• Smart Structures and Systems
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• v.15 no.3
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• pp.913-929
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• 2015

Hybrid simulation is increasingly being recognized as a powerful technique for laboratory testing. It offers the opportunity for global system evaluation of civil infrastructure systems subject to extreme dynamic loading, often with a significant reduction in time and cost. In this approach, a reference structure/system is partitioned into two or more substructures. The portion of the structural system designated as 'physical' or 'experimental' is tested in the laboratory, while other portions are replaced with a computational model. Many researchers have quite effectively used hybrid simulation (HS) and real-time hybrid simulation (RTHS) methods for examination and verification of existing and new design concepts and proposed structural systems or devices. This paper provides a detailed perspective of the enabling role that HS and RTHS methods have played in advancing the practice of earthquake engineering. Herein, our focus is on investigations related to earthquake engineering, those with CURATED data available in their entirety in the NEES Data Repository.

• Structural Engineering and Mechanics
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• v.13 no.2
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• pp.187-209
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• 2002

The infrastructure system in the United States has been aging faster than the resource available to restore them. Therefore decision for allocating the resources is based in part on the condition of the structural system. This paper proposes to use neural network to predict the overall rating of the structural system because of the successful applications of neural network to other fields which require a "symptom-diagnostic" type relationship. The goal of this paper is to illustrate the potential of using neural network in civil engineering applications and, particularly, in bridge evaluations. Data collected by the Tennessee Department of Transportation were used as "test bed" for the study. Multi-layer feed forward networks were developed using the Levenberg-Marquardt training algorithm. All the neural networks consisted of at least one hidden layer of neurons. Hyperbolic tangent transfer functions were used in the first hidden layer and log-sigmoid transfer functions were used in the subsequent hidden and output layers. The best performing neural network consisted of three hidden layers. This network contained three neurons in the first hidden layer, two neurons in the second hidden layer and one neuron in the third hidden layer. The neural network performed well based on a target error of 10%. The results of this study indicate that the potential for using neural networks for the evaluation of infrastructure systems is very good.

• International Journal of Aeronautical and Space Sciences
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• v.8 no.2
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• pp.54-66
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• 2007

Traffic and emergency monitoring systems are essential constituents of Intelligent Transportation System (ITS) technologies, but the lack of traffic monitoring has become a primary weakness in providing prompt emergency services. Demonstrated in numerous military applications, unmanned aerial vehicles (UAVs) have great potentials as a part of ITS infrastructure for providing quick and real-time aerial video images of large surface area to the ground. Despite of obvious advantages of UAVs for traffic monitoring and many other civil applications, it is rare to encounter success stories of UAVs in civil application including transportation. The objective of this paper is to report the outcomes of research supported by the state agency in US to investigate the feasibility of integrating UAVs into urban highway traffic monitoring as a part of ITS infrastructure. These include current technical and regulatory issues, and possible suggestions for a future UAV system in civil applications.

• Smart Structures and Systems
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• v.6 no.9
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• pp.1007-1023
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• 2010

This paper describes the development and evaluation of an innovative TDR distributed moisture sensor. This sensor features advantages of being responsive to the spatial variations of the soil moisture content. The geometry design of the sensor makes it rugged for field installation. Good linear calibration is obtained between the sensor measured dielectric constant and soil physical properties. Simulations by the finite element method (FEM) are conducted to assist the design of this sensor and to determine the effective sampling range. Compared with conventional types of moisture sensor, which only makes point measurement, this sensor possesses distributed moisture sensing capability. This new sensor is not only easy to install, but also measures moisture distribution with much lower cost. This new sensor holds promise to significantly improve the current field instruments. It will be a useful tool to help study the influence of a variety of moisture-related phenomena on infrastructure performance.

