Research on monitoring bedload transport rate based on vibration signals
The accurate measurement of bedload transport rate presents a significant challenge in river dynamics due to the complex nature of bedload movement and the limitations of existing measuring instruments.Conventional direct measurement techniques fail to provide continuous long-term monitoring.To address this,an indirect bedload measurement method,capturing high-resolution vibration signals from bedload movement using an impact plate system equipped with vibration sensors was employed.After extracting the characteristic values of these vibration signals their relationship with bedload transport rate and flow rate was established.Artificial neural network algorithm was also incorporated to predict bedload transport rates effectively.The results reveal a strong correlation between the mean eigenvalue of the vibration signal and the bedload sediment transport rate.Meanwhile,the neural network performed optimally under medium flow conditions,with decreased performance under low and high flow conditions.The optimal input parameters have been found to be related to flow rate and changes in the riverbed.
vibration signalbedload transport rateindirect measurement methodartificial neural network
万方数据
维普
