Optimal Sensor Placement for Long-span Railway Steel Truss Cable-stayed Bridge

Compared with medium and small span bridges, very limited attention has been paid on the research of the impact coefficient of long-span railway bridges. To estimate the impact effects of long-span railway bridges subjected to moving vehicles, a real long-span railway cable-stayed bridge is regarded as the research object in this study, and a coupled model of vehicle-bridge system is established. The track irregularities are taken as the system excitation and the dynamic responses of the vehicle-bridge system are calculated. The impact effects on main girder, stayed cable, bearings, and bridge tower are discussed at various vehicle speeds. The results show that different components of the long-span railway cable-stayed bridge have different impact coefficients. Even for each part, the impact coefficient is also different at different local positions. It reveals that the impact coefficients in the actual situation may have significant differences with the related code clauses in the present design codes.

Investigation of equal strength mild steel tenons as displacement restraining devices for long-span railway arch bridges

Track-bridge deformation relation and interaction of long-span railway suspension bridges subject to strike-slip faulting

Water pipeline networks make up vital infrastructures throughout the world. Since water pipelines operate under high pressure, their failure can cause severe damage to human health and property. In this study, the water pipeline network of a district in Tehran was modeled using the Finite Element Method (FEM). Also, different soil-pipe conditions were considered and nonlinear time-history analysis was performed via seismic scaled records of Near-Fault (NF) and Far-Fault (FF) earthquakes. Furthermore, Optimal Sensor Placement (OSP) procedure was done in which a Genetic Algorithm (GA) was selected to act as the solution of the optimization formulation. Only BOLTED-GLAND joints in the modeled network were considered for OSP due to their vulnerability against seismic loads. Unlike the common algorithms that employ modal analysis results, the author proposed a novel numerical approach for OSP that utilizes nonlinear time-history analysis results as an exact seismic response. The results showed that there is a significant difference between the obtained OSP results of NF and FF earthquakes.

Optimal sensor placement (OSP) is an important task during the implementation of sophisticated structural health monitoring (SHM) systems for large-scale structures. In this paper, a comparative study between the genetic algorithm (GA) and the firefly algorithm (FA) in solving the OSP problem is conducted. To overcome the drawback related to the inapplicability of the FA in optimization problems with discrete variables, some improvements are proposed, including the one-dimensional binary coding system, the Hamming distance between any two fireflies, and the semioriented movement scheme; also, a simple discrete firefly algorithm (SDFA) is developed. The capabilities of the SDFA and the GA in finding the optimal sensor locations are evaluated using two disparate objective functions in a numerical example with a long-span benchmark cable-stayed bridge. The results show that the developed SDFA can find the optimal sensor configuration with high reliability. The comparative study indicates that the SDFA outperforms the GA in terms of algorithm complexity, computational efficiency, and result quality. The optimization mechanism of the FA has the potential to be extended to a wide range of optimization problems.

Optimal sensor placement is an important yet unsolved problem in control theory. In biological organisms, genetic activity is often highly nonlinear, making it difficult to design libraries of promoters to act as reporters of the cell state. We make use of the Koopman observability gramian to develop an algorithm for optimal sensor (or reporter) placement for discrete time nonlinear dynamical systems to ease the difficulty of design of the promoter library. This ease is enabled due to the fact that the Koopman operator represents the evolution of a nonlinear system linearly by lifting the states to an infinite-dimensional space of observables. The Koopman framework ideally demands high temporal resolution, but data in biology are often sampled sparsely in time. Therefore we compute what we call the temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator, the latter of which is identified from the sparse data. The optimal placement of sensors then corresponds to maximizing the observability of the fine-grained system. We demonstrate the algorithm on a simulation example of a circadian oscillator.

With the flexible vibration influence to attitude signals in large space structure, the issues of modal parameter identification and optimal attitude sensor placement are investigated in this paper. By constructing corresponding Hankel matrix using on-orbit input and output signals, the modal parameter identification of large space structure is implemented based on the observer/Kalman filter identification (OKID) method. In addition, optimal attitude sensor placement of the large flexible structure by using genetic algorithm is also studied. In numerical simulations, the distributed placement of the attitude angle and angular velocity are discussed by establishing a large plate structure model, and the modal frequencies of the model are identified by the OKID algorithm. The simulation results illustrate that the flexible vibration will obviously affect the attitude angular velocity signals and the OKID algorithm can effectively identify most of frequency parameters.

Blasts can produce, in a very short time, an overload much greater than the design load of a building. The blast explosion nearby or within structures causes catastrophic damage to the building both externally and internally. This study intends to model a Cold-Formed Steel (CFS) building using Finite Element Method (FEM) in which material properties of the model are defined according to results of performed laboratory tests. Then accelerograph record of a standard blast was applied to the Finite Element (FE) model. Furthermore, various Optimal Sensor Placement (OSP) algorithms were used and Genetic Algorithm (GA) was selected to act as the solution of the optimization formulation in selection of the best sensor placement according to the blast loading response of the system. In this research a novel numerical algorithm was proposed for OSP procedure which utilizes the exact value of the structural response under blast excitation. Results show that with a proper OSP method for Structural Health Monitoring (SHM) can detect the weak points of CFS structures in different parts efficiently.

