scholarly journals Adaptive Compaction Construction Simulation Based on Bayesian Field Theory

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5178
Author(s):  
Jun Zhang ◽  
Jia Yu ◽  
Tao Guan ◽  
Jiajun Wang ◽  
Dawei Tong ◽  
...  
Keyword(s):  
Field Theory ◽  
Input Parameter ◽  
Resource Planning ◽  
Absolute Error ◽  
Simulation Method ◽  
Earth Dam ◽  
Construction Simulation ◽  
Judgment Condition ◽  
The Impact ◽  
Adaptive Modelling

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.

scholarly journals Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

2020 ◽  
Vol 10 (7) ◽  
pp. 2588
Author(s):  
Abhishek Kumar ◽  
Syahrir Ridha ◽  
Tarek Ganet ◽  
Pandian Vasant ◽  
Suhaib Umer Ilyas
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Pressure Drop ◽  
Pressure Loss ◽  
Oil And Gas ◽  
Input Parameter ◽  
Absolute Error ◽  
Support Vector ◽  
Bayesian Neural Network ◽  
The Impact

Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel–Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

Stochastic simulation for determining the design flood of cascade reservoir systems

2012 ◽  
Vol 43 (1-2) ◽  
pp. 54-63 ◽  
Author(s):  
Baohong Lu ◽  
Huanghe Gu ◽  
Ziyin Xie ◽  
Jiufu Liu ◽  
Lejun Ma ◽  
...  
Keyword(s):  
Simulation Model ◽  
Stochastic Simulation ◽  
Southern China ◽  
Flow Simulation ◽  
Simulation Method ◽  
Simulation Approach ◽  
Design Flood ◽  
Design Values ◽  
Stochastic Simulation Model ◽  
The Impact

Stochastic simulation is widely applied for estimating the design flood of various hydrosystems. The design flood at a reservoir site should consider the impact of upstream reservoirs, along with any development of hydropower. This paper investigates and applies a stochastic simulation approach for determining the design flood of a complex cascade of reservoirs in the Longtan watershed, southern China. The magnitude of the design flood when the impact of the upstream reservoirs is considered is less than that without considering them. In particular, the stochastic simulation model takes into account both systematic and historical flood records. As the reliability of the frequency analysis increases with more representative samples, it is desirable to incorporate historical flood records, if available, into the stochastic simulation model. This study shows that the design values from the stochastic simulation method with historical flood records are higher than those without historical flood records. The paper demonstrates the advantages of adopting a stochastic flow simulation approach to address design-flood-related issues for a complex cascade reservoir system.

scholarly journals Erosion Transportation Processes as Influenced by Gully Land Consolidation Projects in Highly Managed Small Watersheds in the Loess Hilly–Gully Region, China

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1540
Author(s):  
Qianqian Ji ◽  
Zhe Gao ◽  
Xingyao Li ◽  
Jian’en Gao ◽  
Gen’guang Zhang ◽  
...  
Keyword(s):  
Similarity Theory ◽  
Design Method ◽  
Simulation Method ◽  
Practical Significance ◽  
Land Consolidation ◽  
Small Watershed ◽  
Flood Peak ◽  
Erosion Area ◽  
The Impact ◽  
Erosion Environment

The Loess Hilly–Gully region (LHGR) is the most serious soil erosion area in the world. For the small watershed with high management in this area, the scientific problem that has been paid attention to in recent years is the impact of the land consolidation project on the erosion environment in the gully region. In this study, the 3D simulation method of vegetation, eroded sediment and pollutant transport was innovated based on the principles of erosion sediment dynamics and similarity theory, and the impacts of GLCP were analyzed on the erosion environment at different scales. The verification results show that the design method and the scale conversion relationship (geometric scale: λl = 100) were reasonable and could simulate the transport process on the complex underlying surface of a small watershed. Compared with untreated watersheds, a significant change was the current flood peak lagging behind the sediment peak. There were two important critical values of GLCP impact on the erosion environment. The erosion transport in HMSW had no change when the proportion was less than 0.85%, and increased obviously when it was greater than 3.3%. The above results have important theoretical and practical significance for watershed simulation and land-use management in HMSW.

