Готові списки джерел за темами / Probabilistic finite state automata

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Добірка наукової літератури з теми "Probabilistic finite state automata"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 12 лютого 2022

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Статті в журналах з теми "Probabilistic finite state automata"

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Sánchez, Joan Andreu, Martha Alicia Rocha, Verónica Romero, and Mauricio Villegas. "On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models." Computational Linguistics 44, no. 1 (March 2018): 17–37. http://dx.doi.org/10.1162/coli_a_00306.

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Probabilistic finite-state automata are a formalism that is widely used in many problems of automatic speech recognition and natural language processing. Probabilistic finite-state automata are closely related to other finite-state models as weighted finite-state automata, word lattices, and hidden Markov models. Therefore, they share many similar properties and problems. Entropy measures of finite-state models have been investigated in the past in order to study the information capacity of these models. The derivational entropy quantifies the uncertainty that the model has about the probability distribution it represents. The derivational entropy in a finite-state automaton is computed from the probability that is accumulated in all of its individual state sequences. The computation of the entropy from a weighted finite-state automaton requires a normalized model. This article studies an efficient computation of the derivational entropy of left-to-right probabilistic finite-state automata, and it introduces an efficient algorithm for normalizing weighted finite-state automata. The efficient computation of the derivational entropy is also extended to continuous hidden Markov models.
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MACARIE, IOAN I. "A NOTE ON MULTIHEAD FINITE-STATE AUTOMATA." International Journal of Foundations of Computer Science 07, no. 04 (December 1996): 329–37. http://dx.doi.org/10.1142/s0129054196000233.

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We present connections among nondeterministic and one-sided-error probabilistic multihead finite-state automata. Several properties of logarithmic-space Turing machines follow from the more refined results that we prove in the setting of the corresponding multihead finite-state automata.
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3

Sánchez, Joan Andreu, and Verónica Romero. "Computation of moments for probabilistic finite-state automata." Information Sciences 516 (April 2020): 388–400. http://dx.doi.org/10.1016/j.ins.2019.12.052.

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Saboori, Anooshiravan, and Christoforos N. Hadjicostis. "Current-State Opacity Formulations in Probabilistic Finite Automata." IEEE Transactions on Automatic Control 59, no. 1 (January 2014): 120–33. http://dx.doi.org/10.1109/tac.2013.2279914.

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Wen, Yicheng, and Asok Ray. "Vector space formulation of probabilistic finite state automata." Journal of Computer and System Sciences 78, no. 4 (July 2012): 1127–41. http://dx.doi.org/10.1016/j.jcss.2012.02.001.

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6

Huang, Mingzhang, Hongfei Fu, and Joost-Pieter Katoen. "Deciding probabilistic simulation between probabilistic pushdown automata and finite-state systems." Information and Computation 268 (October 2019): 104431. http://dx.doi.org/10.1016/j.ic.2019.05.004.

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Chattopadhyay, Ishanu, and Hod Lipson. "Abductive learning of quantized stochastic processes with probabilistic finite automata." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1984 (February 13, 2013): 20110543. http://dx.doi.org/10.1098/rsta.2011.0543.

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We present an unsupervised learning algorithm ( GenESeSS ) to infer the causal structure of quantized stochastic processes, defined as stochastic dynamical systems evolving over discrete time, and producing quantized observations. Assuming ergodicity and stationarity, GenESeSS infers probabilistic finite state automata models from a sufficiently long observed trace. Our approach is abductive; attempting to infer a simple hypothesis, consistent with observations and modelling framework that essentially fixes the hypothesis class. The probabilistic automata we infer have no initial and terminal states, have no structural restrictions and are shown to be probably approximately correct-learnable. Additionally, we establish rigorous performance guarantees and data requirements, and show that GenESeSS correctly infers long-range dependencies. Modelling and prediction examples on simulated and real data establish relevance to automated inference of causal stochastic structures underlying complex physical phenomena.
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Viard, Kevin, Maria Pia Fanti, Gregory Faraut, and Jean-Jacques Lesage. "Human Activity Discovery and Recognition Using Probabilistic Finite-State Automata." IEEE Transactions on Automation Science and Engineering 17, no. 4 (October 2020): 2085–96. http://dx.doi.org/10.1109/tase.2020.2989226.

