Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning

In the context of modern environmental and societal concerns, there is an increasing demand for methods able to identify management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M decision problem to the component level due to the computational complexity associated with global optimization methodologies under joint system-level state descriptions. In this paper, we propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments, providing optimal management strategies directly at the system level. In our approach, the decision problem is formulated as a factored partially observable Markov decision process, whose dynamics are encoded in Bayesian network conditional structures. The methodology can handle environments under equal or general, unequal deterioration correlations among components, through Gaussian hierarchical structures and dynamic Bayesian networks. In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network. By including deterioration dependence in the simulated environment, and by formulating the cost model at the system level, DDMAC policies intrinsically consider the underlying system-effects. This is demonstrated through numerical experiments conducted for both a 9-out-of-10 system and a steel frame under fatigue deterioration. Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art heuristic approaches. The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies

Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes

Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue or corrosion. Identifying optimal inspection and maintenance policies demands the solution of a complex sequential decision-making problem under uncertainty, with the main objective of efficiently controlling the risk associated with structural failures. Addressing this complexity, risk-based inspection planning methodologies, supported often by dynamic Bayesian networks, evaluate a set of pre-defined heuristic decision rules to reasonably simplify the decision problem. However, the resulting policies may be compromised by the limited space considered in the definition of the decision rules. Avoiding this limitation, Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical methodology for stochastic optimal control under uncertain action outcomes and observations, in which the optimal actions are prescribed as a function of the entire, dynamically updated, state probability distribution. In this paper, we combine dynamic Bayesian networks with POMDPs in a joint framework for optimal inspection and maintenance planning, and we provide the formulation for developing both infinite and finite horizon POMDPs in a structural reliability context. The proposed methodology is implemented and tested for the case of a structural component subject to fatigue deterioration, demonstrating the capability of state-of-the-art point-based POMDP solvers for solving the underlying planning optimization problem. Within the numerical experiments, POMDP and heuristic-based policies are thoroughly compared, and results showcase that POMDPs achieve substantially lower costs as compared to their counterparts, even for traditional problem settings

Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks

Offshore wind structures are subject to deterioration mechanisms throughout their operational lifetime. Even if the deterioration evolution of structural elements can be estimated through physics-based deterioration models, the uncertainties involved in the process hurdle the selection of lifecycle management decisions. In this scenario, the collection of relevant information through an efficient monitoring system enables the reduction of uncertainties, ultimately driving more optimal lifecycle decisions. However, a full monitoring instrumentation implemented on all wind turbines in a farm might become unfeasible due to practical and economical constraints. Besides, certain load monitoring systems often become defective after a few years of marine environment exposure. Addressing the aforementioned concerns, a farm-wide virtual load monitoring scheme directed by a fleet-leader wind turbine offers an attractive solution. Fetched with data retrieved from a fully-instrumented wind turbine, a model can be trained and then deployed, thus yielding load predictions of non-fully monitored wind turbines, from which only standard data remains available. In this paper, we propose a virtual load monitoring framework formulated via Bayesian neural networks (BNNs) and we provide relevant implementation details needed for the construction, training, and deployment of BNN data-based virtual monitoring models. As opposed to their deterministic counterparts, BNNs intrinsically announce the uncertainties associated with generated load predictions and allow to detect inaccurate load estimations generated for non-fully monitored wind turbines. The proposed virtual load monitoring is thoroughly tested through an experimental campaign in an operational offshore wind farm and the results demonstrate the effectiveness of BNN models for fleet-leader-based farm-wide virtual monitoring

Infestação por Aedes aegypti estimada por armadilha de oviposição em Salvador, Bahia

OBJECTIVE: To assess infestation levels of Aedes aegypti using the oviposition trap (ovitrap) method and to compare these results with data obtained with the use of indices traditionally applied in public programs aimed at fighting this vector. METHODS: Nine sentinel areas in Northeastern, Brazil, were assessed and infestation levels were measured for a nine-month period. Egg density and container indices were estimated and compared with previous results found using the house index and Breteau index. RESULTS: The results indicated that the area studied was infested with this vector during the entire study period and that the infestation was widespread in all areas. Different results were found with the different indices studied. There were areas in which the house index and the Breteau index were negative or close to zero, whereas the container index for the same area was 11% and the egg density index was 8.3%. CONCLUSIONS: The container and egg density indices allow better assessment of infestation rates in a city than the conventionally used indices (house index and Breteau index). At lower operational costs and easier standardization, these indices can be applied as a measurement tool for assessing infestation rates during entomological surveillance in programs to fight Aedes aegypti.OBJETIVO: Estimar os índices de infestação do Aedes aegypti, utilizando ovitrampa com atrativo e comparar esse método com os tradicionalmente utilizados nos programas oficiais de combate ao vetor. MÉTODOS: Foram analisadas nove áreas sentinelas de Salvador, Estado da Bahia, durante nove meses. Foram calculados os índices de densidade de ovos e positividade de ovitrampa, e levantamento dos índices de infestação predial e de Breteau para comparação. RESULTADOS: Observou-se que o município apresentou infestação pelo vetor durante todo o período de estudo em todas as áreas sentinelas. Os índices nem sempre apresentaram resultados de infestação semelhantes. Em algumas áreas os índices de infestação predial e de Breteau foram negativos ou próximos de zero, enquanto que o índice de positividade de ovitrampa apresentou valor de 11% e o índice de densidade de ovos 8,3%. CONCLUSÕES: O índice de positividade de ovitrampa e o índice de densidade de ovos permitem avaliar melhor o quadro de infestação de uma cidade com custo operacional bastante reduzido e com maior facilidade de padronização do que os índices tradicionais (infestação predial e de Breteau). Recomenda-se, assim, sua utilização nas fases de levantamento de índices e de vigilância entomológica desenvolvidas pelo programa de combate ao Aedes aegypti

