The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Fernando Moreu - One of the best experts on this subject based on the ideXlab platform.
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experimental analysis on effectiveness of crash beams for impact attenuation of overheight vehicle collisions on Railroad Bridges
Journal of Bridge Engineering, 2020Co-Authors: Ali Irmak Ozdagli, Fernando Moreu, Tao WangAbstract:AbstractCollisions between overheight semitrailer trucks with low-clearance Railroad Bridges are one of the leading causes for Railroad traffic interruptions on Bridges in the United States. Curren...
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3d displacement monitoring of Railroad Bridges using unmanned aerial vehicles uavs
Structural Health Monitoring-an International Journal, 2019Co-Authors: Roya Nasimi, David Mascarenas, Fernando MoreuAbstract:Railroad network is the main freight transportation systems in the US, with 40 percent of the total freight traffic being transported by rail. More than 50% of the Railroad Bridges in North America are over 100 years old. Nowadays, train loadings on Bridges are higher than what they were designed to be in the past. Railroad Bridges have been capable to adapt to new loads and successfully ensure safe operations by proper maintenance and repair since their inception, including continuous monitoring to proactively prevent any disruption in the transportation. Railroad managers have indicated that measuring the transverse displacement of the Bridges under train crossing events can be used to quantify their proper performance and to inform management or repair decisions with objective data. Traditionally, Linear variable differential transformer (LVDTs) and other types of sensors are used to obtain the displacement data of the train, but these methods need human forces for their installation. Additionally, some parts of the Bridges are not accessible or easy to reach for these types of inspections. Thus, aerial and remote sensing have become more popular. However, current methods for using Unmanned Aerial Vehicles (UAVs) to monitoring Railroad Bridges rely on cameras or lasers which are either technologically complex or economically unaffordable at large scale. This paper explains the development of a new hybrid sensing method to obtain total dynamic displacement using UAV. Sensors collecting displacements include lasers, cameras, and dynamic 3D cloud data acquisition. Displacement of the bridge is collected with the use of a low-cost laser sensor on the UAS with a camera attached to it. Then, the new algorithm extracts the hovering of the UAS and correct the laser displacement to obtain the displacement of the bridge under load. The results are compared to LVDT displacement for validation. The paper also identifies the priorities of North American Railroad owners regarding how the proposed technology can be implemented for new cost-effective management of Railroad Bridges maintenance.
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Measuring Reference-Free Total Displacements of Piles and Columns Using Low-Cost, Battery-Powered, Efficient Wireless Intelligent Sensors (LEWIS2)
MDPI AG, 2019Co-Authors: Marlo Aguero, Ali Ozdagli, Fernando MoreuAbstract:Currently, over half of the U.S.’s Railroad Bridges are more than 100 years old. Railroad managers ensure that the proper Maintenance, Repair, and Replacement (MRR) of rail infrastructure is prioritized to safely adapt to the increasing traffic demand. By 2035, the demand for U.S. Railroad transportation will increase by 88%, which indicates that considerable expenditure is necessary to upgrade rail infrastructure. Railroad bridge managers need to use their limited funds for bridge MRR to make informed decisions about safety. Consequently, they require economical and reliable methods to receive objective data about bridge displacements under service loads. Current methods of measuring displacements are often expensive. Wired sensors, such as Linear Variable Differential Transformers (LVDTs), require time-consuming installation and involve high labor and maintenance costs. Wireless sensors (WS) are easier to install and maintain but are in general technologically complex and costly. This paper summarizes the development and validation of LEWIS2, the second version of the real-time, low-cost, efficient wireless intelligent sensor (LEWIS) for measuring and autonomously storing reference-free total transverse displacements. The new features of LEWIS2 include portability, accuracy, cost-effectiveness, and readiness for field application. This research evaluates the effectiveness of LEWIS2 for measuring displacements through a series of laboratory experiments. The experiments demonstrate that LEWIS2 can accurately estimate reference-free total displacements, with a maximum error of only 11% in comparison with the LVDT, while it costs less than 5% of the average price of commercial wireless sensors
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real time low cost wireless reference free displacement sensing of Railroad Bridges
2019Co-Authors: Ali Irmak Ozdagli, Ideng Liu, Fernando MoreuAbstract:The U.S. freight rail network moves about 40 tons of freight per person over 225,000 km (140,000 miles) of rail track every year. The Railroad infrastructure contains more than 100,000 Bridges, which correspond to one bridge for every 2.25 km (1.4 miles) of track. Railroad resources and funds are limited. Consequently, Railroads’ maintenance, repair, and replacement (MRR) decisions should be optimized. An objective prioritization of MRR decisions requires quantitative data that informs the structural integrity. Lateral displacement measurement of Bridges is an objective and quantitative performance indicator. Traditional wired displacement measurement systems are costly, labor-intensive, and are difficult to apply on Bridges due to the need of stationary reference points. This paper proposes an Arduino-based low-cost wireless sensing system to estimate bridge displacements from acceleration data. The system uses a low-cost MMA8451 accelerometer and implements a FIR-filter to convert the measurements to displacement. The data is transmitted to the base station using a XBee Series 1 module in real-time. Each sensor platform is estimated to cost about $75. To evaluate the feasibility of the proposed system, a set of laboratory experiments are conducted by placing the sensor platform on a shake table and simulating bridge displacements measured on the field during train crossing events. The proposed measurement system can have impact on many applications that need real-time displacement information including, but not limited to aerospace engineering, mechanical engineering, and wind engineering.