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Li, Zhi, Harm Derksen, Jonathan Gryak, Cheng Jiang, Zijun Gao, Winston Zhang, Hamid Ghanbari, Pujitha Gunaratne, and Kayvan Najarian. "Prediction of cardiac arrhythmia using deterministic probabilistic finite-state automata." Biomedical Signal Processing and Control 63 (January 2021): 102200. http://dx.doi.org/10.1016/j.bspc.2020.102200.

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Dwork, Cynthia, and Larry Stockmeyer. "A Time Complexity Gap for Two-Way Probabilistic Finite-State Automata." SIAM Journal on Computing 19, no. 6 (December 1990): 1011–23. http://dx.doi.org/10.1137/0219069.

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Дисертації з теми "Probabilistic finite state automata"

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FRANCH, Daniel Kudlowiez. "Dynamical system modeling with probabilistic finite state automata." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/25448.

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FACEPE
Discrete dynamical systems are widely used in a variety of scientific and engineering applications, such as electrical circuits, machine learning, meteorology and neurobiology. Modeling these systems involves performing statistical analysis of the system output to estimate the parameters of a model so it can behave similarly to the original system. These models can be used for simulation, performance analysis, fault detection, among other applications. The current work presents two new algorithms to model discrete dynamical systems from two categories (synchronizable and non-synchronizable) using Probabilistic Finite State Automata (PFSA) by analyzing discrete symbolic sequences generated by the original system and applying statistical methods and inference, machine learning algorithms and graph minimization techniques to obtain compact, precise and efficient PFSA models. Their performance and time complexity are compared with other algorithms present in literature that aim to achieve the same goal by applying the algorithms to a series of common examples.
Sistemas dinâmicos discretos são amplamente usados em uma variedade de aplicações cientifícas e de engenharia, por exemplo, circuitos elétricos, aprendizado de máquina, meteorologia e neurobiologia. O modelamento destes sistemas envolve realizar uma análise estatística de sequências de saída do sistema para estimar parâmetros de um modelo para que este se comporte de maneira similar ao sistema original. Esses modelos podem ser usados para simulação, referência ou detecção de falhas. Este trabalho apresenta dois novos algoritmos para modelar sistemas dinâmicos discretos de duas categorias (sincronizáveis e não-sincronizáveis) por meio de Autômatos Finitos Probabilísticos (PFSA, Probabilistic Finite State Automata) analisando sequências geradas pelo sistema original e aplicando métodos estatísticos, algoritmos de aprendizado de máquina e técnicas de minimização de grafos para obter modelos PFSA compactos e eficientes. Sua performance e complexidade temporal são comparadas com algoritmos presentes na literatura que buscam atingir o mesmo objetivo aplicando os algoritmos a uma série de exemplos.
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Merryman, William Patrick. "Animating the conversion of nondeterministic finite state automata to deterministic finite state automata." Thesis, Montana State University, 2007. http://etd.lib.montana.edu/etd/2007/merryman/MerrymanW0507.pdf.

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Martin, Oliver B. 1979. "Accurate belief state update for probabilistic constraint automata." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32446.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.
Includes bibliographical references (p. 91-93).
As autonomous spacecraft and other robotic systems grow increasingly complex, there is a pressing need for capabilities that more accurately monitor and diagnose system state while maintaining reactivity. Mode estimation addresses this problem by reasoning over declarative models of the physical plant, represented as a factored variant of Hidden Markov Models (HMMs), called Probabilistic Concurrent Constraint Automata (PCCA). Previous mode estimation approaches track a set of most likely PCCA state trajectories, enumerating them in order of trajectory probability. Although Best-First Trajectory Enumeration (BFTE) is efficient, ignoring the additional trajectories that lead to the same target state can significantly underestimate the true state probability and result in misdiagnosis. This thesis introduces two innovative belief state approximation techniques, called Best-First Belief State Enumeration (BFBSE) and Best-First Belief State Update (BFBSU), that address this limitation by computing estimate probabilities directly from the HMM belief state update equations. Theoretical and empirical results show that I3FBSE and BFBSU significantly increases estimator accuracy, uses less memory, and have no increase in computation time when enumerating a moderate number of estimates for the approximate belief state of subsystem sized models.
by Oliver Borelli Martin.
S.M.
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Timmons, Eric (Eric M. ). "Fast, approximate state estimation of concurrent probabilistic hybrid automata." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82494.

