Minimum detectable activity for NaI(Tl) airborne γ-ray spectrometry based on Monte Carlo simulation

Gong, Chunhui; Zeng, Guoqiang; Ge, Liangquan; Tang, Xiaobin; Chengjun, Tan

doi:10.1007/s11431-014-5553-x

Cited by 15 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MC method generates random numbers from a given probability distribution on the basis of the probability and statistics theory [19]. These random numbers are then used as the inputs of the model to solve a multidimensional average valuation problem in complex space.…”

Section: Mcmc Methodsmentioning

confidence: 99%

Robustness analysis of underground powerhouse construction simulation based on Markov Chain Monte Carlo method

Zhong

et al. 2015

Sci. China Technol. Sci.

View full text Add to dashboard Cite

Scheduling is a major concern in construction planning and management, and current construction simulation research typically targets the shortest total duration. However, uncertainties are inevitable in actual construction, which may lead to discrepancies between the actual and planned schedules and increase the risk of total duration delay. Therefore, developing a robust construction scheduling technique is of vital importance for mitigating disturbance and improving completion probability. In the present study, the authors propose a robustness analysis method that involves underground powerhouse construction simulation based on the Markov Chain Monte Carlo (MCMC) method. Specifically, the MCMC method samples construction disturbances by considering the interrelationship between the states of parameters through a Markov state transition probability matrix, which is more robust and efficient than traditional sampling methods such as the Monte Carlo (MC) method. Additionally, a hierarchical simulation model coupling critical path method (CPM) and a cycle operation network (CYCLONE) is built, using which construction duration and robustness criteria can be calculated. Furthermore, a detailed measurement method is presented to quantize the robustness of underground powerhouse construction, and the setting model of the time buffer is proposed based on the MCMC method. The application of this methodology not only considers duration but also robustness, providing scientific guidance for engineering decision making. We analyzed a case study project to demonstrate the effectiveness and superiority of the proposed methodology. underground powerhouse, construction schedule, simulation model, MCMC method, robustness Citation: Zhong D H, Bi L, Yu J, et al. Robustness analysis of underground powerhouse construction simulation based on Markov Chain Monte Carlo method.

show abstract

Section: Mcmc Methodsmentioning

confidence: 99%

Robustness analysis of underground powerhouse construction simulation based on Markov Chain Monte Carlo method

Zhong

et al. 2015

Sci. China Technol. Sci.

View full text Add to dashboard Cite

show abstract

“…The required radius R, which was covered by gamma-rays in the air to reduce the density I to 0.3% of its initial value I 0 , mainly depended on the energy [15]. The 0.3% was selected arbitrarily here.…”

Section: Source Termmentioning

confidence: 99%

Spectrum correction algorithm for detectors in airborne radioactivity monitoring equipment NH-UAV based on a ratio processing method

Cao

Tang

Wang

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

View full text Add to dashboard Cite

“…Santos and Celestino (2008) utilized artificial neural networks (ANNs) to predict tunnel-induced settlement in the case study of the Säo Paulo subway construction. Gong et al (2014) conducted a site exploration and Monte Carlo simulation to study tunnel-induced settlement in clays. Wei and Yang (2018) predicted coal-mining induced ground settlement using an online sequential extreme learning machine.…”

Section: Introductionmentioning

confidence: 99%

Interval Prediction of Building Foundation Settlement Using Kernel Extreme Learning Machine

et al. 2022

View full text Add to dashboard Cite

Dynamic building foundation settlement subsidence threatens urban businesses and residential communities. In the temporal domain, building foundation settlement is often dynamic and requires real-time monitoring. Accurate quantification of the uncertainty of foundation settlement in the near future is essential to advanced risk management for buildings. Traditional models for predicting foundation settlement mostly utilize the point estimates approach, which provides a single value that can be close or distant from the actual one. However, such an estimation fails to quantify estimation uncertainties. The interval prediction, as an alternative, can provide a prediction interval for the ground settlement with high confidence bands. This study, proposes a lower upper bound estimation approach integrated with a kernel extreme learning machine to predict ground settlement levels with prediction intervals in the temporal domain. A revised objective function is proposed to further improve the interval prediction performance. In this study, the proposed method is compared to the artificial neural network and classical extreme learning machine. Building settlement data collected from Fuxing City, Liaoning Province in China was used to validate the proposed approach. The comparative results show that the proposed approach can construct superior prediction intervals for foundation settlement.

show abstract

Minimum detectable activity for NaI(Tl) airborne γ-ray spectrometry based on Monte Carlo simulation

Cited by 15 publications

References 6 publications

Robustness analysis of underground powerhouse construction simulation based on Markov Chain Monte Carlo method

Robustness analysis of underground powerhouse construction simulation based on Markov Chain Monte Carlo method

Spectrum correction algorithm for detectors in airborne radioactivity monitoring equipment NH-UAV based on a ratio processing method

Interval Prediction of Building Foundation Settlement Using Kernel Extreme Learning Machine

Contact Info

Product

Resources

About