Идентификация повреждений методом подвижного фрактала при автоматизированном мониторинге

Автоматизированная система мониторинга технического состояния здания или сооружения предназначена для выдачи актуальной информацией о степени износа строительных конструкций, а также о появлении в них дефектов. Распознавание дефектов достигается обработкой многосенсорной матричной информации на выходе системы мониторинга, состоящей из большого числа датчиков, непрерывно измеряющих параметры сооружения: углы наклонов, ускорения, деформации несущих конструкций. В статье предложен метод распознавания дефектов, основанный на анализе периодически поступающих в компьютер данных, представленных в матричном виде. Каждая строка матрицы представляет из себя последовательность величин, считываемых с каждого датчика. Количество строк равно числу опрашиваемых датчиков. Для обработки этих данных из большой прямоугольной матрицы выделяют квадратную матрицу, из которой в процессе обработки выделяют ее характеристики: главные значения, главные вектора, коэффициенты корреляции и пр. Движушаяся квадратная матрица названа нами движущимся фракталом. В процессе моделирования системы определяются зависимости параметров матрицы от дефектов в конструкции. Практическое применение предлагаемого метода проиллюстрировано на компьютерной модели реального высотного здания. Выполненные исследования показывают, что метод подвижного фрактала позволяет идентифицировать появление дефектов в несущих конструкциях

Competitive percolation strategies for network recovery

Restoring operation of critical infrastructure systems after catastrophic events is an important issue, inspiring work in multiple fields, including network science, civil engineering, and operations research. We consider the problem of finding the optimal order of repairing elements in power grids and similar infrastructure. Most existing methods either only consider system network structure, potentially ignoring important features, or incorporate component level details leading to complex optimization problems with limited scalability. We aim to narrow the gap between the two approaches. Analyzing realistic recovery strategies, we identify over- and undersupply penalties of commodities as primary contributions to reconstruction cost, and we demonstrate traditional network science methods, which maximize the largest connected component, are cost inefficient. We propose a novel competitive percolation recovery model accounting for node demand and supply, and network structure. Our model well approximates realistic recovery strategies, suppressing growth of the largest connected component through a process analogous to explosive percolation. Using synthetic power grids, we investigate the effect of network characteristics on recovery process efficiency. We learn that high structural redundancy enables reduced total cost and faster recovery, however, requires more information at each recovery step. We also confirm that decentralized supply in networks generally benefits recovery efforts.Comment: 14 pages, 6 figure

Adaptive and Restorative Capacity Planning for Complex Infrastructure Networks: Optimization Algorithms and Applications

This research focuses on planning and scheduling of adaptive and restorative capacity enhancement efforts provided by complex infrastructure network in the aftermath of disruptive events. To maximize the adaptive capacity, we propose a framework to optimize the performance level to which a network can quickly adapt to post-disruption conditions by temporary means. Optimal resource allocation is determined with respect to the spatial dimensions of network components and available resources, the effectiveness of the resources, the importance of each element, and the system-wide impact to potential flows within the network. Optimal resource allocation is determined with respect to the spatial dimensions of network components and available resources, the effectiveness of the resources, the importance of each element, and the system-wide impact to potential flows within the network. To optimize the restorative capacity enhancement, we present two mathematical formulations to assign restoration crews to disrupted components and maximize network resilience progress in any given time horizon. In the first formulation, the number of assigned restoration crews to each component can vary to increase the flexibility of models in the presence of different disruption scenarios. Along with considering the assumptions of the first formulation, the second formulation models the condition where the disrupted components can be partially active during the restoration process. We test the efficacy of proposed formulation, for adaptive and restorative capacity enhancement, on the realistic data set of 400-kV French electric transmission Network. The results indicate that the proposed formulations can be used for a wide variety of infrastructure networks and real-time restoration process planning. Approaching the proposed formulations to reality introduces a synchronized routing problem for planning and scheduling restorative efforts for infrastructure networks in the aftermath of a disruptive event. In this problem, a set of restoration crews are dispatched from depots to a road network to restore the disrupted infrastructure network. Considering Binary and Proportional Active formulation, we propose two mathematical formulation in which the number of restoration crews assigned to each disrupted component, the arrival time of each assigned crew to each disrupted component and consequently the restoration rate associated with each disrupted component are considered as variables to increase the flexibility of the model in the presence of different disruptive events. To find the coordinated routes, we propose a relaxed mixed integer program as well as a set of valid inequalities which relates the planning and scheduling efforts to decision makers policies. The integration of the relaxed formulation and valid inequalities results in a lower bound for the original formulations. Furthermore, we propose a constructive heuristic algorithm based on the strong initial solution obtained from feasibility algorithm and a local search algorithm. Computational results on gas, water, and electric power infrastructure network instances from Shelby County, TN data, demonstrates both the effectiveness of the proposed model formulation, in solving small to medium scale problems, the strength of the initial solution procedure, especially for large-scale problems. We also prove that the heuristic algorithm to obtain the near optimal or near-optimal solutions

