Dynamic optimization of compaction process for rockfill materials

An, Zaizhan; Liu, Tianyun; Zhang, Zhaosheng; Zhang, Qinglong; Huangfu, Zehua; Li, Qingbin

doi:10.1016/j.autcon.2019.103038

Cited by 37 publications

(6 citation statements)

References 24 publications

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“…In the hierarchical “study” layer, the results of measurements and forecasts are determined, which are made available to interested specialists, and are used for theoretical analysis using a mathematical model of the object for further adjustment of optimal operating modes. Based on the vibration compaction process model and the compaction quality comprehensive evaluation method, An et al [ 255 ] put forward a dynamic optimization method of compaction process for the rockfill materials, and realized the dynamic optimization of compaction parameters considering the overall optimal solution. Zhang et al [ 17 , 256 , 257 ] constructed an intelligent compaction control theory and an intelligent rolling compaction method for compaction quality control and assurance of earth–rock dams, which has certain application potential in the construction of highways, railways, and airports.…”

Section: Intelligent Control Compaction Methodsmentioning

confidence: 99%

A Review on Roller Compaction Quality Control and Assurance Methods for Earthwork in Five Application Scenarios

Zhang

Huangfu³

et al. 2022

Materials

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Successful quality control and quality assurance (QC/QA) of earthwork compaction is critical to the long-term performance of roads, railways, airports, dams, and embankments. The purpose of this paper is to provide insights into the current practice, existing problems, challenges, and future development trends of QC/QA methods from the perspective of bibliometrics and the development stage. A bibliometric analysis is presented. Through quantitative analysis of literature and qualitative analysis of the development stage, insights into the current research practices and future directions of QC/QA methods have been derived from the perspectives of literature, cluster analysis, classification, different types of QC/QA methods, conclusions, and recommendations. It is found that the current QC/QA methods can be roughly divided into conventional compaction, digital rolling compaction, automatic rolling compaction, and intelligent control compaction. Currently, QC/QA methods are mainly confronted with the issues of accurate detection of compaction quality, autonomous optimization and intelligent decision-making of compaction process, multi-machine coordination, QC/QA-related specification formulation, and process standardization. To address these issues, several critical potential research directions are further identified: comprehensive CCI measurement system; simple and realistic mathematical representation of the complex compaction dynamics; parallel computing and distributed management of multi-source heterogeneous data; standardized application workflow and the cost–benefit assessment in the context of the full life cycle; intelligent control theories, methods, and technologies of earthwork compaction based on multidisciplinary integration. The paper enables researchers to obtain a comprehensive understanding of QC/QA methods for earthwork compaction as well as the suggested solutions for future work.

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Section: Intelligent Control Compaction Methodsmentioning

confidence: 99%

A Review on Roller Compaction Quality Control and Assurance Methods for Earthwork in Five Application Scenarios

Zhang

Huangfu³

et al. 2022

Materials

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“…To ensure adequate quality control in embankment dam construction, the construction process can be divided into the following procedures: preparation of the dam surface, spreading of dam materials, compaction of dam materials, and quality inspection. In the last dozen years, numerous researchers [8][9][10][11][12][13] have developed real-time intelligent monitoring systems for each process of earth-rock dam construction, encompassing dam filling and rolling. Zhong et al [8] and Zhong et al [9] proposed real-time monitoring indexes and construction control standards for compaction quality in rolling process, and established a real-time monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Key technologies and future development trends of intelligent earth–rock dam construction

Wang,

Zhao,

Liu

et al. 2023

Journal of Intelligent Construction

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Keywordsartificial intelligence rolled earth-rock dam intelligent detection of gradation building information modeling (BIM) rapid assessment on compaction qualityThe rapid advancement of information technologies, such as cloud computing, internet of things (IoT), and big data, has significantly enhanced the intelligence level in dam construction. Intelligent monitoring systems for dam filling have garnered considerable attention from water conservancy and hydropower engineering units, owing to their capability for refined management and quality control of layered and compartmentalized construction processes. The construction of earth-rock dams has undergone a transformation, shifting from manual management of dam compaction equipment, layer management, and quality control to a comprehensive intelligent approach. This new approach encompasses finegrained control of unit engineering, dam material identification, unmanned driving technology, real-time assessment and monitoring of compaction quality, and early warning forecasting. This article presents a systematic overview of key technologies employed in the intelligent construction process of earth-rock dams. These technologies include the fine decomposition of building information modeling (BIM) models on web platforms, rapid identification of dam material particle size, the application of autonomous driving technology for intelligent dam filling, and real-time monitoring and evaluation of dam compaction characteristics. Additionally, the paper discusses the latest research results in the field, building upon existing technology progress, and concludes by exploring the future development trends of intelligent dam construction. The construction of intelligent earth-rock dams serve as a crucial reference and valuable lessons for the development and construction of high earth-rock dam projects.

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“…Cao et al (2021) employed an artificial neural network (ANN) model to predict the CMV value during compaction, and the results were in good coherence with measured data. Besides, An and colleagues (An et al, 2020; Zhang et al, 2019a) adopted sound compaction value as an index to evaluate the compaction quality of rockfill materials and proposed an optimization procedure to optimize the overall compaction process based on genetic algorithm. Nevertheless, the above methods are still immature and need further study before they can be applied in the industry.…”

Section: Introductionmentioning

confidence: 99%

A data-driven method for real-time compaction quality evaluation of a cement-stabilized base layer

Cao

Lam

et al. 2022

Advances in Structural Engineering

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Adequate and uniform compaction is essential for the safety and durability of a pavement structure. The current compaction quality control, which relies on limited spot tests data, cannot provide timely feedback about compaction quality. The main objective of this paper is to develop a novel data-driven method for compaction quality assessment of cement-stabilized base in a real-time manner. The basic idea is to consider the drum of the vibratory roller together with the underlying cement-stabilized base layer as a spring-mass-dashpot system. The compaction level of the base is connected to the stiffness of the system and to the corresponding natural frequencies. In this study, the vibration of the drum was monitored during operation. The fundamental mode natural frequency of the system at different compaction levels of the base was identified by the fast Bayesian fast Fourier transform method. The results indicate that the natural frequency of the vibratory system has a positive correlation with the degree of compaction (DOC) of the underlying cement-stabilized base layer. Therefore, a novel and practical method for on-site compaction quality assessment utilizing measured natural frequency is proposed in this paper. A data-driven classification method based on support vector machine (SVM) was developed to realize the compaction quality evaluation. The classification results show that the proposed method can recognize signal patterns corresponding to different DOCs. The proposed method combines vibration theory and machine learning to provide a technical reference for the research of continuous compaction control with vibratory rollers.

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Dynamic optimization of compaction process for rockfill materials

Cited by 37 publications

References 24 publications

A Review on Roller Compaction Quality Control and Assurance Methods for Earthwork in Five Application Scenarios

A Review on Roller Compaction Quality Control and Assurance Methods for Earthwork in Five Application Scenarios

Key technologies and future development trends of intelligent earth–rock dam construction

A data-driven method for real-time compaction quality evaluation of a cement-stabilized base layer

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