scholarly journals ANALYSIS OF STOCHASTIC PROCESS TO MODEL SAFETY RISK IN CONSTRUCTION INDUSTRY

2021 ◽  
Vol 27 (2) ◽  
pp. 87-99
Author(s):  
Zhenhao Zhang ◽  
Wenbiao Li ◽  
Jianyu Yang
Keyword(s):  
Stochastic Process ◽  
First Passage Time ◽  
Markov Property ◽  
Prediction Method ◽  
Risk Process ◽  
Construction Safety ◽  
Time Interval ◽  
Normal Process ◽  
Safety Risk ◽  
Safety Accident

There are many factors leading to construction safety accident. The rule presented under the influence of these factors should be a statistical random rule. To reveal those random rules and study the probability prediction method of construction safety accident, according to stochastic process theory, general stochastic process, Markov process and normal process are respectively used to simulate the risk-accident process in this paper. First, in the general-random-process-based analysis the probability of accidents in a period of time is calculated. Then, the Markov property of the construction safety risk evolution process is illustrated, and the analytical expression of probability density function of first-passage time of Markov-based risk-accident process is derived to calculate the construction safety probability. In the normal-process-based analysis, the construction safety probability formulas in cases of stationary normal risk process and non-stationary normal risk process with zero mean value are derived respectively. Finally, the number of accidents that may occur on construction site in a period is studied macroscopically based on Poisson process, and the probability distribution of time interval between adjacent accidents and the time of the nth accident are calculated respectively. The results provide useful reference for the prediction and management of construction accidents.

Research Summary of Bridge and Tunnel Construction Safety Risk Evaluation Method

2012 ◽  
Vol 209-211 ◽  
pp. 1402-1405
Author(s):  
Cheng Tie Tang ◽  
Zhen Hao Zhang ◽  
Zhi Gang He
Keyword(s):  
Risk Evaluation ◽  
Evaluation Method ◽  
Construction Safety ◽  
Tunnel Construction ◽  
Tunnel Engineering ◽  
Main Subject ◽  
Construction Stage ◽  
Safety Risk ◽  
Safety Accident

The risk of bridge and tunnel engineering in construction stage is a main subject which must be faced by bridge and tunnel engineer. Risk evaluation can analyze the happening possibility of construction safety accident and the consequence and loss caused by the accident. The research summary of risk evaluation method of bridge and tunnel construction safety is generalized in this paper. The main methods of risk evaluation at present are extrapolated, and the quality of each method is analyzed. The word of this paper will provide favorable reference for further research on risk evaluation of construction safety of bridge and tunnel engineering.

Gaussian Process Approach to Spiking Neurons for Inhomogeneous Poisson Inputs

2001 ◽  
Vol 13 (12) ◽  
pp. 2763-2797 ◽  
Author(s):  
Ken-ichi Amemori ◽  
Shin Ishii
Keyword(s):  
Stochastic Process ◽  
Membrane Potential ◽  
Gaussian Process ◽  
Diffusion Approximation ◽  
Process Analysis ◽  
First Passage Time ◽  
Markov Property ◽  
Passage Time ◽  
Spiking Neuron Model ◽  
New Formulation

This article presents a new theoretical framework to consider the dynamics of a stochastic spiking neuron model with general membrane response to input spike. We assume that the input spikes obey an inhomogeneous Poisson process. The stochastic process of the membrane potential then becomes a gaussian process. When a general type of the membrane response is assumed, the stochastic process becomes a Markov-gaussian process. We present a calculation method for the membrane potential density and the firing probability density. Our new formulation is the extension of the existing formulation based on diffusion approximation. Although the single Markov assumption of the diffusion approximation simplifies the stochastic process analysis, the calculation is inaccurate when the stochastic process involves a multiple Markov property. We find that the variation of the shape of the membrane response, which has often been ignored in existing stochastic process studies, significantly affects the firing probability. Our approach can consider the reset effect, which has been difficult to deal with by analysis based on the first passage time density.

scholarly journals Fractional compound Poisson processes with multiple internal states

2018 ◽  
Vol 13 (1) ◽  
pp. 10 ◽  
Author(s):  
Pengbo Xu ◽  
Weihua Deng
Keyword(s):  
Stochastic Process ◽  
Random Walk ◽  
Waiting Time ◽  
Anomalous Diffusion ◽  
First Passage Time ◽  
Passage Time ◽  
Poisson Processes ◽  
First Passage ◽  
Internal States ◽  
Functional Distribution

For the particles undergoing the anomalous diffusion with different waiting time distributions for different internal states, we derive the Fokker-Planck and Feymann-Kac equations, respectively, describing positions of the particles and functional distributions of the trajectories of particles; in particular, the equations governing the functional distribution of internal states are also obtained. The dynamics of the stochastic processes are analyzed and the applications, calculating the distribution of the first passage time and the distribution of the fraction of the occupation time, of the equations are given. For the further application of the newly built models, we make very detailed discussions on the none-immediately-repeated stochastic process, e.g., the random walk of smart animals.