Theory of the Relativistic Brownian Motion. The (1+1)-Dimensional Case

We construct a theory for the 1+1-dimensional Brownian motion in a viscous medium, which is (i) consistent with Einstein's theory of special relativity, and (ii) reduces to the standard Brownian motion in the Newtonian limit case. In the first part of this work the classical Langevin equations of motion, governing the nonrelativistic dynamics of a free Brownian particle in the presence of a heat bath (white noise), are generalized in the framework of special relativity. Subsequently, the corresponding relativistic Langevin equations are discussed in the context of the generalized Ito (pre-point discretization rule) vs. the Stratonovich (mid-point discretization rule) dilemma: It is found that the relativistic Langevin equation in the Haenggi-Klimontovich interpretation (with the post-point discretization rule) is the only one that yields agreement with the relativistic Maxwell distribution. Numerical results for the relativistic Langevin equation of a free Brownian particle are presented.Comment: see cond-mat/0607082 for an improved theor

An Augmented Interface to Display Industrial Robot Faults

Technology advancement is changing the way industrial factories have to face an increasingly complex and competitive market. The fourth industrial revolution (known as industry 4.0) is also changing how human workers have to carry out tasks and actions. In fact, it is no longer impossible to think of a scenario in which human operators and industrial robots work side-by-side, sharing the same environment and tools. To realize a safe work environment, workers should trust robots as well as they trust human operators. Such goal is indeed complex to achieve, especially when workers are under stress conditions, such as when a fault occurs and the human operators are no longer able to understand what is happening in the industrial manipulator. Indeed, Augmented Reality (AR) can help workers to visualize in real-time robots’ faults. This paper proposes an augmented system that assists human workers to recognize and visualize errors, improving their awareness of the system. The system has been tested using both an AR see-through device and a smartphone

Urban Climate Action. The urban content of the NDCs: Global review 2022

This report was prepared by United Nations Human Settlement Programme (UN-Habitat) and the UNESCO Chair on Urban Resilience at the University of Southern Denmark (SDU.Resilience). It offers a global analysis of the urban content of 193 Nationally Determined Contributions (NDCs) submitted to the Secretariat of the United Nations Framework Convention on Climate Change (UNFCCC) before the 19th of June 2022. For this report, more than 200 indicators were used to analyse external data (e.g., Human Development Index and income categorisation) and data within the NDCs, including climate mitigation and adaptation challenges and responses, as well as specific sectors. This analysis is instrumental to supporting Parties’ efforts in further integrating national climate policies and urban climate actions, which is considered fundamental to raising ambition and developing adequate and timely actions as required by the current climate emergency. This review can be instrumental for advocacy and direct support to countries by partner organisations. The work was supported by a group of experts from bilateral and multilateral organisations and academia. Three expert group meetings were convened, and a peer review was organised for the final report

Paternity testing and behavioral ecology: a case study of jaguars (Panthera onca) in Emas National Park, Central Brazil.

We used microsatellite loci to test the paternity of two male jaguars involved in an infanticide event recorded during a long-term monitoring program of this species. Seven microsatellite primers originally developed for domestic cats and previously selected for Panthera oncawere used. In order to deal with uncertainty in the mother?s genotypes for some of the loci, 10000 values of Wwere derived by simulation procedures. The male that killed the two cubs was assigned as the true sire. Although the reasons for this behavior remain obscure, it shows, in principle, a low recognition of paternity and kinship in the species. Since the two cubs were not very young, one possibility is that the adult male did not recognize the cubs and killed them for simple territorial reasons. Thus, ecological stress in this local population becomes a very plausible explanation for this infanticide, without further sociobiological implications

The Schroedinger Problem, Levy Processes Noise in Relativistic Quantum Mechanics

The main purpose of the paper is an essentially probabilistic analysis of relativistic quantum mechanics. It is based on the assumption that whenever probability distributions arise, there exists a stochastic process that is either responsible for temporal evolution of a given measure or preserves the measure in the stationary case. Our departure point is the so-called Schr\"{o}dinger problem of probabilistic evolution, which provides for a unique Markov stochastic interpolation between any given pair of boundary probability densities for a process covering a fixed, finite duration of time, provided we have decided a priori what kind of primordial dynamical semigroup transition mechanism is involved. In the nonrelativistic theory, including quantum mechanics, Feyman-Kac-like kernels are the building blocks for suitable transition probability densities of the process. In the standard "free" case (Feynman-Kac potential equal to zero) the familiar Wiener noise is recovered. In the framework of the Schr\"{o}dinger problem, the "free noise" can also be extended to any infinitely divisible probability law, as covered by the L\'{e}vy-Khintchine formula. Since the relativistic Hamiltonians $|\nabla |$ and $\sqrt {-\triangle +m^2}-m$ are known to generate such laws, we focus on them for the analysis of probabilistic phenomena, which are shown to be associated with the relativistic wave (D'Alembert) and matter-wave (Klein-Gordon) equations, respectively. We show that such stochastic processes exist and are spatial jump processes. In general, in the presence of external potentials, they do not share the Markov property, except for stationary situations. A concrete example of the pseudodifferential Cauchy-Schr\"{o}dinger evolution is analyzed in detail. The relativistic covariance of related waveComment: Latex fil