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measuring total transverse reference free displacements for condition assessment of timber Railroad Bridges experimental validation
Journal of Structural Engineering-asce, 2018Co-Authors: Ali Irmak Ozdagli, Ideng Liu, Fernando MoreuAbstract:AbstractToday, the Railroad industry carries 40% of the United States’ total freight ton-miles over 100,000 Bridges spanning over 225,000 km (140,000 mi) of rail tracks and the demand for Railroads...
F Spence - One of the best experts on this subject based on the ideXlab platform.
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campaign monitoring of Railroad Bridges using wireless smart sensors
1st International Workshop on Structural Health Monitoring for Railway System, 2016Co-Authors: F Spence, Fernando Moreu, Robi E KimAbstract:This paper presents research results using wireless smart sensors to develop a cost-effective, practical, and portable structural health monitoring system for Railroad Bridges in North America. The system is designed to enable campaign-style monitoring of Railroad Bridges under in-service train loads. Validation is carried out on a 94.6 m long steel truss bridge near Chicago, the busiest Railroad hub in North America. A finite element (FE) model of the bridge is developed and calibrated using the measured data. Subsequently, the model is used to predict bridge responses at unmeasured locations for varied train loads and speeds. The results from this research provide a technological foundation to develop campaign monitoring as an important tool with which to manage Railroad bridge assets.
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dynamic assessment of timber Railroad Bridges using displacements
Journal of Bridge Engineering, 2015Co-Authors: Fernando Moreu, F Spence, Robi E Kim, S Scola, Sooji Cho, A Kimmle, James M LafaveAbstract:Abstract Infrastructure spending is such a large component of a Railroad budget that it must be prioritized to meet the concurrent safety and line capacity requirements. Current bridge inspection and rating practices recommend observing bridge movements under a live load to help assess bridge conditions. However, measuring bridge movements under trains in the field is a challenging task. Even when they are measured, the relationships between bridge displacements and different loads/speeds are generally unknown. The research reported herein shows the effects of known train loadings, speeds, and traffic directions on the magnitude and frequency of displacements as measured on timber pile bents of a Class I Railroad bridge. Researchers collected both vertical and transverse (lateral) displacements under revenue service traffic and work trains using LVDTs with a sampling frequency of 100 Hz. To investigate the effect of traffic on timber Railroad Bridges, displacements were measured under crossing events at d...
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campaign monitoring of Railroad Bridges in high speed rail shared corridors using wireless smart sensors
2015Co-Authors: F Spence, Fernando Moreu, Robi E KimAbstract:This research project used wireless smart sensors to develop a cost-effective and practical portable structural health monitoring system for Railroad Bridges in North America. The system is designed for periodic deployment rather than as a permanent installation to enable campaign-style bridge response monitoring under in-service conditions. This research project measured bridge responses from a 310 feet long steel truss bridge using wireless sensors and developed a finite element (FE) model to obtain global bridge responses under varied train loads and speeds. Additionally, this project developed a new simple beam model that predicts critical speeds and resonances based on train traffic properties. The results from this pilot project provide a technological foundation to develop campaign monitoring sensor technology as an important tool with which to manage Railroad bridge assets.
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campaign monitoring Railroad Bridges using wireless smart sensors networks
Engineering Mechanics Institute, 2014Co-Authors: Fernando Moreu, Robi E Kim, Sooji Cho, Susu Lei, Guillermo Diazfanas, Fangzhou Dai, F SpenceAbstract:Railroads carry more than 40% of the ton-mile freight transported in North America, with a large portion of the 100,000 Bridges in use today aging approximately 100 years (AREMA, 2003). Freight traffic demands will increase an 88% for domestic freight Railroads by 2035 (Dierkx, 2009). To secure overall profitability margins, add capacity to their rail network and rail operations, and comply with new federal regulations on bridge safety (FRA, 2010a), North American Railroads are currently exploring means and methods to improve the management of their Bridges. Current advances in sensing technology have permitted the shift from traditional vision-based bridge maintenance strategies toward the collection of bridge response measurements under trains. Because Railroad Bridges inspectors need to rate individual Bridges annually, they are interested in the practical use of wireless sensors for campaign monitoring applications. Attractive features of such sensors include wireless communication, solar power, portability, ease of deployment/retrieval, low cost, and onboard computing abilities. This research demonstrates that wireless smart sensors can support campaign monitoring of Railroad Bridges, providing information that Railroad bridge inspectors and managers can use (i.e., structural strain responses under loads). By collecting both train loads at the track level and structural strain responses, Railroads can obtain normalized bridge responses to loads. Railroads can then compare changes in responses between annual bridge inspections, and in the case of steel Bridges, predict remaining fatigue life. Once a specific bridge assessment is complete from a normalized measurement under one train, inspectors can quickly compare them with other responses of different elements in the same bridge, and between similar Bridges. The ultimate goal of the system is to provide Railroads with new objective information about the in-service performance of their Bridges that can assist the prioritization of bridge repairs and replacements at the network level. A CN double-track steel truss bridge over the Calumet River on the south side of Chicago was selected for validation of the wireless monitoring system. The bridge has a 310’-4” span and carries both passenger and freight traffic. Researchers also designed, built, and installed a long-term deployment of wireless sensors for continuous monitoring applications.
