scholarly journals Probabilistic Modelling using Monte Carlo Simulation for Incorporating Uncertainty in Least Cost Path Results: a Postdictive Roman Road Case Study

2020 ◽  
Author(s):  
Joseph Lewis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Input Data ◽  
Graphical Representation ◽  
Study Finding ◽  
Least Cost Path ◽  
True Location ◽  
Vertical Error ◽  
Modelling Process

The movement of past peoples in the landscape has been studied extensively through the use of Least Cost Path (LCP) analysis. Although methodological issues of applying LCP analysis in Archaeology have frequently been discussed, the effect of vertical error in the DEM on LCP results has not been fully assessed. This research proposes the use of Monte Carlo simulation as a method for incorporating and propagating the effects of vertical error on LCP results. Random error fields representing the vertical error of the DEM are calculated and incorporated into the documented and reproducible LCP modelling process using the R package leastcostpath. By incorporating vertical error into the LCP modelling process the accuracy of the LCP results can be understood probabilistically, with the likelihood of obtaining an LCP result quantified. Furthermore, the effect of incorporating vertical error on the LCP results can be expressed through the use of probabilistic LCPs, allowing for a graphical representation of the uncertainty in the LCP calculation, as well as identifying the most probable location of the ‘true’ least cost path. The method of understanding LCP results probabilistically is applied to a Roman road case study, finding that the accuracy of the LCP from south-to-north without incorporating vertical error is not representative of the LCP population with vertical error accounted for. In contrast, the accuracy of the LCP without incorporating vertical error from north-to-south is representative of the LCP population. The use of probabilistic LCPs suggests that the location of the Roman road in the southern section of the study area was selected to minimise the time taken to move up and down slope, irrespective of the direction of movement. However, the identification of two corridors of similar likelihood of containing the ‘true’ location of the LCP in the northern section when modelling movement south-to-north suggests that the input data and parameters used in the LCP analysis are unable to discern which corridor contains the most probable ‘true’ location of the LCP. Therefore, this research suggests that different input data and parameters are used and tested.

scholarly journals Probabilistic Modelling for Incorporating Uncertainty in Least Cost Path Results: a Postdictive Roman Road Case Study

2021 ◽  
Author(s):  
Joseph Lewis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Input Data ◽  
Study Finding ◽  
R Package ◽  
Least Cost Path ◽  
Vertical Error ◽  
The Impact ◽  
Modelling Process

AbstractThe movement of past peoples in the landscape has been studied extensively through the use of least cost path (LCP) analysis. Although methodological issues of applying LCP analysis in archaeology have frequently been discussed, the effect of DEM error on LCP results has not been fully assessed. Due to this, the reliability of the LCP result is undermined, jeopardising how well the method can confidently be used to model past movement. To strengthen the reliability of LCP results, this research proposes the use of Monte Carlo simulation as a method for incorporating and propagating the effects of error on LCP results. Focusing on vertical error, random error fields are calculated and incorporated into the documented and reproducible LCP modelling process using the R package leastcostpath. By graphically communicating the impact of vertical error using probabilistic LCPs, uncertainty in the results can be taken into account when interpreting LCPs. The method is applied to a Roman road case study, finding that the incorporation of vertical error results in the identification of multiple ‘least cost’ routes within the landscape. Furthermore, the deviation between the roman road and the probabilistic LCP suggests that the location of the roman road was influenced by additional factors other than minimising energy expenditure. This research finds that the probabilistic LCP derived using Monte Carlo simulation is a viable method for the graphical communication of the uncertainty caused by error within the input data used within the LCP modelling process. Therefore, it is recommended that probabilistic LCPs become the default approach when modelling movement using input data that contains errors.

Monte Carlo Simulation of Texture and Microstructure Transformation during Annealing of Steel

2007 ◽  
Vol 129 ◽  
pp. 83-87
Author(s):  
Hua Long Li ◽  
Jong Tae Park ◽  
Jerzy A. Szpunar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Grain Boundary ◽  
Computer Model ◽  
Graphical Representation ◽  
Structural Characteristics ◽  
Polycrystalline Materials ◽  
Virtual Experiments ◽  
Grain Boundary Character ◽  
Simulation Based

Controlling texture and microstructure evolution during annealing processes is very important for optimizing properties of steels. Theories used to explain annealing processes are complicated and always case dependent. An recently developed Monte Carlo simulation based model offers an effective tool for studying annealing process and can be used to verify the arbitrarily defined theories that govern such processes. The computer model takes Orientation Image Microscope (OIM) measurements as an input. The abundant information contained in OIM measurement allows the computer model to incorporate many structural characteristics of polycrystalline materials such as, texture, grain boundary character, grain shape and size, phase composition, chemical composition, stored elastic energy, and the residual stress. The outputs include various texture functions, grain boundary and grain size statistics that can be verified by experimental results. Graphical representation allows us to perform virtual experiments to monitor each step of the structural transformation. An example of applying this simulation to Si steel is given.

Application of life cycle assessment and Monte Carlo simulation for enabling sustainable product design

2014 ◽  
Vol 12 (3) ◽  
pp. 307-315 ◽  
Author(s):  
Sekar Vinodh ◽  
Gopinath Rathod
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Product Design ◽  
Sustainable Product Design ◽  
Content Type ◽  
Sustainable Product ◽  
Manufacturing Organization

Purpose – The purpose of this paper is to present an integrated technical and economic model to evaluate the reusability of products or components. Design/methodology/approach – Life cycle assessment (LCA) methodology is applied to obtain the product’s environmental performance. Monte Carlo simulation is utilized for enabling sustainable product design. Findings – The results show that the model is capable of assessing the potential reusability of used products, while the usage of simulation significantly increases the effectiveness of the model in addressing uncertainties. Research limitations/implications – The case study has been conducted in a single manufacturing organization. The implications derived from the study are found to be practical and useful to the organization. Practical implications – The paper reports a case study carried out for an Indian rotary switches manufacturing organization. Hence, the model is practically feasible. Originality/value – The article presents a study that investigates LCA and simulation as enablers of sustainable product design. Hence, the contributions of this article are original and valuable.

Uncertainty Analysis and Monitoring of Earth Dam

2014 ◽  
Vol 580-583 ◽  
pp. 954-957
Author(s):  
Ling Qiang Yang ◽  
Rui Gao ◽  
Yan Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probabilistic Method ◽  
Finite Difference Methods ◽  
Earth Dam ◽  
Earth Dams ◽  
Program Code ◽  
Physical Behavior ◽  
Difference Methods

Monte Carlo simulation provides a probabilistic method to evaluate the physical behavior of earth dam. Therefore, the behavior could be got in a more realistic manner. Based on the theory, an innovative software program code is developed by combining the Monte Carlo and finite difference methods to predict the performance of earth dams after impounding. In order to assess the efficiency of the method, the case study of earth dam, located at Southeast of China, has been studied in detail. The performance of this dam is predicted and compared with the field monitoring by using the monitoring data. The results shows the robustness of the proposed method.

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