Article
Assessment and Feedback Control of Paving Quality of
Earth-Rock Dam Based on OODA Loop
Cheng Wang , Jiajun Wang * , Wenlong Chen , Jia Yu, Zheng Jiao and Hongling Yu
Citation: Wang, C.; Wang, J.; Chen,
W.; Yu, J.; Jiao, Z.; Yu, H. Assessment
and Feedback Control of Paving
Quality of Earth-Rock Dam Based on
OODA Loop. Sensors 2021, 21, 7756.
https://doi.org/10.3390/s21227756
Academic Editors: Kamil Krasuski
and Damian Wierzbicki
Received: 19 October 2021
Accepted: 17 November 2021
Published: 22 November 2021
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Attribution (CC BY) license (https://
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4.0/).
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China;
chengwtju@tju.edu.cn (C.W.); chenwenlong@tju.edu.cn (W.C.); yujia@tju.edu.cn (J.Y.); jiaozheng@tju.edu.cn (Z.J.);
yuhongling@tju.edu.cn (H.Y.)
* Correspondence: jiajun_2014_bs@tju.edu.cn; Tel.: +86-138-2180-8530
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.
Keywords:
paving; quality control; quality assessment; OODA loop; cellular learning automaton;
dynamic path planning; GNSS
1. Introduction
The paving operation is an important part of the construction process of earth-rock
dams [
1
]. Generally, bulldozers and dump trucks constitute the construction machinery
for the paving of earth-rock dams for meeting the tight construction schedule. The paving
work involves a series of varied operations such as cutting, carrying, spreading, and
simple grading. In each stage, there are specific requirements for the operation mode,
driving path, and paving parameters of bulldozers and dump trucks. Failure to meet
these requirements may result in unqualified paving results such as uneven paving, thus
affecting the compactness quality of the final earth-rock dam and reducing the service life
of the dam [
2
–
4
]. Therefore, the quality evaluation and feedback control of the bulldozer
paving operation is of great significance for ensuring the paving quality [5].
Levelling is a process of spreading soil or earth-rock materials evenly in the work
area. Effective levelling quality control plays a crucial role in increasing the productivity
of earth-rock dam construction [
5
]. At existing earth-rock dam construction sites, the
bulldozer is used for levelling work, which frequently comprises a series of operations
Sensors 2021, 21, 7756. https://doi.org/10.3390/s21227756 https://www.mdpi.com/journal/sensors