scholarly journals Exoplanet mass estimation for a sample of targets for the Ariel mission

2021 ◽  
Author(s):  
J. R. Barnes ◽  
C. A. Haswell
Keyword(s):  
Radial Velocity ◽  
Input Parameter ◽  
Noise Sources ◽  
Time Commitment ◽  
Low Mass ◽  
Time Required ◽  
Fixed Input ◽  
The Impact ◽  
Quadrature Sampling

AbstractAriel’s ambitious goal to survey a quarter of known exoplanets will transform our knowledge of planetary atmospheres. Masses measured directly with the radial velocity technique are essential for well determined planetary bulk properties. Radial velocity masses will provide important checks of masses derived from atmospheric fits or alternatively can be treated as a fixed input parameter to reduce possible degeneracies in atmospheric retrievals. We quantify the impact of stellar activity on planet mass recovery for the Ariel mission sample using Sun-like spot models scaled for active stars combined with other noise sources. Planets with necessarily well-determined ephemerides will be selected for characterisation with Ariel. With this prior requirement, we simulate the derived planet mass precision as a function of the number of observations for a prospective sample of Ariel targets. We find that quadrature sampling can significantly reduce the time commitment required for follow-up RVs, and is most effective when the planetary RV signature is larger than the RV noise. For a typical radial velocity instrument operating on a 4 m class telescope and achieving 1 m s−1 precision, between ~17% and ~ 37% of the time commitment is spent on the 7% of planets with mass Mp < 10 M⊕. In many low activity cases, the time required is limited by asteroseismic and photon noise. For low mass or faint systems, we can recover masses with the same precision up to ~3 times more quickly with an instrumental precision of ~10 cm s−1.

scholarly journals Sustainability of Extraction of Raw Material by a Combination of Mobile and Stationary Mining Machines and Optimization of Machine Life Cycle

2020 ◽  
Vol 12 (24) ◽  
pp. 10454
Author(s):  
Katarína Teplická ◽  
Martin Straka
Keyword(s):  
Life Cycle ◽  
Raw Materials ◽  
Simulation Method ◽  
Raw Material ◽  
Optimal Distribution ◽  
Transport Costs ◽  
Scientific Discussion ◽  
Mining Companies ◽  
Conveyor Belts ◽  
The Impact

This article summarizes the arguments within the scientific discussion on the issue of using mining machines and their life cycle. The main goal of the article is to investigate the impact of a combination of mobile and stationary mining machines and their optimal distribution in the mining process to increase the efficiency of mining and processing of raw materials. The following methods of research were focused on the use of technical indicators for the valuation efficiency of the mining process: a simulation method was used for the distribution of mining machines, comparison analysis was used for the real and past state of mining machines, and a decision tree was used as managerial instrument for optimal alternatives of mining machines. The research empirically confirms and theoretically proves that optimal distribution of mining machines and machine parks is very important for mining companies. The benefit of this research for the mining company was the new location of the machines and the combination of stationary production lines and mobile equipment. The optimal layout of the machines reduced the number of conveyor belts and improved the transfer of limestone processing to mobile devices, saving time, which was reflected in transport costs. The results can be useful for other mining companies seeking to create an optimal machine park.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

Quantifying parametric uncertainty in the Rothermel model

2008 ◽  
Vol 17 (5) ◽  
pp. 638 ◽  
Author(s):  
Edwin Jimenez ◽  
M. Yousuff Hussaini ◽  
Scott Goodrick
Keyword(s):  
Variance Reduction ◽  
Input Parameter ◽  
Parametric Uncertainty ◽  
Fire Spread ◽  
The United States ◽  
Linear System Of Equations ◽  
Spread Model ◽  
Non Linear System ◽  
The Impact ◽  
Sensitivity Derivative

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a non-linear system of equations that relates environmental variables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. This model predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effective wind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploits sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled with traditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, which implies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficient method to quantify the impact of input uncertainties on the output parameters.

