As an important infrastructure project, the concrete gravity dam plays an extremely important role in hydropower generation, irrigation, flood control, and other aspects. Seepage is an important factor affecting the stability of concrete gravity dams. Seepage safety assessment is of great significance to the safe operation of the dams. However, the existing seepage safety assessment models are not dynamic, and the correlation among indicators is often neglected and the overall seepage safety of the concrete gravity dams has not been considered. To solve these problems, this research proposes a dynamic matter-element extension (D-MEE) model. First, the D-MEE model is established through adroit integration of the matter-element extension (MEE) model and functional data analysis (FDA). Second, a dynamic criteria importance through the intercriteria correlation (D-CRITIC) method that can effectively consider the correlation among indicators is proposed to determine the weights. Third, the influence of different dam blocks on the overall seepage safety status is considered by constructing a spatial weight matrix. Finally, the proposed method is applied to the concrete gravity dam X in southwest China. The results show that the proposed method is effective and superior to the existing evaluation methods of seepage safety.

Paving thickness and evenness are two key factors that affect the paving operation quality of earth-rock dams. However, in the recent study, both of the key factors characterising the paving quality were measured using finite point random sampling, which resulted in subjectivity in the detection and a lag in the feedback control. At the same time, the on-site control of the paving operation quality based on experience results in a poor and unreliable paving quality. To address the above issues, in this study, a novel assessment and feedback control framework for the paving operation quality based on the observe–orient–decide–act (OODA) loop is presented. First, in the observation module, a cellular automaton is used to convert the location of the bulldozer obtained by monitoring devices into the paving thickness of the levelling layer. Second, in the orient module, the learning automaton is used to update the state of the corresponding and surrounding cells. Third, in the decision module, an overall path planning method is developed to realise feedback control of the paving thickness and evenness. Finally, in the act module, the paving thickness and evenness of the entire work unit are calculated and compared to their control thresholds to determine whether to proceed with the next OODA loop. The experiments show that the proposed method can maintain the paving thickness less than the designed standard value and effectively prevent the occurrence of ultra-thick or ultra-thin phenomena. Furthermore, the paving evenness is improved by 21.5% as compared to that obtained with the conventional paving quality control method. The framework of the paving quality assessment and feedback control proposed in this paper has extensive popularisation and application value for the same paving construction scene.