Assessment and Feedback Control of Paving Quality of Earth-Rock Dam Based on OODA Loop

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.

Robustness of the Storage in Cloud Data Centers Based on Simple Swarm Optimization Algorithm

Storing extensive data in cloud environments affects service quality, transmission speed, and access to information in systems, which is becoming a growing challenge. In storage improvement, reducing various costs and reducing the shortest path in the storage of distributed cloud data centers are among the important issues in the field of cloud computing. In this paper, particle swarm optimization (PSO) algorithm and learning automaton (LA) are used to minimize the cost of a data center, which includes communication, data transfer, and storage and optimization of communication between data centers. To improve storage in distributed data centers, a new model called LAPSO is proposed by combining LA and PSO, in which LA improves particle control by searching for particle speed and position. In this method, LA moves each particle in the direction where it has the best individual and group experiences. In multipeak problems, it does not fall into local optimums. Results of the experiments are shown on the dataset of spatial information and cadastre of country lands, which includes 13 data centers. The proposed method evaluates and improves the optimal position parameters, minimum route cost, distance, data transfer cost, storage cost, data communication cost, load balance, and access performance better than other methods.