The present paper first investigates the collapse behavior of a conventional pipe-framed greenhouse under snow loading based on a 3-D finite element analysis,in which both geometrical and material non-linearities are ...The present paper first investigates the collapse behavior of a conventional pipe-framed greenhouse under snow loading based on a 3-D finite element analysis,in which both geometrical and material non-linearities are considered.Three snow load distribution patterns related to the wind-driven snow particle movement are used in the analysis.It is found that snow load distribution affects the deformation and collapse behavior of the pipe-framed greenhouse significantly.The results obtained in this study are consistent with the actual damage observed.Next,discussion is made of the effects of reinforcements by adding members to the basic frame on the strength of the whole structure,in which seven kinds of reinforcement methods are examined.A buckling analysis is also carried out.The results indicate that the most effective reinforcement method depends on the snow load distribution pattern.展开更多
To investigate the impact of building heat transfer on roof snow loads,roof snow loads and snow load thermal coefficients from 61 Chinese sites over a period of 50 years are simulated based on basic meteorological dat...To investigate the impact of building heat transfer on roof snow loads,roof snow loads and snow load thermal coefficients from 61 Chinese sites over a period of 50 years are simulated based on basic meteorological data such as temperature,humidity,wind speed,and precipitation,and a multi-layer snowmelt model considering the building heat transfer.Firstly,the accuracy of the multi-layer snowmelt model is validated using the data of observed ground snow load and roof snow melting tests.The relationship between meteorological conditions,snow cover characteristics,and thermal coefficients of snow loads in three representative sites is then studied.Furthermore,the characteristics of thermal coefficients in each zone are analyzed by combining them with the statistical results of meteorological data from 1960 to 2010,and the equations of thermal coefficients in different zones on indoor temperatures and roof heat transfer coefficients are fitted separately.Finally,the equations in this paper are compared with the thermal coefficients in the main snow load codes.The results indicate that the snowmelt model using basic meteorological data can effectively provide samples of roof snow loads.In the cold zone where the snow cover lasts for a long time and does not melt easily,the thermal coefficients of the snow loads on the heating buildings are lower than those in the warm zone due to the long-term influence of the heat from inside the buildings.Thermal coefficients are negatively correlated with indoor temperatures and roof heat transfer coefficients.When the indoor temperature is too low or the roof insulation is good,the roof snow load may exceed the ground snow load.The thermal coefficients for heated buildings in the main snow load codes are more conservative than those calculated in this paper,and the thermal coefficients for buildings with lower indoor temperatures tend to be smaller.展开更多
Extreme snow loads can collapse roofs.This load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sit...Extreme snow loads can collapse roofs.This load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sites,while the ground snow depth is frequently measured and recorded.A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth,precipitation data,wind speed,and air temperature.For the evaluation,the precipitation was clas sified as snowfall or rainfall according to the air temperature,the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch,and the effect of snow water loss was considered.The developed algorithm was applied and validated using data from57 meteorological stations located in the northeastern region of China.The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements.The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code.The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load.Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.展开更多
为提高综合能源系统(integrated energy system,IES)多元负荷预测的精确度,综合考虑多能源相互作用机理、多元负荷耦合特性及气象因素相关性,提出了一种基于多尺度特征提取的IES多元负荷短期联合预测方法。首先,通过最大互信息系数(maxi...为提高综合能源系统(integrated energy system,IES)多元负荷预测的精确度,综合考虑多能源相互作用机理、多元负荷耦合特性及气象因素相关性,提出了一种基于多尺度特征提取的IES多元负荷短期联合预测方法。首先,通过最大互信息系数(maximum information coefficient,MIC)研究多元负荷耦合特性及影响因素相关性,选择预测特征;其次,利用变分模态分解技术(variational mode decomposition,VMD)对输入特征进行分解,提升特征纯洁度;最后,采用卷积神经网络-双向长短期记忆神经网络(convolutional neural network-bidirectional long and short-term memory,CNN-BiLSTM)多任务学习模型进行纵向、横向特征选择,注意力(Attention)机制对重要特征差异化提取,实现多尺度特征提取,并利用雪消融优化器(snow ablation optmizer,SAO)对VMD和CNN-BiLSTM多任务学习模型进行超参数优化,以此实现IES多元负荷的联合预测。以美国亚利桑那州实测数据进行实验,结果表明,无论与单一预测方法还是与其他模型相比,所提联合预测方法的均方根误差更低、准确率更高,在IES多元负荷预测中具有更高的精确性和鲁棒性。展开更多
基金financially supported by the Steel Structure Research and Education Promotion Project of the Japan Iron and Steel Federation in FY2016.