Disaster risk management of interdependent infrastructure systems for community resilience planning

This research focuses on developing methodologies to model the damage and recovery of interdependent infrastructure systems under disruptive events for community resilience planning. The overall research can be broadly divided into two parts: developing a model to simulate the post-disaster performance of interdependent infrastructure systems and developing decision frameworks to support pre-disaster risk mitigation and post-disaster recovery planning of the interdependent infrastructure systems towards higher resilience. The Dynamic Integrated Network (DIN) model is proposed in this study to simulate the performance of interdependent infrastructure systems over time following disruptive events. It can consider three different levels of interdependent relationships between different infrastructure systems: system-to-system level, system-to-facility level and facility-to-facility level. The uncertainties in some of the modeling parameters are modeled. The DIN model first assesses the inoperability of the network nodes and links over time to simulate the damage and recovery of the interdependent infrastructure facilities, and then assesses the recovery and resilience of the individual infrastructure systems and the integrated network utilizing some network performance metrics. The recovery simulation result from the proposed model is compared to two conventional models, one with no interdependency considered, and the other one with only system-level interdependencies considered. The comparison results suggest that ignoring the interdependencies between facilities in different infrastructure systems would lead to poorly informed decision making. The DIN model is validated through simulating the recovery of the interdependent power, water and cellular systems of Galveston City, Texas after Hurricane Ike (2008). Implementing strategic pre-disaster risk mitigation plan to improve the resilience of the interdependent infrastructure systems is essential for enhancing the social security and economic prosperity of a community. Majority of the existing infrastructure risk mitigation studies or projects focus on a single infrastructure system, which may not be the most efficient and effective way to mitigate the loss and enhance the overall community disaster resilience. This research proposes a risk-informed decision framework which could support the pre-disaster risk mitigation planning of several interdependent infrastructure systems. The characteristics of the Interdependent Infrastructure Risk Mitigation (IIRM) decision problem, such as objective, decision makers, constraints, etc., are clearly identified. A four-stage decision framework to solve the IIRM problem is also presented. The application of the proposed IIRM decision framework is illustrated using a case study on pre-disaster risk mitigation planning for the interdependent critical infrastructure systems in Jamaica. The outcome of the IIRM problem is useful for the decision makers to allocate limited risk mitigation budget or resources to the most critical infrastructure facilities in different systems to achieve greater community disaster resilience. Optimizing the post-disaster recovery of damaged infrastructure systems is essential to alleviate the adverse impacts of natural disasters to communities and enhance their disaster resilience. As a result of infrastructure interdependencies, the complete functional restoration of a facility in one infrastructure system relies on not only the physical recovery of itself, but also the recovery of the facilities in other systems that it depends on. This study introduces the Interdependent Infrastructure Recovery Planning (IIRP) problem, which aims at optimizing the assignment and scheduling of the repair teams for an infrastructure system with considering the repair plan of the other infrastructure systems during the post-disaster recovery phase. Key characteristics of the IIRP problem are identified and a game theory-based IIRP decision framework is presented. Two recovery time-based performance metrics are introduced and applied to evaluate the efficiency and effectiveness of the post-disaster recovery plan. The IIRP decision framework is illustrated using the interdependent power and water systems of the Centerville virtual community subjected to seismic hazard