This paper uses of the dual structure of coded genetic algorithm to optimize the sensor placement methods. The method using the optimal preservation strategy using the adaptive part of the cross, overcomes deficiencies of computer applying to the lengthy large-scale structure data, storage space, and to ensure that the optimal solution search. Finally, through the analysis of a continuous rigid frame bridge Project, proved that the method superior to the effective independent method in the search capability, computational efficiency and reliability, but still need to further improve the speed of convergence.

This article addresses the problem of moving source localization in 3-D using the time-difference-of-arrival and frequency-difference-of-arrival measurements without accurate sensor location information. Different from the previous studies, we focus on improving the localization performance by introducing two approaches with a limited number of sensors, including optimal sensor placement and velocity configuration, as well as successive measurements for a time period. This article first determines the sensor placement and velocity (including speed and orientation) configuration to achieve optimal localization based on a criterion devised from the Fisher information matrix, in which the restrictions for the two-stage weighted-least-squares (TSWLS) positioning algorithm and the geographical conditions are taken into considerations. Then, to utilize successive measurements while the source is in motion, we separate the sensor velocity into two components for achieving the localization optimality and compensating the source motion, and propose a practical sensor placement and velocity configuration strategy. Finally, combining with the TSWLS-based prelocalization, Kalman filtering and the proposed sensor motion strategy, we develop a source tracking scheme that simultaneously obeys the optimal sensor placement and velocity configuration during tracking. Mathematical analyses and numerical simulations validate the optimal sensor placement and the efficiencies of the source tracking scheme.

This paper considers the optimal sensor placement problem for angle-of-arrival (AOA) target localization in the 2D plane with a Gaussian prior. Optimal sensor locations are analytically determined for a single AOA sensor using the D- and A-optimality criteria and an approximation of the Bayesian Fisher information matrix (BFIM). Optimal sensor placement is shown to align with the minor axis of the prior covariance error ellipse for both optimality criteria. The approximate BFIM is argued to be valid for a sufficiently small prior covariance compared with the target range. Optimal sensor placement results obtained for Bayesian target localization are extended to manoeuvring target tracking. For sensor trajectory optimization subject to turn-rate constraints, numerical search methods based on the D- and A-optimality criteria as well as a new closed-form projection algorithm that aims to achieve alignment with the minor axis of the prior error ellipse are proposed. It is observed that the two optimality criteria generate significantly different optimal sensor trajectories despite having the same optimal sensor placement for the localization of a stationary target. Analysis results and the performance of the sensor trajectory optimization methods are demonstrated with simulation examples. It is observed that the new closed-form projection algorithm achieves superior tracking performance compared with the two numerical search methods.

Given a water distribution network, where should we place sensors toquickly detect contaminants? Or, which blogs should we read to avoid missing important stories?.These seemingly different problems share common structure: Outbreak detection can be modeled as selecting nodes (sensor locations, blogs) in a network, in order to detect the spreading of a virus or information asquickly as possible. We present a general methodology for near optimal sensor placement in these and related problems. We demonstrate that many realistic outbreak detection objectives (e.g., detection likelihood, population affected) exhibit the property of submodularity. We exploit submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm. We also derive online bounds on the quality of the placements obtained by any algorithm. Our algorithms and bounds also handle cases where nodes (sensor locations, blogs) have different costs.We evaluate our approach on several large real-world problems,including a model of a water distribution network from the EPA, andreal blog data. The obtained sensor placements are provably near optimal, providing a constant fraction of the optimal solution. We show that the approach scales, achieving speedups and savings in storage of several orders of magnitude. We also show how the approach leads to deeper insights in both applications, answering multicriteria trade-off, cost-sensitivity and generalization questions.

Structural health monitoring plays an increasingly significant role in detecting damages for large and complex structures to ensure their serviceability and sustainability. Optimal sensor placement is critical in the structural health monitoring system as the sensor configuration directly impacts the quality of collected data used for structural health diagnosis. Therefore, this study presents a comprehensive review of computational methodologies for optimal sensor placement in structural health monitoring. The problem formulation of optimal sensor placement is first introduced, including commonly used evaluation criteria for sensor configurations. Then, various existing optimization methodologies for sensor placement are summarized and introduced in detail, especially for the evolutionary algorithms and their improved variants. Finally, the suitability of computational methods for specific structural health monitoring applications is also discussed. The main goal of this study is to deliver a comprehensive reference of computational methodologies for optimal sensor placement in structural health monitoring studies and applications. This article is concluded by highlighting the most widely utilized evaluation criteria and optimization methodologies for sensor configuration determination.

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

A novel tensor-based modal decomposition method for reduced order modeling and optimal sparse sensor placement