The Influence of Underpass on Surface Subsidence Caused by Double-Tube Shield Tunneling

2014 ◽  
Vol 915-916 ◽  
pp. 108-113
Author(s):  
Wei Kai Zong
Keyword(s):  
Simulation Method ◽  
Surface Subsidence ◽  
Channel Width ◽  
Ground Movement ◽  
Shield Tunnel ◽  
Underground Structures ◽  
Shield Tunneling ◽  
Underground Channel ◽  
Underground Passage ◽  
The Impact

Shield construction will cause surface subsidence, and the presence of underground structures above the tunnel has an impact on surface subsidence. Based on this, with the engineering of undercross shield tunnel construction on railway station as background, used numerical simulation method to analyze the effect of surface subsidence of underground passage, and studied the influence of depth and width of underpasses on ground movement induced. The results show that: The impact of the underground passage to the wire surface subsidence caused by the shield cannot be ignored. Surface subsidence caused by double shield will be decreased because of the existence of the underground passage, and that related to the channel depth and width. The greater the depth of underground channel, the greater the surface subsidence; greater the underground channel width, the smaller surface subsidence. Meanwhile, the surface subsidence trough width and the largest settlement scope unrelated to the depth of underground tunnels but the underground channel width, and increases with the increasing of the underpass width.

Do cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era?

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaojun Yao ◽  
Masoumeh Azma
Keyword(s):  
Human Resources ◽  
Human Resource ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Equation Modeling ◽  
Planning Systems ◽  
Privacy And Security ◽  
Content Type ◽  
Enterprise Resource Planning Systems ◽  
The Impact

PurposeThis study aims to investigate the impact of skills and knowledge of employees, economic situations of the company, current IT infrastructure, payment fashion, cloud availability, and cloud privacy and security on the productivity of the human resources in the COVID-19 era.Design/methodology/approachOver the past few years, the advent of cloud-assisted technologies has dramatically advanced the Information Technology (IT)-based industries by providing everything as a service. Cloud computing is recognized as a growing technology among companies around the world. One of the most critical cloud applications is deploying systems and organizational resources, especially systems whose deployment costs are high. Manpower is one of the basic and vital resources of the organization, and organizations need an efficient workforce to achieve their goals. But, in the COVID-19 era, human resources' productivity can be reduced due to stress, high labor force, reduced organizational performance and profits, unfavorable organizational conditions, inability to manage and lack of training. Therefore, this study tries to investigate the productivity of human resources in the COVID-19 era. Data were collected from the medium-sized companies through a questionnaire. Distributed questionnaires were conducted on the Likert scale. The model is assessed using the structural equation modeling technique to examine its reliability and validity. The study is a library method and literature review. A case study was conducted through a questionnaire and statistical analysis by SPSS 25 and SMART-PLS.FindingsBased on the findings, the skills and knowledge of employees, the economic situations of the company, payment fashion, cloud availability and the current IT infrastructures of the company have a positive impact on human resource efficiency in the COVID-19 era. But cloud privacy and security have a negative effect on the productivity of human resources. The findings can be the basis for companies and organizations in the COVID-19 era.Research limitations/implicationsThis study has some restrictions that need to be considered in evaluating the obtained results. First, due to the prevalence of Coronavirus, access to information from the companies under study was limited. Second, this research may have overlooked other variables that affect human resource productivity in the COVID-19 era. Prospective researchers can examine the impact of Customer Relationship Management (CRM) and Supply Chain Management (SCM) on the human resource's productivity in the COVID-19 era.Practical implicationsThe results of this research are applicable for all companies, their departments and human resources in the COVID-19 era.Originality/valueIn this paper, human resources' productivity in the COVID-19 era is pointed out. The presented new model provides a complete framework for investigating cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era.

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