China
Africa
Explore chapters and articles related to this topic
Technological Development
Published in Edward Y. Uechi, Business Automation and Its Effect on the Labor Force, 2023
Technology aside, new methods and procedures have had to be developed to analyze data types that are not quantifiable. Probabilistic method is a technique that draws on probability theory. A computer that uses the probabilistic method evaluates a random variable against a set of defined conditions and gives a probability value to the random variable based on how well it matches the conditions. Another method draws on how neurons operate in the human brain. An artificial neural network (also known as a neural net) comprises nodes distinguished by input nodes, hidden nodes, and output nodes. Each node would hold a specific datum. A computer processes the nodes to find connections and the strengths of those connections among the various nodes. Machine learning, which can be divided into three areas (supervised learning, unsupervised learning, and reinforcement learning), provides a method for a computer to analyze different types of data to find a specific match or a pattern. Machine learning requires a massive amount of data, which the computer can base its conclusions on.
Risk level evaluation of transport of dangerous goods through road tunnels
Published in Maurizio Crispino, Pavement and Asset Management, 2019
C. Caliendo, M.L. De Guglielmo
Generally speaking, two main groups of approaches appertain to QRA, which are deterministic and probabilistic, respectively. However, the probabilistic method, compared to the deterministic one that can give accurate results only if the exact input parameter are known, is generally considered to be the best tool for taking into account the uncertainty associated with some parameters describing the process and for assessing long-term risk in more complex systems as tunnels. A probabilistic method involves the identification of hazards, the estimations of probability and consequences of each hazard, and quantifies the risk as the sum of probabilities multiplied by consequences. According to this approach, QRA includes event trees, faults trees and consequences estimation models. Individual risk (e.g. the probability of death per year for a specific person exposed to a risk), social risk (e.g. the expected number of fatalities in the tunnel per year) and estimates of damages (structural and environmental) are output of QRA. In this respect, the social risk is used in this paper and it is represented in the form of F/N curves.
Probabilistic structural modelling in parallel systems
Published in Alphose Zingoni, Insights and Innovations in Structural Engineering, Mechanics and Computation, 2016
M. Krejsa, R. Cajka, P. Janas, J. Brozovsky, V. Krejsa
Probabilistic methods are used in engineering where a computational model contains random variables (Major & Major 2007, Cajka & Krejsa 2014) and is defined target reliability level (Kotes & Vican 2013). Primary probability approaches are presented and developed for the modeling and analysis of uncertainty, and for evaluating the associated effects on safety and reliability. An important part in structural failure analysis is modeling and quantification of various sources of uncertainty. In structural theory of reliability there are analyzed aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty is related to the randomness of physical quantities (e.g., variability in yield strength of steel) and can be modeled as random variables in probabilistic form. Epistemic uncertainty is knowledge-based and is coming out from imperfection of the calculation model (discrepancy between behavior of real structure and its computational simplified representation in numerical model, e.g. FEM) or limited availability of random input data.
Reliability-based assessment of scour at pier in gravel beds
Published in ISH Journal of Hydraulic Engineering, 2023
Mohd Muzzammil, Javed Alam, Gaurav Gupta, Mohd Khalid
Because of the inherent uncertainties in many parameters, deterministic scour equations contain significant uncertainty due to unpredictable flow, sediment, structural and model parameters; consequently, pier safety should be maintained in a probabilistic sense. A reliability analysis could provide a numerical estimate of the pier’s scouring resistance. Reliability analysis is a probabilistic method based on the violation of the limit state function in any system during its entire life. The probabilistic assessment of scour depth at bridge foundation has been made by various researchers in the past (Johnson and Ayyub 1992; Chang et al. 1994; Johnson 1996; Yanmaz and Cicekdag 2001; Bolduc et al. 2008; Dordoni et al. 2010; Turan and Yanmaz 2011; Muzammil et al. 2006; Muzzammil and Siddiqui 2010; Muzammil and Siddiqui 2012; Liao et al. 2015, 2018; Khalid et al. 2019; Yilmaz et al. 2019).
