The accurate prediction of the bearing capacity of ring footings,which is crucial for civil engineering projects,has historically posed significant challenges.Previous research in this area has been constrained by con...The accurate prediction of the bearing capacity of ring footings,which is crucial for civil engineering projects,has historically posed significant challenges.Previous research in this area has been constrained by considering only a limited number of parameters or utilizing relatively small datasets.To overcome these limitations,a comprehensive finite element limit analysis(FELA)was conducted to predict the bearing capacity of ring footings.The study considered a range of effective parameters,including clay undrained shear strength,heterogeneity factor of clay,soil friction angle of the sand layer,radius ratio of the ring footing,sand layer thickness,and the interface between the ring footing and the soil.An extensive dataset comprising 80,000 samples was assembled,exceeding the limitations of previous research.The availability of this dataset enabled more robust and statistically significant analyses and predictions of ring footing bearing capacity.In light of the time-intensive nature of gathering a substantial dataset,a customized deep neural network(DNN)was developed specifically to predict the bearing capacity of the dataset rapidly.Both computational and comparative results indicate that the proposed DNN(i.e.DNN-4)can accurately predict the bearing capacity of a soil with an R2 value greater than 0.99 and a mean squared error(MSE)below 0.009 in a fraction of 1 s,reflecting the effectiveness and efficiency of the proposed method.展开更多
Ring footings are suitable for the structures like tall transmission towers, chimneys, silos and oil storages.These types of structures are susceptible to horizontal loads(wind load) in addition to their dead weight.I...Ring footings are suitable for the structures like tall transmission towers, chimneys, silos and oil storages.These types of structures are susceptible to horizontal loads(wind load) in addition to their dead weight.In the literature, very little or no effort has been made to study the effect of ring footing resting on reinforced sand when subjected to eccentric, inclined and/or eccentric-inclined loadings. This paper aims to study the behavior of ring footing resting on loose sand and/or compacted randomly distributed fiberreinforced sand(RDFS) when subjected to eccentric(0 B, 0.05 B and 0.1 B, where B is the outer diameter of ring footing), inclined(0°,5°,10°, 15°,-5°,-10° and-15°)and eccentric-inclined loadings by using a finite element(FE) software PLAXIS 3 D. The behavior of ring footing is studied by using a dimensionless factor called reduction factor(RF). The numerical model used in the PLAXIS 3 D has been validated by conducting model plate load tests. Moreover, an empirical expression using regression analysis has been presented which will be helpful in plotting a load-settlement curve for the ring footing.展开更多
为探讨鸡群不同栖息习惯可能影响鸡肉品质的相关性,本研究以智能电子栖架为载体,在高、中、低三个水平高度的栖杆上安装射频读写器,分别感应出在不同水平高度栖杆栖息的土鸡佩戴的定制NFC(Near field communication)脚环信息,应用物联...为探讨鸡群不同栖息习惯可能影响鸡肉品质的相关性,本研究以智能电子栖架为载体,在高、中、低三个水平高度的栖杆上安装射频读写器,分别感应出在不同水平高度栖杆栖息的土鸡佩戴的定制NFC(Near field communication)脚环信息,应用物联网传感与射频识别技术建立土鸡栖息位置实时监测系统,实时采集、监测土鸡活动过程的栖息位置与停留时间。研究结果表明,处于电子栖杆高、中、低及地面等四个水平位置的鸡胸肉,除甘氨酸、胱氨酸、亮氨酸外,其他测定成分无显著性差异;地面活动频率高的鸡群,甘氨酸、胱氨酸和亮氨酸三类氨基酸含量最高,在栖杆中间位置活跃的鸡群,甘氨酸、胱氨酸和亮氨酸三类氨基酸含量最低。通过测定不同栖息位置土鸡鸡胸肉的营养成分,建立土鸡栖息位置与鸡肉品质的关系模型,表明活动空间大且地面行走、运动量多的鸡群,可能通过活动改变体内风味物质的合成与代谢,使鸡肉品质更具风味。展开更多
文摘The accurate prediction of the bearing capacity of ring footings,which is crucial for civil engineering projects,has historically posed significant challenges.Previous research in this area has been constrained by considering only a limited number of parameters or utilizing relatively small datasets.To overcome these limitations,a comprehensive finite element limit analysis(FELA)was conducted to predict the bearing capacity of ring footings.The study considered a range of effective parameters,including clay undrained shear strength,heterogeneity factor of clay,soil friction angle of the sand layer,radius ratio of the ring footing,sand layer thickness,and the interface between the ring footing and the soil.An extensive dataset comprising 80,000 samples was assembled,exceeding the limitations of previous research.The availability of this dataset enabled more robust and statistically significant analyses and predictions of ring footing bearing capacity.In light of the time-intensive nature of gathering a substantial dataset,a customized deep neural network(DNN)was developed specifically to predict the bearing capacity of the dataset rapidly.Both computational and comparative results indicate that the proposed DNN(i.e.DNN-4)can accurately predict the bearing capacity of a soil with an R2 value greater than 0.99 and a mean squared error(MSE)below 0.009 in a fraction of 1 s,reflecting the effectiveness and efficiency of the proposed method.
文摘Ring footings are suitable for the structures like tall transmission towers, chimneys, silos and oil storages.These types of structures are susceptible to horizontal loads(wind load) in addition to their dead weight.In the literature, very little or no effort has been made to study the effect of ring footing resting on reinforced sand when subjected to eccentric, inclined and/or eccentric-inclined loadings. This paper aims to study the behavior of ring footing resting on loose sand and/or compacted randomly distributed fiberreinforced sand(RDFS) when subjected to eccentric(0 B, 0.05 B and 0.1 B, where B is the outer diameter of ring footing), inclined(0°,5°,10°, 15°,-5°,-10° and-15°)and eccentric-inclined loadings by using a finite element(FE) software PLAXIS 3 D. The behavior of ring footing is studied by using a dimensionless factor called reduction factor(RF). The numerical model used in the PLAXIS 3 D has been validated by conducting model plate load tests. Moreover, an empirical expression using regression analysis has been presented which will be helpful in plotting a load-settlement curve for the ring footing.
文摘为探讨鸡群不同栖息习惯可能影响鸡肉品质的相关性,本研究以智能电子栖架为载体,在高、中、低三个水平高度的栖杆上安装射频读写器,分别感应出在不同水平高度栖杆栖息的土鸡佩戴的定制NFC(Near field communication)脚环信息,应用物联网传感与射频识别技术建立土鸡栖息位置实时监测系统,实时采集、监测土鸡活动过程的栖息位置与停留时间。研究结果表明,处于电子栖杆高、中、低及地面等四个水平位置的鸡胸肉,除甘氨酸、胱氨酸、亮氨酸外,其他测定成分无显著性差异;地面活动频率高的鸡群,甘氨酸、胱氨酸和亮氨酸三类氨基酸含量最高,在栖杆中间位置活跃的鸡群,甘氨酸、胱氨酸和亮氨酸三类氨基酸含量最低。通过测定不同栖息位置土鸡鸡胸肉的营养成分,建立土鸡栖息位置与鸡肉品质的关系模型,表明活动空间大且地面行走、运动量多的鸡群,可能通过活动改变体内风味物质的合成与代谢,使鸡肉品质更具风味。
