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Some non-linear height–diameter models performance for mixed stand in forests in Northwest Iran

Journal of Mountain Science 2020年第5期17卷 1084-1095页

作者： Roya ABEDI Tooba ABEDI Department of Forestry Ahar Faculty of Agriculture and Natural ResourcesUniversity of TabrizAhar ***Iran Environmental Research Institute Academic Center for EducationCulture and ResearchRasht 41635-3114Iran

This study evaluated the total height of trees based on diameter at breast height by using 23 widely used height-diameter non-linear regression models for mixed-species forest stands consisting of Caucasian oak,field maple,and hornbeam from forests in Northwest Iran.1920 trees were measured in 6 sampling plots(every sampling plot has 1 ha area).The fit of the best height–diameter models for each species were compared based on R2,Root Mean Square Error(RMSE),Akaike information criterion(AIC),standard error,and relative ranking performance *** the final step,verification of results was performed by paired sample t-test to compare the observed height and estimated *** showed that among 23 height-diameter models,the best models were obtained from the top five ones including Modified-logistic,Prodan,Sibbesen,Burkhart,and *** between the actual observed height and estimated height for Caucasian oak showed that Modified–Logistic,Prodan,Sibbesen,Burkhart,and Exponential performed better than the others,respectively(There were no statistically significant differences between observed heights and predicted height(p≥0.05)).Prodan,Modified-Logistic,Burkhart,and Loetch evaluated field maple tree height correctly,and Modified-Logistic,Burkhart,and Loetch had better fitness compared to the others for hornbeam,*** other models were introduced as appropriate criteria,they could not reliably predict the height of *** the Rank analysis,the Modified-Logistic model for the Caucasian oak and Prodan model for field maple and hornbeam had the best ***,to complement the results of this study,it is suggested to assess how environmental factors such as elevation,climate parameters,forest protection policy and forest structure will modify height-diameter allometry models and will enhance the prediction accuracy of tree heights prediction in mixed stands.

关键词： Arasbaran Forest inventory Height prediction Model validation non-linear modeling Rank analysis

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Earthquake safety assessment of concrete arch and gravity dams

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Earthquake Engineering and Engineering Vibration 2005年第2期4卷 251-264页

作者：林皋胡志强 School of Civil and Hydraulic Engineering Dalian University of TechnologyDalian 116024China Professor School of Civil and Hydraulic Engineering Dalian University of TechnologyDalian 116024ChinaDoctorLecturer

Based on research studies currently being carried out at Dalian University of Technology, some important aspects for the earthquake safety assessmcnt of concrete dams are reviewed and discussed. First, the rate-dependent behavior of concrcte subjected to earthquake loading is examined, emphasizing the properties of concrete under cyclic and biaxial loading conditions. Second, a modified four-parameter Hsieh-Ting-Chen viscoplastic consistency model is developed to simulate the rate-dependent behavior of concrete. The earthquake response of a 278m high arch dam is analyzed, and the results show that the strain-rate effects become noticeable in the inelastic range, Third, a more accurate non-smooth Newton algorithm for the solution of three-dimensional frictional contact problems is developed to study the joint opening effects of arch dams during strong earthquakes. Such effects on two nearly 300m high arch dams have been studied. It was found that the canyon shape has great influence on the magnitude and distribution of the joint opening along the dam axis. Fourth, the scaled boundary finite element method presented by Song and Wolf is employed to study the dam-reservoir-foundation interaction effects of concrete dams. Particular emphases were placed on the variation of foundation stiffness and the anisotropic behavior of the foundation material on the dynamic response of concrete dams. Finally, nonlinear modeling of concrete to study the damage evolution of concrete dams during strong earthquakes is discussed. An elastic-damage mechanics approach for damage prediction of concrete gravity dams is described as an example. These findings are helpful in understanding the dynamic behavior of concrete dams and promoting the improvement of seismic safety assessment methods.

关键词： arch dam gravity dam earthquake safety dynamic behavior of concrete strain-rate effect joint-opening effect dam-foundation interaction non-linear modeling

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Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm

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Petroleum 2017年第1期3卷 56-67页

作者： Veronica Chan Christine Chan Energy Informatics Laboratory Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S0A2Canada

This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system *** objective of the application is to enhance understanding of the intricate relationships among the key process *** algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network(ANN).The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach,in which accuracies of the generated predictive models are often not satisfactory,and the opaqueness of the ANN *** results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process *** analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO_(2) production rate are the steam flow rate through reboiler,reboiler pressure,and the CO_(2) concentration in the flue gas.

关键词： Carbon dioxide capture process system Artificial neural network Rule extractions non-linear modeling linear regression

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