimizer(CDO)to further improve the predictive performance of the RFR model.The performance evaluation results show that the CMRSA-RFR is the best prediction model.The damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping layer.This study verifies the feasibility of combining numerical simulation with machine learning technology,and provides a new solution for predicting the mechanical response of aseismic tunnel with damping layer.展开更多
Forests provide multiple benefits as a safety net to the poor and also help to increase the overall ecological balance.Of particular importance in this respect this research has been conducted in six typical villages ...Forests provide multiple benefits as a safety net to the poor and also help to increase the overall ecological balance.Of particular importance in this respect this research has been conducted in six typical villages in South and Southeast Asia,located in Northern Bangladesh,Southern Myanmar and Northwest Thailand.Data collection methods were used by means of observation,FGD,interviews,and a structured survey of farm households.Data were analyzed through qualitative methods展开更多
The effects of urban remnant natural evergreen broad-leaved forest (EBLF) on the restoration of artificial pine forests surrounding it were studied with reference to species composition,biodiversity,dominant species a...The effects of urban remnant natural evergreen broad-leaved forest (EBLF) on the restoration of artificial pine forests surrounding it were studied with reference to species composition,biodiversity,dominant species and stand structure on Mt. Tieshanping in Chongqing metropolis,Southwest China. The seeds from the remnant EBLF naturally facilitate the restoration process of artificial Pinus massoniana forests near it. The similarity of species composition between the artificial Pinus massoniana forests and the remnant EBLF and biodiversity index of the artificial Pinus massoniana forests decrease as the distance from the remnant EBLF increases. Castanopsis carlesii var. spinusa is the dominant species in the ground vegetation,shrub layer and sub-tree layer of the Pinus massoniana forests near the remnant EBLF. However,the natural restoration processes of those farther away from the remnant EBLF are restricted for the absence of seed source of the inherent components of the remnant EBLF,and the anthropogenic measures should be taken to facilitate the restoration process.展开更多
基金This work was supported by the National Key Research and Development Program of China(2022YFC3003101)the Fundamental Research Funds for the Central Universities(WK2320000050)the Science and Technology Program of State Grid Anhui Electric Power Co.,Ltd.(521205220001).
文摘Forest fire accidents caused by distribution line faults occur frequently,resulting in heavy impacts on people’s safety and social and economic development.Currently,there are few risk assessments for forest fires induced by over-head distribution lines,and existing assessment methods may have difficulties in data acquisition.On this basis,a novel as-sessment framework based on an analytic hierarchy process,a Bayesian network and a Fussel-Vesely importance metric is proposed in this paper.The framework combines field research and historical operation and maintenance data to assess the regional-scale risk of forest fires induced by overhead distribution lines to derive the probability of forest fires and to identify high-risk lines and key hazard events in the assessment region.Finally,taking the southern Anhui region as an ex-ample,the annual fire probability of forest fires induced by overhead distribution lines in the southern Anhui region is 5.88%,and rectification measures are proposed.This study provides management with a complete assessment framework that optimizes the difficulty of data collection and allows for additional targeted corrective measures to be proposed for the entire region and route on the basis of the assessment results.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(SDGZK2431)supported by the State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering,Sichuan University,China。
文摘The residual elastic energy index is a scientific evaluation index for rockburst proneness.In laboratory test,it is sometimes difficult to obtain the post-peak curve or to test the rock sample several times,which makes it impossible to calculate the residual elastic energy index accurately.Based on 241 sets of experimental data and four input indexes of density,elastic modulus,peak intensity and peak input strain energy,this study proposed a machine learning model combining k-means clustering algorithm and random forest regression model:cluster forest(CF)model.The research employed a stratified sampling method on the dataset to ensure the representativeness and balance of the samples.Subsequently,grid search and five-fold cross-validation were utilized to optimize the model’s hyperparameters,aiming to enhance its generalization capability and prediction accuracy.Finally,the performance of the optimal model was evaluated using a test set and compared with five other commonly used models.The results indicate that the CF model outperformed the other models on the testing set,with a mean absolute error of 6.6%,and an accuracy of 93.9%.The results of sensitivity analyses reveal the degree of influence of each variable on rockburst proneness and the applicability of the CF model when the input parameters are missing.The robustness and generalization ability of the model were verified by introducing experimental data from other studies,and the results confirmed the reliability and applicability of the model.Therefore,the model not only effectively simplifies the acquisition of the residual elastic energy index,but also shows excellent performance and wide applicability.
基金Project(2023YFB2390400)supported by the National Key R&D Programs for Young Scientists,ChinaProjects(U21A20159,52079133,52379112,52309123,41902288)supported by the National Natural Science Foundation of China+5 种基金Project(2024AFB041)supported by the Hubei Provincial Natural Science Foundation,ChinaProject(QTKS0034W23291)supported by the Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources,ChinaProject(2023SGG07)supported by the Visiting Researcher Fund Program of State Key Laboratory of Water Resources Engineering and Management,ChinaProject(2022KY56(ZDZX)-02)supported by the Key Research Program of FSDI,ChinaProject(SKS-2022103)supported by the Key Research Program of the Ministry of Water Resources,ChinaProject(202102AF080001)supported by the Yunnan Major Science and Technology Special Program,China。
文摘Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters,and several methods have been explored to predict mechanical response of tunnel lining with damping layer.However,the traditional numerical methods suffer from the complex modelling and time-consuming problems.Therefore,a prediction model named the random forest regressor(RFR)is proposed based on 240 numerical simulation results of the mechanical response of tunnel lining.In addition,circle mapping(CM)is used to improve Archimedes optimization algorithm(AOA),reptile search algorithm(RSA),and Chernobyl disaster optimizer(CDO)to further improve the predictive performance of the RFR model.The performance evaluation results show that the CMRSA-RFR is the best prediction model.The damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping layer.This study verifies the feasibility of combining numerical simulation with machine learning technology,and provides a new solution for predicting the mechanical response of aseismic tunnel with damping layer.
文摘Forests provide multiple benefits as a safety net to the poor and also help to increase the overall ecological balance.Of particular importance in this respect this research has been conducted in six typical villages in South and Southeast Asia,located in Northern Bangladesh,Southern Myanmar and Northwest Thailand.Data collection methods were used by means of observation,FGD,interviews,and a structured survey of farm households.Data were analyzed through qualitative methods
基金Project(30700094) supported by the National Natural Science Foundation of ChinaProject (CSTC, 2008BB7187) supported by the Natural Science Foundation of CQ CSTC, China+2 种基金Project (20092x07104-003-02)supported by the National Science and Technology MinistrySubsidy from the Pro Natural Fund of Japan for 2007Research project for a sustainable development of economic and social structure dependent on the environment of the eastern coast of Asia from Tokyo University of Information
文摘The effects of urban remnant natural evergreen broad-leaved forest (EBLF) on the restoration of artificial pine forests surrounding it were studied with reference to species composition,biodiversity,dominant species and stand structure on Mt. Tieshanping in Chongqing metropolis,Southwest China. The seeds from the remnant EBLF naturally facilitate the restoration process of artificial Pinus massoniana forests near it. The similarity of species composition between the artificial Pinus massoniana forests and the remnant EBLF and biodiversity index of the artificial Pinus massoniana forests decrease as the distance from the remnant EBLF increases. Castanopsis carlesii var. spinusa is the dominant species in the ground vegetation,shrub layer and sub-tree layer of the Pinus massoniana forests near the remnant EBLF. However,the natural restoration processes of those farther away from the remnant EBLF are restricted for the absence of seed source of the inherent components of the remnant EBLF,and the anthropogenic measures should be taken to facilitate the restoration process.
