Construction Phase Oriented Dynamic Simulation: Taking RCC Dam Placement Process as an Example

We, Hu; Zhong, Dexing; Wu, Binping; Zheng, Ling

doi:10.3846/jcem.2019.7948

Cited by 7 publications

(8 citation statements)

References 35 publications

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“…According to Chen W. C. et al’s study [ 28 ], the latest research of the Bayesian field theory approach,

as a defined real scalar field variable with a constraint is firstly adopted to reparametrize and normalize each candidate of

. Assume that the Bayesian prior distribution of

is denoted as

and can be abbreviated as

.…”

Section: Methodsmentioning

confidence: 99%

“…In Chen W.C. et al’s study [ 28 ], the importance resampling method is utilized during the sampling process of

. Based on the maximum posterior density estimation (MAP) method and Equation (9), the optimal estimate

can be obtained when the following is satisfied:

…”

Section: Methodsmentioning

confidence: 99%

“…[ 26 ] and Mueller P et al [ 27 ] are recommended. Recently, Hu W. et al [ 28 ] adopted the Dirichlet process mixture model to represent the probability distribution of construction simulation parameters. However, the hyper-parameters still need to be manually set.…”

Section: Introductionmentioning

confidence: 99%

“…[ 38 ] proposed a refined compaction construction simulation model for the first time from the point of view of simulating the movement of rollers. On this basis, Hu W. et al [ 28 ] proposed a construction phase-oriented dynamic simulation method for a roller compacted concrete dam placement project. However, as an important member of the state transition conditions, the condition of when the roller stops moving is set as to whether a fixed offset times (or a fixed number of compaction bands) has been reached.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Compaction Construction Simulation Based on Bayesian Field Theory

Zhang

Guan

et al. 2020

Sensors

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The compaction construction process is a critical operation in civil engineering projects. By establishing a construction simulation model, the compaction duration can be predicted to assist construction management. Existing studies have achieved adaptive modelling of input parameters from a Bayesian inference perspective, but usually assume the model as parametric distribution. Few studies adopt the nonparametric distribution to achieve robust inference, but still need to manually set hyper-parameters. In addition, the condition of when the roller stops moving ignores the impact of randomness of roller movement. In this paper, a new adaptive compaction construction simulation method is presented. The Bayesian field theory is innovatively adopted for input parameter adaptive modelling. Next, whether rollers have offset enough distance is used to determine the moment of stopping. Simulation experiments of the compaction process of a high earth dam project are demonstrated. The results indicate that the Bayesian field theory performs well in terms of accuracy and efficiency. When the size of roller speed dataset is 787,490, the Bayesian field theory costs only 1.54 s. The mean absolute error of predicted compaction duration reduces significantly with improved judgment condition. The proposed method can contribute to project resource planning, particularly in a high-frequency construction monitoring environment.

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“…According to Chen W. C. et al’s study [ 28 ], the latest research of the Bayesian field theory approach,

as a defined real scalar field variable with a constraint is firstly adopted to reparametrize and normalize each candidate of

. Assume that the Bayesian prior distribution of

is denoted as

and can be abbreviated as

.…”

Section: Methodsmentioning

confidence: 99%

“…In Chen W.C. et al’s study [ 28 ], the importance resampling method is utilized during the sampling process of

. Based on the maximum posterior density estimation (MAP) method and Equation (9), the optimal estimate

can be obtained when the following is satisfied:

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Compaction Construction Simulation Based on Bayesian Field Theory

Zhang

Guan

et al. 2020

Sensors

Self Cite

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show abstract

“…Roller compacted concrete (RCC) dam has become one of the most competitive dam types due to its fast construction speed, low cost, and strong adaptability [1][2][3]. However, the current research is mostly on the RCC dam vibration compaction mechanism from a macroperspective through physical means such as rolling tests or laboratory tests, which fails to consider the mesoscopic structure of concrete fully.…”

Section: Introductionmentioning

confidence: 99%

Mesoscopic Simulation Method for Uniaxial Compression Test of RCC Dam Material Based on DEM

Tang

2020

Mathematical Problems in Engineering

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The roller compacted concrete (RCC) dam has become one of the most competitive dam types due to its fast construction speed, low cost, and strong adaptability. However, the macroscale compaction test can hardly reflect the mesoscopic structure on the RCC’s rolling characteristics. According to the characteristics of RCC dam materials, a numerical discrete element method (DEM) is proposed in this paper, which is used to simulate the irregular shape and proportion of RCC aggregates. Moreover, a mesoscopic parameter inversion method based on the adaptive differential evolution (ADE) algorithm is proposed to enhance the efficiency of model contact parameters determination and overcome the inconvenience and time-consumption of conventional methods. Compared with the physical test, the simulation compression curve has good consistency with the physical test curve, and the proposed method can adequately reflect the physical and mechanical properties of RCC dam materials, which provides a basis for the subsequent research on the properties of RCC dam materials under different filling times.

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Enhanced semi‐supervised ensemble machine learning approach for earthwork construction simulation activity sequence automatically updating driven by weather data

Zhang

Peng

et al. 2022

Geological Journal

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Construction activity sequence changes are among the most crucial considerations in establishing construction simulation models. However, conventional simulation models are designed with a fixed sequence of simulation activities, which is unable to update automatically. Existing machine learning methods require abundant time for manual labelling and are unsuitable for construction data with high‐dimensional and heterogeneous characteristics. The motivation for this work is to develop a labour‐saving activity sequence classification model for earthwork construction simulation which realizes flexibly modifying simulation activities according to different weather conditions. Three heterogeneous semi‐supervised classifiers with complementary characteristics were ensembled to reduce the workload of manual labelling and enhance the generalization ability of activity sequence classification based on weather data. Furthermore, Dempster–Shafer‐based evidence reasoning improved by a security filtering mechanism was adopted to enhance the accuracy of semi‐supervised classification. The proposed enhanced ensemble semi‐supervised activity sequence classification model was embedded in an earthwork construction simulation model, which was evaluated in a case study of rockfill dam construction. The proposed classifier outperformed four common semi‐supervised methods and two common supervised methods in terms of accuracy and generalization ability. Additionally, the proposed simulation method outperformed conventional simulation methods in terms of the construction schedule, construction intensity and consistency of the simulated activity sequence with the true values by 65.55%, 28.47% and 88.15%, respectively.

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Construction Phase Oriented Dynamic Simulation: Taking RCC Dam Placement Process as an Example

Cited by 7 publications

References 35 publications

Adaptive Compaction Construction Simulation Based on Bayesian Field Theory

Adaptive Compaction Construction Simulation Based on Bayesian Field Theory

Mesoscopic Simulation Method for Uniaxial Compression Test of RCC Dam Material Based on DEM

Enhanced semi‐supervised ensemble machine learning approach for earthwork construction simulation activity sequence automatically updating driven by weather data

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