• Smart Structures and Systems
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• v.11 no.5
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• pp.511-531
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• 2013

Recent advances in low-cost remote monitoring systems have made it possible and practical to perform structural health monitoring (SHM) on a large scale. However, it is difficult for a single remote monitoring system to cover a wide range of SHM applications due to the amount of specialization required. For the remote monitoring system to be flexible, sustainable, and robust, this article introduces a new cost-effective, advanced remote monitoring and inspection system named DuraMote that can serve as a next generation supervisory control and data acquisition (SCADA) system for civil infrastructure systems. To evaluate the performance of DuraMote, we conduct experiments at two representative counterpart sites: a bridge and water pipelines. The objectives of this article are to improve upon the existing SCADA by integrating the remote monitoring system (i.e., DuraMote), to describe a prototype SCADA for civil engineering structures, and to validate its effectiveness with long-term field deployment results.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.319-328
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• 2022

Ensuring the integrity of a country's infrastructure is necessary to protect surrounding communities in case of disaster. Embankment dam systems across the US are an essential component of infrastructure, referred to as lifeline structures. Embankment dams are crucial to the survival of life and if these structures were to fail, it is imperative that states be prepared. Southern California is particularly concerned with the stability of embankment dams due to the frequent seismic activity that occurs in the state. The purpose of this study was to create a numerical model of an existing embankment dam simulated under seismic loads using previously recorded data. The embankment dam that was studied in Los Angeles, California was outfitted with accelerometers provided by the California Strong Motion Instrumentation Program that have recorded strong motion data for decades and was processed by the Center for Engineering Strong Motion Data to be used in future engineering applications. The accelerometer data was then used to verify the numerical model that was created using finite element modeling software RS2. The results from this study showed Puddingstone Dam's simulated response was consistent with that experienced during previous earthquakes and therefore validated the predicted behavior from the numerical model. The study also identified areas of weakness and instability on the dam that posed the greatest risk for its failure. Following this study, the numerical model can now be used to predict the dam's response to future earthquakes, develop plans for its remediation, and for emergency response in case of disaster.

• Smart Structures and Systems
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• v.15 no.3
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• pp.593-608
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• 2015

In many of the industrialized countries an increasing amount of infrastructure is ageing. This has become specifically critical to bridges which are a major asset with respect to keeping an economy alive. Life of this infrastructure is scattering but often little quantifiable information is known with respect to its damage condition. This article describes how a damage tolerance approach used in aviation today may even be applied to civil infrastructure in the sense that operational life can be applied in the context of modern life cycle management. This can be applied for steel structures as a complete process where much of the damage accumulation behavior is known and may even be adopted to concrete structures in principle, where much of the missing knowledge in damage accumulation has to be substituted by enhanced inspection. This enhanced and continuous inspection can be achieved through robotic systems in a first approach as well as built in sensors in the sense of structural health monitoring (SHM).

• Smart Structures and Systems
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• v.20 no.6
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• pp.697-708
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• 2017

Maintenance plays a critical role in the bridge industry, but actual practices show many limitations because of traditional, 2D-based information systems. It is necessary to develop a new generation of maintenance information management systems for more reliable decision making in bridge maintenance. Enhancing current work processes requires a BIM-based 3D digital model that can use information from the whole lifecycle of a project (design, construction, operation, and maintenance) through continuous exchanges and updates from each stakeholder. This study describes the development of a data scheme for maintenance of cable-stayed bridges. We implemented the proposed system for a cable-stayed bridge and discussed its effectiveness.

• Smart Structures and Systems
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• v.24 no.3
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• pp.303-317
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• 2019

Magneto-rheological fluid (MRF) rotatory dampers are normally used for controlling the constant rotation of machines and engines. In this research, such a device is proposed to act as variable stiffness device to alleviate the rotational oscillation existing in the many engineering applications, such as motor. Under such thought, the main purpose of this work is to characterize the nonlinear torque-angular displacement/angular velocity responses of an MRF based variable stiffness device in oscillatory motion. A rotational hysteresis model, consisting of a rotatory spring, a rotatory viscous damping element and an error function-based hysteresis element, is proposed, which is capable of describing the unique dynamical characteristics of this smart device. To estimate the optimal model parameters, a modified whale optimization algorithm (MWOA) is employed on the captured experimental data of torque, angular displacement and angular velocity under various excitation conditions. In MWOA, a nonlinear algorithm parameter updating mechanism is adopted to replace the traditional linear one, enhancing the global search ability initially and the local search ability at the later stage of the algorithm evolution. Additionally, the immune operation is introduced in the whale individual selection, improving the identification accuracy of solution. Finally, the dynamic testing results are used to validate the performance of the proposed model and the effectiveness of the proposed optimization algorithm.