The mechanical parameters of materials in a dam body and dam foundation tend to change when dams are reinforced in aging processes.It is important to use an early-warning index to reflect the safety status of dams,par...The mechanical parameters of materials in a dam body and dam foundation tend to change when dams are reinforced in aging processes.It is important to use an early-warning index to reflect the safety status of dams,particularly of heightened projects in the impoundment period.Herein,a new method for monitoring the safety status of heightened dams is proposed based on the deformation monitoring data of a dam structure,a statistical model,and finite-element numerical simulation.First,a fast optimization inversion method for estimation of dam mechanical parameters was developed,which used the water pressure component extracted from a statistical model,an improved inversion objective function,and a genetic optimization iterative algorithm.Then,a finite element model of a heightened concrete gravity dam was established,and the deformation behavior of the dam with rising water levels in the impoundment period was simulated.Subsequently,mechanical parameters of aged dam parts were calculated using the fast optimization inversion method with simulated deformation and the water pressure deformation component obtained by the statistical model under the same conditions of water pressure change.Finally,a new earlywarning index of dam deformation was constructed by means of the forward-simulated deformation and other components of the statistical model.The early-warning index is useful for forecasting dam deformation under different water levels,especially high water levels.展开更多
如何用定量分析的方法识别复杂网络中哪些节点最重要,或评价某个节点相对于其他一个或多个节点的重要程度,是复杂网络研究的热点问题.目前已有多种有效模型被提出用于识别网络重要节点.其中,引力模型将节点的核数(网络进行k-核分解时的k...如何用定量分析的方法识别复杂网络中哪些节点最重要,或评价某个节点相对于其他一个或多个节点的重要程度,是复杂网络研究的热点问题.目前已有多种有效模型被提出用于识别网络重要节点.其中,引力模型将节点的核数(网络进行k-核分解时的ks值)看作物体的质量,将节点间的最短距离看作物体间距离,综合考虑了节点局部信息和路径信息用于识别网络重要节点.然而,仅将节点核数表示为物体的质量考虑的因素较为单一,同时已有研究表明网络在进行k-核分解时容易将具有局部高聚簇特征的类核团节点识别为核心节点,导致算法不够精确.基于引力方法,综合考虑节点H指数、节点核数以及节点的结构洞位置,本文提出了基于结构洞引力模型的改进算法(improved gravity method based on structure hole method,ISM)及其扩展算法ISM_(+).在多个经典的实际网络和人工网络上利用SIR(susceptible-infected-recovered)模型对传播过程进行仿真,结果表明所提算法与其他中心性指标相比能够更好地识别复杂网络中的重要节点.展开更多
基金This work was supported by the National Key Research and Development Program of China(Grant No.2018YFC0407104)the National Natural Science Foundation of China(Grants No.52079049 and 51739003)+1 种基金the Central University Basic Research Project(Grant No.B200202160)the Water Science Project of Xinjiang(Grant No.YF 2020-05).
文摘The mechanical parameters of materials in a dam body and dam foundation tend to change when dams are reinforced in aging processes.It is important to use an early-warning index to reflect the safety status of dams,particularly of heightened projects in the impoundment period.Herein,a new method for monitoring the safety status of heightened dams is proposed based on the deformation monitoring data of a dam structure,a statistical model,and finite-element numerical simulation.First,a fast optimization inversion method for estimation of dam mechanical parameters was developed,which used the water pressure component extracted from a statistical model,an improved inversion objective function,and a genetic optimization iterative algorithm.Then,a finite element model of a heightened concrete gravity dam was established,and the deformation behavior of the dam with rising water levels in the impoundment period was simulated.Subsequently,mechanical parameters of aged dam parts were calculated using the fast optimization inversion method with simulated deformation and the water pressure deformation component obtained by the statistical model under the same conditions of water pressure change.Finally,a new earlywarning index of dam deformation was constructed by means of the forward-simulated deformation and other components of the statistical model.The early-warning index is useful for forecasting dam deformation under different water levels,especially high water levels.
文摘如何用定量分析的方法识别复杂网络中哪些节点最重要,或评价某个节点相对于其他一个或多个节点的重要程度,是复杂网络研究的热点问题.目前已有多种有效模型被提出用于识别网络重要节点.其中,引力模型将节点的核数(网络进行k-核分解时的ks值)看作物体的质量,将节点间的最短距离看作物体间距离,综合考虑了节点局部信息和路径信息用于识别网络重要节点.然而,仅将节点核数表示为物体的质量考虑的因素较为单一,同时已有研究表明网络在进行k-核分解时容易将具有局部高聚簇特征的类核团节点识别为核心节点,导致算法不够精确.基于引力方法,综合考虑节点H指数、节点核数以及节点的结构洞位置,本文提出了基于结构洞引力模型的改进算法(improved gravity method based on structure hole method,ISM)及其扩展算法ISM_(+).在多个经典的实际网络和人工网络上利用SIR(susceptible-infected-recovered)模型对传播过程进行仿真,结果表明所提算法与其他中心性指标相比能够更好地识别复杂网络中的重要节点.
