scholarly journals Assessing the Economic Risk of Building Damage due to the Tunneling-Induced Settlement Using Monte Carlo Simulations and BIM

2020 ◽  
Vol 12 (23) ◽  
pp. 10034
Author(s):  
Stylianos Providakis ◽  
Chris D. F. Rogers ◽  
David N. Chapman
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Assessment Method ◽  
Building Damage ◽  
Analytical Models ◽  
Integrated Analysis ◽  
Economic Damage ◽  
Economic Risk ◽  
Advanced Analysis

Due to the increasing use of underground space to align with sustainability needs, geohazard risk assessments have become a valuable tool for decision-making. One common issue in relation to urban geohazard assessments relates to ground movements due to tunneling affecting adjacent buildings. A framework for assessing costs related to subsequent building damage, using integrated data, statistics and considering the uncertainties involved, is presented in this paper. The proposed methodology provides an integration of Monte Carlo simulations to support uncertainty estimations with an analysis for building-damage cost risk due to tunneling-induced settlements. The analysis involves analytical models using green-field conditions and a typically used building damage assessment method. BIM is capable of collating, combining and visualizing information with advanced analysis techniques into a risk-based tool. The resulting tool provides a clear way of assessing building-damage costs risk due to tunneling-induced settlements. This uses a BIM-based environment and incorporates 3D visualizations and an integrated analysis via MATLAB to reveal and highlight hazardous areas and the severity of economic risk along the tunneling route. This informs the need for additional ground investigations or secondary analyses to ensure engineering processes reduce or remove the risk of economic damage and advance sustainable decision-making.

scholarly journals Substitution Rates Under Stabilizing Selection

Genetics ◽  
1987 ◽  
Vol 116 (3) ◽  
pp. 479-486
Author(s):  
Alan Hastings
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Quantitative Traits ◽  
Strong Dependence ◽  
Phenotypic Selection ◽  
Analytical Models ◽  
Stabilizing Selection ◽  
Substitution Rates ◽  
Quantitative Characters ◽  
Selection For

ABSTRACT Allelic substitutions under stabilizing phenotypic selection on quantitative traits are studied in Monte Carlo simulations of 8 and 16 loci. The results are compared and contrasted to analytical models based on work of M. Kimura for two and "infinite" loci. Selection strengths of S = 4Nes approximately four (which correspond to reasonable strengths of selection for quantitative characters) can retard substitution rates tenfold relative to rates under neutrality. An important finding is a strong dependence of per locus substitution rates on the number of loci.

Robustness of the a b Signal Detection Theory Model of Decision Making

2007 ◽  
Vol 51 (18) ◽  
pp. 1263-1267
Author(s):  
Ernesto A. Bustamante ◽  
Brittany L. Anderson ◽  
Amy R. Thompson ◽  
James P. Bliss ◽  
Mark W. Scerbo
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Classical Model ◽  
Empirical Evaluation ◽  
Detection Theory ◽  
Theory Model ◽  
Extreme Responses

Bustamante, Fallon, and Bliss (2006) showed that the a b Signal Detection Theory (SDT) model was more parsimonious, generalizable, and applicable than the classical SDT model. Additionally, they demonstrated that both models provided statistically equivalent and uncorrelated measures of sensitivity and bias under ideal conditions. The purpose of this research was to show the robustness of the a b model for handling extreme responses. We conducted an empirical evaluation of operators' decision-making and two Monte Carlo simulations. Results from the empirical study showed that the a b model provided equivalent yet independent measures of decision-making accuracy and bias, whereas the classical model failed to provide independent measures in the presence of extreme responses. The Monte Carlo simulations showed a similar trend for the superiority of the a b model. Results from this research provide evidence to support the use of the a b model instead of the classical model.

scholarly journals Probabilistic Analysis of the Hard Rock Disintegration Process

10.14311/1041 ◽  
2008 ◽  
Vol 48 (4) ◽  
Author(s):  
K. Frydrýšek
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Hard Rock ◽  
Assessment Method ◽  
Contact Forces ◽  
Disintegration Process ◽  
Long Time ◽  
Structural Nonlinearities ◽  
Reaction Forces ◽  
Mathcad Software