文摘The present paper first investigates the collapse behavior of a conventional pipe-framed greenhouse under snow loading based on a 3-D finite element analysis,in which both geometrical and material non-linearities are considered.Three snow load distribution patterns related to the wind-driven snow particle movement are used in the analysis.It is found that snow load distribution affects the deformation and collapse behavior of the pipe-framed greenhouse significantly.The results obtained in this study are consistent with the actual damage observed.Next,discussion is made of the effects of reinforcements by adding members to the basic frame on the strength of the whole structure,in which seven kinds of reinforcement methods are examined.A buckling analysis is also carried out.The results indicate that the most effective reinforcement method depends on the snow load distribution pattern.
基金the National Natural Science Foundation of China(52078380)。
文摘To investigate the impact of building heat transfer on roof snow loads,roof snow loads and snow load thermal coefficients from 61 Chinese sites over a period of 50 years are simulated based on basic meteorological data such as temperature,humidity,wind speed,and precipitation,and a multi-layer snowmelt model considering the building heat transfer.Firstly,the accuracy of the multi-layer snowmelt model is validated using the data of observed ground snow load and roof snow melting tests.The relationship between meteorological conditions,snow cover characteristics,and thermal coefficients of snow loads in three representative sites is then studied.Furthermore,the characteristics of thermal coefficients in each zone are analyzed by combining them with the statistical results of meteorological data from 1960 to 2010,and the equations of thermal coefficients in different zones on indoor temperatures and roof heat transfer coefficients are fitted separately.Finally,the equations in this paper are compared with the thermal coefficients in the main snow load codes.The results indicate that the snowmelt model using basic meteorological data can effectively provide samples of roof snow loads.In the cold zone where the snow cover lasts for a long time and does not melt easily,the thermal coefficients of the snow loads on the heating buildings are lower than those in the warm zone due to the long-term influence of the heat from inside the buildings.Thermal coefficients are negatively correlated with indoor temperatures and roof heat transfer coefficients.When the indoor temperature is too low or the roof insulation is good,the roof snow load may exceed the ground snow load.The thermal coefficients for heated buildings in the main snow load codes are more conservative than those calculated in this paper,and the thermal coefficients for buildings with lower indoor temperatures tend to be smaller.
基金Financial support from the National Natural Science Foundation of China(Grant Nos.51808169 and 51927813)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.2020083)are gratefully acknowledged.
文摘Extreme snow loads can collapse roofs.This load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sites,while the ground snow depth is frequently measured and recorded.A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth,precipitation data,wind speed,and air temperature.For the evaluation,the precipitation was clas sified as snowfall or rainfall according to the air temperature,the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch,and the effect of snow water loss was considered.The developed algorithm was applied and validated using data from57 meteorological stations located in the northeastern region of China.The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements.The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code.The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load.Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.
文摘为提高综合能源系统(integrated energy system,IES)多元负荷预测的精确度,综合考虑多能源相互作用机理、多元负荷耦合特性及气象因素相关性,提出了一种基于多尺度特征提取的IES多元负荷短期联合预测方法。首先,通过最大互信息系数(maximum information coefficient,MIC)研究多元负荷耦合特性及影响因素相关性,选择预测特征;其次,利用变分模态分解技术(variational mode decomposition,VMD)对输入特征进行分解,提升特征纯洁度;最后,采用卷积神经网络-双向长短期记忆神经网络(convolutional neural network-bidirectional long and short-term memory,CNN-BiLSTM)多任务学习模型进行纵向、横向特征选择,注意力(Attention)机制对重要特征差异化提取,实现多尺度特征提取,并利用雪消融优化器(snow ablation optmizer,SAO)对VMD和CNN-BiLSTM多任务学习模型进行超参数优化,以此实现IES多元负荷的联合预测。以美国亚利桑那州实测数据进行实验,结果表明,无论与单一预测方法还是与其他模型相比,所提联合预测方法的均方根误差更低、准确率更高,在IES多元负荷预测中具有更高的精确性和鲁棒性。
