Seepage behavior assessment of earth-rock dams based on Bayesian network

Abstract

Seepage behavior assessment is an important part of the safety operation assessment of earth-rock dams, because of insufficient intelligent analysis of monitoring information, abnormal phenomena or measured values are often ignored or improperly processed. To improve the intelligent performance of the monitoring system, this article has established an assessment framework covering project quality, maintenance status, monitoring data analysis, and on-site inspection based on the relevant norms of seepage safety assessment of earth-rock dams and the expert survey scoring method, and the Leaky Noisy-OR Gate extended model were used to determine the probability of events, and the dynamic and static Bayesian networks used to assess the possibility of seepage failure of earth-rock dams and diagnose the most likely cause of failure. The function of static and dynamic Bayesian networks to assess the seepage behavior of earth-rock dams, abnormal measured values, and causes of anomalies can make up for the limitations of reservoir management personnel and monitoring system in seepage failure experience and seepage knowledge of earth-rock dams and enable better handling of abnormal phenomena and monitoring information, making the monitoring system more intelligent.

New Method for Measuring Diagnosis Indexes Based on Concept Lattices in Dam Health Diagnosis

“…At present, the measurement methods of diagnosis indexes are mainly divided into three categories: (1) subjective methods based on expert knowledge or engineering experience; the most commonly used is experts grading method [6], [7], [8]. This method show strong subjective randomness and may lead to uncoordinated or inconsistent grades with limitation of expert knowledge.…”

Enhanced Earth and Rockfill Dam Seepage Forecasting via an Integrated PLS-BO-BiLSTM Approach: A Novel Model Incorporating Lag Effects and Optimization Algorithms

“…Developing the lag function and enhancing the predictive accuracy of security monitoring models has emerged as a challenging area of research. He et al (2021) employed both static and dynamic Bayesian networks for the analysis of seepage anomalies in earth and rockfill dams, addressing shortcomings in existing monitoring models. Shi et al (2020)developed a seepage safety monitoring, incorporating the lag effect through the use of a radial basis function neural network.…”

Novel propagation phenomena: behaviors of local trend imitation on multiple limited contact networks

“…The information is considered to have been adopted if the proportion of neighbors who have adopted it meets or surpasses the adoption threshold [10,11]. When the initial seed size is very tiny, the percolation theory can be used to estimate the proportion [12,13]. When the adoption threshold is fixed, the average degree scale change results in saddle point bifurcation, and with the increasing of network average degree, the final adoption scope increases continuously at first and then decreases discontinuously [14,15].…”