This paper focuses on a numerical analysis of the hard rock (ore) disintegration process. The bit moves and sinks into the hard rock (mechanical contact with friction between the ore and the cutting bit) and subsequently disintegrates it. The disintegration (i.e. the stress-strain relationship, contact forces, reaction forces and fracture of the ore) is solved via the FEM (MSC.Marc/Mentat software) and SBRA (Simulation-Based Reliability Assessment) method (Monte Carlo simulations, Anthill and Mathcad software). The ore is disintegrated by deactivating the finite elements which satisfy the fracture condition. The material of the ore (i.e. yield stress, fracture limit, Young’s modulus and Poisson’s ratio), is given by bounded histograms (i.e. stochastic inputs which better describe reality). The results (reaction forces in the cutting bit) are also of stochastic quantity and they are compared with experimental measurements. Application of the SBRA method in this area is a modern and innovative trend in mechanics. However, it takes a long time to solve this problem (due to material and structural nonlinearities, the large number of elements, many iteration steps and many Monte Carlo simulations). Parallel computers were therefore used to handle the large computational needs of this problem. 

Adsorption and Condensation of SO2 in Double-Walled Carbon Nanotube Arrays Studied by Monte Carlo Simulations and Simple Analytical Models

2016 ◽  
Vol 120 (14) ◽  
pp. 7510-7521 ◽  
Author(s):  
Yong-Biao Yang ◽  
Mahshid Rahimi ◽  
Jayant K. Singh ◽  
Michael C. Böhm ◽  
Florian Müller-Plathe
Keyword(s):  
Monte Carlo ◽  
Carbon Nanotube ◽  
Monte Carlo Simulations ◽  
Analytical Models ◽  
Nanotube Arrays ◽  
Carbon Nanotube Arrays

scholarly journals Action Thresholds for Summer Fungicide Sprays and Sequential Classification of Apple Scab Incidence

Plant Disease ◽  
2009 ◽  
Vol 93 (5) ◽  
pp. 490-498 ◽  
Author(s):  
Odile Carisse ◽  
Catherine Meloche ◽  
Guy Boivin ◽  
Tristan Jobin
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Sequential Sampling ◽  
Apple Scab ◽  
Parameter Estimates ◽  
Ratio Test ◽  
Sample Number ◽  
Action Thresholds

The objectives of this study were to establish scab incidence thresholds at which summer fungicide sprays should be initiated and to develop sequential sampling for classification plans to facilitate decision making. The relationship between proportion of scabbed leaves per shoot and percent scabbed fruits at harvest was established based on data collected in both experimental and commercial orchards. Action threshold at which fungicide spray program should be initiated in order to maintain fruit scab at harvest below 2% was established at 0.006 scabbed leaves per shoot; hence incidence thresholds of 0.005 and 0.01 scabbed leaves per shoot were evaluated. Sequential sampling for classification procedures (SSCP), using the Wald's sequential probability ratio test (SPRT), were developed based on a beta-binomial distribution and using parameter estimates of the binary power law. Monte Carlo simulations were used to establish the probability of classifying mean scab incidence as less than the action thresholds (pth = 0.005 and pth = 0.01). Operating characteristic (OC) and average sample number (ASN) curves were established for eight combinations of stop lines and error levels (α and β). Based on the results of the Monte Carlo simulations, four sets of stop lines (two for each pth threshold) were selected for further evaluation. Bootstrap simulations of 50 data sets indicated that both the OC and ASN curves for each of the four pairs of stop lines were similar to OC and ASN values determined by Monte Carlo simulation. When validated with data not used to construct the models, the SSCPs provided the appropriate classification in 98 and 95% of the simulations for the pth = 0.005 and pth = 0.01 (α = 0.05 and β = 0.10), respectively. The sequential sampling plans developed in this study should allow for rapid and accurate classification of the incidence of apple scab on leaves, and aid in sampling for summer scab management decision making.

scholarly journals EXPERT ESTIMATES AVERAGING BY CONSTRUCTING INTUITIONISTIC FUZZY TRIANGLES

2015 ◽  
Vol 20 (3) ◽  
pp. 409-421 ◽  
Author(s):  
Aleksandras Krylovas ◽  
Natalja Kosareva
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Fuzzy Numbers ◽  
Fuzzy Arithmetic ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Probability Matrices

The problem of ranking (sorting) of m alternatives is considered when experts evaluate each alternative according to k criteria. Functions of arithmetic and geometric averages are constructed for decision making. We present a generalization of this scheme when there are evaluation matrices of several experts and this information is aggregated in the form of triangular intuitionistic fuzzy numbers. Fuzzy triangles were constructed with different uncertainty levels, experts decision matrices and the number of experts varied from 2 to 5. Moreover, method for construction of experts decision probability matrices is proposed in the paper. Ranking results obtained by performing Monte Carlo simulations. Probabilities of errors are compared for arithmetic, geometric, fuzzy arithmetic and fuzzy geometric averages.

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