First-passage time for a particular stationary periodic Gaussian process

1976 ◽  
Vol 13 (01) ◽  
pp. 27-38 ◽  
Author(s):  
L. A. Shepp ◽  
D. Slepian
Keyword(s):  
Gaussian Process ◽  
Infinite Series ◽  
First Passage Time ◽  
Passage Time ◽  
Numerical Calculations ◽  
Time Interval ◽  
Two Dimensional ◽  
First Passage ◽  
Gaussian Density ◽  
First Passage Probability

We find the first-passage probability that X(t) remains above a level a throughout a time interval of length T given X(0) = x 0 for the particular stationary Gaussian process X with mean zero and (sawtooth) covariance P(τ) = 1 – α | τ |, | τ | ≦ 1, with ρ(τ + 2) = ρ(τ), – ∞ < τ < ∞. The desired probability is explicitly found as an infinite series of integrals of a two-dimensional Gaussian density over sectors. Simpler expressions are found for the case a = 0 and also for the unconditioned probability that X(t) be non-negative throughout [0, T]. Results of some numerical calculations are given.

scholarly journals Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

2019 ◽  
Vol 16 (9) ◽  
pp. 1511 ◽  
Author(s):  
Yikun Su ◽  
Shijing Yang ◽  
Kangning Liu ◽  
Kaicheng Hua ◽  
Qi Yao
Keyword(s):  
Decision Making ◽  
Safety Management ◽  
Construction Safety ◽  
Case Based Reasoning ◽  
Prevention Measures ◽  
Proposed Model ◽  
Unsafe Behavior ◽  
Safety Accident ◽  
Construction Safety Management ◽  
Case Based

Case-based reasoning (CBR) has been extensively employed in various construction management areas, involving construction cost prediction, duration estimation, risk management, tendering, bidding and procurement. However, there has been a dearth of research integrating CBR with construction safety management for preventing safety accidents. This paper proposes a CBR model which focuses on case retrieval and reuse to provide safety solutions for new problems. It begins with the identification of case problem attribute and solution attribute, the state of hazard is used to describe the problem attribute based on principles of people’s unsafe behavior and objective’s unsafe state. Frame-based knowledge representation method is adopted to establish the case database from dimensions of slot, facet and facet’s value. Besides, cloud graph method is introduced to determine the attribute weight through analyzing the numerical characteristics of expectation value, entropy value and hyper entropy value. Next, thesaurus method is employed to calculate the similarity between cases including word level similarity and sentence level similarity. Principles and procedures have been provided on case revise and case retain. Finally, a real-world case is conducted to illustrate the applicability and effectiveness of the proposed model. Considering the high potential for pre-control and decision-making of construction safety accident, the proposed model is expected to contribute safety managers to take decisions on prevention measures more efficiently.

scholarly journals Estimating Remaining Useful Life for Degrading Systems with Large Fluctuations

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Dang-Bo Du ◽  
Jian-Xun Zhang ◽  
Zhi-Jie Zhou ◽  
Xiao-Sheng Si ◽  
Chang-Hua Hu
Keyword(s):  
Stochastic Process ◽  
Standard Deviation ◽  
Health Management ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Threshold Function ◽  
Engineering Practice ◽  
Practical Case ◽  
Degradation Data ◽  
Useful Life

Remaining useful life (RUL) prediction method based on degradation trajectory has been one of the most important parts in prognostics and health management (PHM). In the conventional model, the degradation data are usually used for degradation modeling directly. In engineering practice, the degradation of many systems presents a volatile situation, that is, fluctuation. In fact, the volatility of degradation data shows the stability of system, so it could be used to reflect the performance of system. As such, this paper proposes a new degradation model for RUL estimation based on the volatility of degradation data. Firstly the degradation data are decomposed into trend items and random items, which are defined as a stochastic process. Then the standard deviation of the stochastic process is defined as another performance variable because standard deviation reflects the system performance. Finally the Wiener process and the normal stochastic process are used to model the trend items and random items separately, and then the probability density function (PDF) of the RUL is obtained via a redefined failure threshold function that combines the trend items and the standard deviation of the random items. Two practical case studies demonstrate that, compared with traditional approaches, the proposed model can deal with the degradation data with many fluctuations better and can get a more reasonable result which is convenient for maintenance decision.

scholarly journals Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Wen Tian ◽  
Huiqing Xu ◽  
Yixing Guo ◽  
Bin Hu ◽  
Yi Yao
Keyword(s):  
Traffic Congestion ◽  
Southern China ◽  
Probabilistic Method ◽  
Prediction Method ◽  
Weather Conditions ◽  
Error Distribution ◽  
Time Interval ◽  
Distribution Characteristics ◽  
Traffic Demand ◽  
Demand Prediction

In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management.

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