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Анотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.
This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from department-submitted PDF version of thesis
Includes bibliographical references (p. 73).
It is an undeniable fact that autonomous systems are simultaneously becoming more common place, more complex, and deployed in more inhospitable environments. Examples include smart homes, smart cars, Mars rovers, unmanned aerial vehicles, and autonomous underwater vehicles. A common theme that all of these autonomous systems share is that in order to appropriately control them and prevent mission failure, they must be able to quickly estimate their internal state and the state of the world. A natural representation of many real world systems is to describe them in terms of a mixture of continuous and discrete variables. Unfortunately, hybrid estimation is typically intractable due to the large space of possible assignments to the discrete variables. In this thesis, we investigate how to incorporate conflict directed techniques from the consistency-based, model-based diagnosis community into a hybrid framework that is no longer purely consistency based. We introduce a novel search algorithm, A* with Bounding Conflicts, that uses conflicts to not only record infeasiblilities, but also learn where in the search space the heuristic function provided to the A* search is weak (possibly due to heavy to moderate sensor or process noise). Additionally, we describe a hybrid state estimation algorithm that uses this new search to perform estimation on hybrid discrete/continuous systems.
by Eric Timmons.
S.M.
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Khemuka, Atul Ravi. "Workflow Modeling Using Finite Automata." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000172.

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Bird, Philip. "Unifying programming paradigms : logic programming and finite state automata." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419609.

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Wagner, Daniel. "Finite-state abstractions for probabilistic computation tree logic." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/6348.

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Probabilistic Computation Tree Logic (PCTL) is the established temporal logic for probabilistic verification of discrete-time Markov chains. Probabilistic model checking is a technique that verifies or refutes whether a property specified in this logic holds in a Markov chain. But Markov chains are often infinite or too large for this technique to apply. A standard solution to this problem is to convert the Markov chain to an abstract model and to model check that abstract model. The problem this thesis therefore studies is whether or when such finite abstractions of Markov chains for model checking PCTL exist. This thesis makes the following contributions. We identify a sizeable fragment of PCTL for which 3-valued Markov chains can serve as finite abstractions; this fragment is maximal for those abstractions and subsumes many practically relevant specifications including, e.g., reachability. We also develop game-theoretic foundations for the semantics of PCTL over Markov chains by capturing the standard PCTL semantics via a two-player games. These games, finally, inspire a notion of p-automata, which accept entire Markov chains. We show that p-automata subsume PCTL and Markov chains; that their languages of Markov chains have pleasant closure properties; and that the complexity of deciding acceptance matches that of probabilistic model checking for p-automata representing PCTL formulae. In addition, we offer a simulation between p-automata that under-approximates language containment. These results then allow us to show that p-automata comprise a solution to the problem studied in this thesis.
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Egri-Nagy, Attila. "Algebraic hierarchical decomposition of finite state automata : a computational approach." Thesis, University of Hertfordshire, 2005. http://hdl.handle.net/2299/14267.

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The theory of algebraic hierarchical decomposition of finite state automata is an important and well developed branch of theoretical computer science (Krohn-Rhodes Theory). Beyond this it gives a general model for some important aspects of our cognitive capabilities and also provides possible means for constructing artificial cognitive systems: a Krohn-Rhodes decomposition may serve as a formal model of understanding since we comprehend the world around us in terms of hierarchical representations. In order to investigate formal models of understanding using this approach, we need efficient tools but despite the significance of the theory there has been no computational implementation until this work. Here the main aim was to open up the vast space of these decompositions by developing a computational toolkit and to make the initial steps of the exploration. Two different decomposition methods were implemented: the VuT and the holonomy decomposition. Since the holonomy method, unlike the VUT method, gives decompositions of reasonable lengths, it was chosen for a more detailed study. In studying the holonomy decomposition our main focus is to develop techniques which enable us to calculate the decompositions efficiently, since eventually we would like to apply the decompositions for real-world problems. As the most crucial part is finding the the group components we present several different ways for solving this problem. Then we investigate actual decompositions generated by the holonomy method: automata with some spatial structure illustrating the core structure of the holonomy decomposition, cases for showing interesting properties of the decomposition (length of the decomposition, number of states of a component), and the decomposition of finite residue class rings of integers modulo n. Finally we analyse the applicability of the holonomy decompositions as formal theories of understanding, and delineate the directions for further research.
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Cazalis, Daniel S. "Algebraic Theory of Minimal Nondeterministic Finite Automata with Applications." FIU Digital Commons, 2007. http://digitalcommons.fiu.edu/etd/8.

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Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA's behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.
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Makarov, Alexander. "Application of finite state methods to shape coding and processing in object-based video." Thesis, Staffordshire University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368316.

