Abstract

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.

Highlights

  • Thickness state transfer should satisfy the d = La − Lb the storehouse surface is divided into grids, and the real-time monitoring data are obtained by using cellular automata

  • Paving quality control is of great significance ensuring thebe construction quality of a 14 that the of thedam

  • Thiscontrol is because the dynamic method sets strategy for levelling a mound and and of paving qualitycontrol is proposed, and thea following are the main results obtained: leaving, such that the location and other information of the mound can be approximately

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Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. We propose a novel assessment and feedback control framework for feedback, lowthe productivity, and of high dependence improving levelling quality earth-rock dams. The. By specifying composition of each model that of the OODA loop, that,interOODA loop is athe closed-loop tactical concept comprises the we usedemonstrate of information in addition to being a very simple framework, it can be used to comprehensively and and action to optimise tactics in real time and includes observing, orienting, deciding, effectively evaluate the levelling quality during the construction process and provide acting [8]. Strate that, in addition to being a very simple framework, it can be used to comprehenThe contributions of this paper are the following: sively and effectively evaluate thetolevelling quality during the construction (1) The. OODA loop is used build a feedback framework that is appliedprocess to the and provide guidance for improvement levelling quality control.

Related Work
Research Framework
Cellular Learning Automaton
Relationship
OODA Framework Coupled with CLA
Dynamic Assessment
Feedback Control
Dynamic
Feedback
12. Real-time
10. Update Bulldozer ‘s location and the cell that it passed just now
Findings
Discussion
Conclusions and Future Research
Full Text
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