Resilience metrics and measurement methods for transportation infrastructure: the state of the art
Published in Sustainable and Resilient Infrastructure, 2020
Wenjuan Sun, Paolo Bocchini, Brian D. Davison
Probabilistic methods use probabilistic distributions to describe uncertainties in different aspects, such as event, demand, damage state, recovery, and decision-making. Event uncertainties lie in occurrence frequency, location, and intensity (Bocchini et al., 2016, Chapter 2; Pitilakis et al., 2013a; Selva & Sandir, 2013). Event uncertainties can be incorporated in resilience analyses through probabilistic models (Duan et al., 2016). The variability of geometric, material, structural properties, and demand can be addressed by fragility analyses (Banerjee & Shinozuka, 2009; de Felice & Giannini, 2010; Gehl et al., 2009; Jeong & Elnashai, 2007; Jia et al., 2017; Karamlou & Bocchini, 2016b; Banerjee & Shinozuka, 2009; Kwon & Elnashai, 2009; Nielson & DesRoches, 2007; Padgett & DesRoches, 2007; Tsionis & Fardis, 2013). Based on the concept of fragility analyses, functionality–fragility surfaces, adding time-related information, can predict functionality recovery over time at different intensity levels in a probabilistic manner (Karamlou & Bocchini, 2017). Uncertainties in thresholds of damage states on capacity curves are usually handled through design codes (FEMA, 2010), expert judgments (Ghosh & Padgett, 2010), experimental data (Mackie & Stojadinovic, 2004; Perrault et al., 2013), analytical approaches and simulations (Azevedo et al., 2010; Banerjee & Shinozuka, 2008; Cho et al., 2002; Choi et al., 2004; Choi & Mahadevan, 2008; Nielson & DesRoches, 2007; Pinto et al., 2012; Wang et al., 2014). However, there are still debates on thresholds of damage states and parameters of fragility curves for structures of the same types (Trucco et al., 2013, Chapter 9).
Dynamic response analysis of a shaft-disk-drum rotor system with interval uncertainties
Published in Mechanics Based Design of Structures and Machines, 2023
Shengnan Zhao, Feibin Zhang, Zhaoye Qin, Fulei Chu
Probabilistic methods have been intensively studied and widely applied in the uncertain dynamics of rotor systems. A number of studies were done by Didier, Sinou, and Faverjon (2012a,2012b,2013), who used Fourier series and polynomial chaos expansion (PCE) to approximate the dynamic response based on the assumption that the uncertain parameters follow the Gaussian law. Dourado, Cavalini, and Steffen (2018) modeled the inherent uncertainties affecting rotor performance by employing stochastic and fuzzy logic based analysis. These methods have been compared through numerical simulations in terms of the dynamic behaviors of the system, which is represented by the rotor orbits, unbalance response and frequency response functions. Sinou, Didier, and Faverjon (2015) presented a method called the Stochastic Harmonic Balance Method (Stochastic-HBM) which was applied to a flexible non-linear rotor system, with random parameters modeled as random fields. A numerical analysis is performed to analyze the influence of uncertainties on the non-linear behavior of rotor system by using this method. Sinou and Jacquelin (2015) developed a stochastic harmonic balance method with a recursive procedure to evaluate the steady-state response of a rotor system with uncertain stiffness and asymmetric coupling that involves time-dependent terms. Gan et al. (2014) established a random matrix model about shaft-disk rotor and employed Monte Carlo method to analyze the sensitivity of the first order critical speed and vibration amplitude to uncertain parameters. It should be noted that probabilistic methods require a large amount of samples and are applicable only if the exact probability distributions of the parameters are given. However, it is often encountered that the complete statistic information on uncertain parameters is often not available or insufficient to obtain satisfactory preference functions. On the other hand, the ranges of variation of the parameters are more easily defined and can be modeled with unknown but bounded interval variables in practice. In these cases, non-probabilistic methods demonstrate great superiority.