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Книги з теми "Probabilistic finite state automata"

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Schulz, Klaus U., and Stoyan Mihov. Finite-State Techniques: Automata, Transducers and Bimachines. Cambridge University Press, 2019.

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Karttunen, Lauri. Finite-State Technology. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0018.

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The article introduces the basic concepts of finite-state language processing: regular languages and relations, finite-state automata, and regular expressions. Many basic steps in language processing, ranging from tokenization, to phonological and morphological analysis, disambiguation, spelling correction, and shallow parsing, can be performed efficiently by means of finite-state transducers. The article discusses examples of finite-state languages and relations. Finite-state networks can represent only a subset of all possible languages and relations; that is, only some languages are finite-state languages. Furthermore, this article introduces two types of complex regular expressions that have many linguistic applications, restriction and replacement. Finally, the article discusses the properties of finite-state automata. The three important properties of networks are: that they are epsilon free, deterministic, and minimal. If a network encodes a regular language and if it is epsilon free, deterministic, and minimal, the network is guaranteed to be the best encoding for that language.
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Частини книг з теми "Probabilistic finite state automata"

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Condon, Anne. "Bounded Error Probabilistic Finite State Automata." In Handbook of Randomized Computing, 509–31. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0013-1_13.

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Esposito, Yann, Aurélien Lemay, François Denis, and Pierre Dupont. "Learning Probabilistic Residual Finite State Automata." In Grammatical Inference: Algorithms and Applications, 77–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45790-9_7.

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Rosier, Louis E., and Hsu-Chun Yen. "On the complexity of deciding fair termination of probabilistic concurrent finite-state programs." In Automata, Languages and Programming, 334–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/3-540-16761-7_83.

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Rekaby Salama, Amr, and Wolfgang Menzel. "Joint Labeling of Syntactic Function and Semantic Role Using Probabilistic Finite State Automata." In Advances in Intelligent Systems and Computing, 588–605. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01057-7_46.

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Palmer, Nick, and Paul W. Goldberg. "PAC-Learnability of Probabilistic Deterministic Finite State Automata in Terms of Variation Distance." In Lecture Notes in Computer Science, 157–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564089_14.

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Moosbrugger, Marcel, Ezio Bartocci, Joost-Pieter Katoen, and Laura Kovács. "Automated Termination Analysis of Polynomial Probabilistic Programs." In Programming Languages and Systems, 491–518. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_18.

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AbstractThe termination behavior of probabilistic programs depends on the outcomes of random assignments. Almost sure termination (AST) is concerned with the question whether a program terminates with probability one on all possible inputs. Positive almost sure termination (PAST) focuses on termination in a finite expected number of steps. This paper presents a fully automated approach to the termination analysis of probabilistic while-programs whose guards and expressions are polynomial expressions. As proving (positive) AST is undecidable in general, existing proof rules typically provide sufficient conditions. These conditions mostly involve constraints on supermartingales. We consider four proof rules from the literature and extend these with generalizations of existing proof rules for (P)AST. We automate the resulting set of proof rules by effectively computing asymptotic bounds on polynomials over the program variables. These bounds are used to decide the sufficient conditions – including the constraints on supermartingales – of a proof rule. Our software tool Amber can thus check AST, PAST, as well as their negations for a large class of polynomial probabilistic programs, while carrying out the termination reasoning fully with polynomial witnesses. Experimental results show the merits of our generalized proof rules and demonstrate that Amber can handle probabilistic programs that are out of reach for other state-of-the-art tools.
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Andriushchenko, Roman, Milan Češka, Sebastian Junges, and Joost-Pieter Katoen. "Inductive Synthesis for Probabilistic Programs Reaches New Horizons." In Tools and Algorithms for the Construction and Analysis of Systems, 191–209. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_11.

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AbstractThis paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov chains with related but distinct topologies, and a reachability specification. The method builds on a novel inductive oracle that greedily generates counter-examples (CEs) for violating programs and uses them to prune the family. These CEs leverage the semantics of the family in the form of bounds on its best- and worst-case behaviour provided by a deductive oracle using an MDP abstraction. The method further monitors the performance of the synthesis and adaptively switches between inductive and deductive reasoning. Our experiments demonstrate that the novel CE construction provides a significantly faster and more effective pruning strategy leading to an accelerated synthesis process on a wide range of benchmarks. For challenging problems, such as the synthesis of decentralized partially-observable controllers, we reduce the run-time from a day to minutes.
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Ligot, Antoine, Ken Hasselmann, and Mauro Birattari. "AutoMoDe-Arlequin: Neural Networks as Behavioral Modules for the Automatic Design of Probabilistic Finite-State Machines." In Lecture Notes in Computer Science, 271–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60376-2_21.

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Dai, Jack J., James I. Lathrop, Jack H. Lutz, and Elvira Mayordomo. "Finite-State Dimension." In Automata, Languages and Programming, 1028–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48224-5_83.

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Denis, François, Aurélien Lemay, and Alain Terlutte. "Residual Finite State Automata." In STACS 2001, 144–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44693-1_13.

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Тези доповідей конференцій з теми "Probabilistic finite state automata"

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Adenis, Patrick, Kushal Mukherjee, and Asok Ray. "State splitting and state merging in probabilistic finite state automata." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5990861.

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Suresh, Ananda Theertha, Brian Roark, Michael Riley, and Vlad Schogol. "Distilling weighted finite automata from arbitrary probabilistic models." In Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-3112.

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Viard, K., M. P. Fanti, G. Faraut, and J.-J. Lesage. "Recognition of human activity based on probabilistic finite-state automata." In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2017. http://dx.doi.org/10.1109/etfa.2017.8247621.

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Dwork, C., and L. Stockmeyer. "On the power of 2-way probabilistic finite state automata." In 30th Annual Symposium on Foundations of Computer Science. IEEE, 1989. http://dx.doi.org/10.1109/sfcs.1989.63522.

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Saikrishna, Vidya, David L. Dowe, and Sid Ray. "MML inference of Finite State Automata for probabilistic spam detection." In 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR). IEEE, 2015. http://dx.doi.org/10.1109/icapr.2015.7050655.

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Wilson, James, Nayeff Najjar, James Hare, and Shalabh Gupta. "Human activity recognition using LZW-Coded Probabilistic Finite State Automata." In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139613.

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Peng, Tao, Liuxiang Dai, Zhiwen Chen, ChengLei Ye, and Xia Peng. "A Probabilistic Finite State Automata-based Fault Detection Method for Traction Motor." In 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE). IEEE, 2020. http://dx.doi.org/10.1109/isie45063.2020.9152449.

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Chattopadhyay, Ishanu, and Asok Ray. "Optimal path-planning under finite memory obstacle dynamics based on probabilistic finite state automata models." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5160369.

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Vardi, Moshe Y. "Automatic verification of probabilistic concurrent finite state programs." In 26th Annual Symposium on Foundations of Computer Science (sfcs 1985). IEEE, 1985. http://dx.doi.org/10.1109/sfcs.1985.12.

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Yicheng Wen, Asok Ray, Ishanu Chattopadhyay, and Shashi Phoha. "Modeling of symbolic systems: Part I - Vector space representation of probabilistic finite state automata." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5990763.

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Звіти організацій з теми "Probabilistic finite state automata"

1

Terzic, Vesna, and William Pasco. Novel Method for Probabilistic Evaluation of the Post-Earthquake Functionality of a Bridge. Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1916.

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Анотація:
While modern overpass bridges are safe against collapse, their functionality will likely be compromised in case of design-level or beyond design-level earthquake, which may generate excessive residual displacements of the bridge deck. Presently, there is no validated, quantitative approach for estimating the operational level of the bridge after an earthquake due to the difficulty of accurately simulating residual displacements. This research develops a novel method for probabilistic evaluation of the post-earthquake functionality state of the bridge; the approach is founded on an explicit evaluation of bridge residual displacements and associated traffic capacity by considering realistic traffic load scenarios. This research proposes a high-fidelity finite-element model for bridge columns, developed and calibrated using existing experimental data from the shake table tests of a full-scale bridge column. This finite-element model of the bridge column is further expanded to enable evaluation of the axial load-carrying capacity of damaged columns, which is critical for an accurate evaluation of the traffic capacity of the bridge. Existing experimental data from the crushing tests on the columns with earthquake-induced damage support this phase of the finite-element model development. To properly evaluate the bridge's post-earthquake functionality state, realistic traffic loadings representative of different bridge conditions (e.g., immediate access, emergency traffic only, closed) are applied in the proposed model following an earthquake simulation. The traffic loadings in the finite-element model consider the distribution of the vehicles on the bridge causing the largest forces in the bridge columns.
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