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structural health monitoring of Railroad Bridges research needs and preliminary results
Structures Congress 2012, 2012Co-Authors: Fernando Moreu, James M Lafave, F SpenceAbstract:The initial content of this paper presents results of a survey-based study that identified simplified displacement monitoring of Railroad Bridges under trains as the main research interest of Railroad bridge structural engineers. The second part of this publication describes a Railroad bridge classification towards structural health monitoring (SHM) applications, based on current structural engineering problems and challenges identified by the Railroad industry for each Railroad bridge type. Finally, this paper briefly describes ongoing research related to attempting to monitor Railroad bridge deflections by means of using simplified wireless sensors, and preliminary field experimentation and proof of concept validations.
Ali Irmak Ozdagli - One of the best experts on this subject based on the ideXlab platform.
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experimental analysis on effectiveness of crash beams for impact attenuation of overheight vehicle collisions on Railroad Bridges
Journal of Bridge Engineering, 2020Co-Authors: Ali Irmak Ozdagli, Fernando Moreu, Tao WangAbstract:AbstractCollisions between overheight semitrailer trucks with low-clearance Railroad Bridges are one of the leading causes for Railroad traffic interruptions on Bridges in the United States. Curren...
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real time low cost wireless reference free displacement sensing of Railroad Bridges
2019Co-Authors: Ali Irmak Ozdagli, Ideng Liu, Fernando MoreuAbstract:The U.S. freight rail network moves about 40 tons of freight per person over 225,000 km (140,000 miles) of rail track every year. The Railroad infrastructure contains more than 100,000 Bridges, which correspond to one bridge for every 2.25 km (1.4 miles) of track. Railroad resources and funds are limited. Consequently, Railroads’ maintenance, repair, and replacement (MRR) decisions should be optimized. An objective prioritization of MRR decisions requires quantitative data that informs the structural integrity. Lateral displacement measurement of Bridges is an objective and quantitative performance indicator. Traditional wired displacement measurement systems are costly, labor-intensive, and are difficult to apply on Bridges due to the need of stationary reference points. This paper proposes an Arduino-based low-cost wireless sensing system to estimate bridge displacements from acceleration data. The system uses a low-cost MMA8451 accelerometer and implements a FIR-filter to convert the measurements to displacement. The data is transmitted to the base station using a XBee Series 1 module in real-time. Each sensor platform is estimated to cost about $75. To evaluate the feasibility of the proposed system, a set of laboratory experiments are conducted by placing the sensor platform on a shake table and simulating bridge displacements measured on the field during train crossing events. The proposed measurement system can have impact on many applications that need real-time displacement information including, but not limited to aerospace engineering, mechanical engineering, and wind engineering.
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measuring total transverse reference free displacements for condition assessment of timber Railroad Bridges experimental validation
Journal of Structural Engineering-asce, 2018Co-Authors: Ali Irmak Ozdagli, Ideng Liu, Fernando MoreuAbstract:AbstractToday, the Railroad industry carries 40% of the United States’ total freight ton-miles over 100,000 Bridges spanning over 225,000 km (140,000 mi) of rail tracks and the demand for Railroads...
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total reference free displacements for condition assessment of timber Railroad Bridges using tilt
Smart Structures and Systems, 2017Co-Authors: Ali Irmak Ozdagli, Jose A Gomez, Fernando MoreuAbstract:The US Railroad network carries 40% of the nation\'s total freight. Railroad Bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect Bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a Railroad bridge under train crossings is one parameter of interest to Railroad bridge owners, as it quantifies a bridge\'s ability to perform safely and addresses its serviceability. Railroad Bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe Bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a Railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of Railroad Bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber Railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of Railroad Bridges without a fixed reference.
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reference free dynamic displacements of Railroad Bridges using low cost sensors
Journal of Intelligent Material Systems and Structures, 2017Co-Authors: Jose A Gomez, Ali Irmak Ozdagli, Fernando MoreuAbstract:Displacements of Railroad Bridges under service loads are important parameters in assessing bridge conditions and risk of train derailment, according to Railroad bridge managers. Measuring bridge r...
Tomonori Nagayama - One of the best experts on this subject based on the ideXlab platform.
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use of wireless sensors for timber trestle Railroad Bridges health monitoring assessment
Structures Congress 2008, 2008Co-Authors: Fernando Moreu, Tomonori NagayamaAbstract:This presentation discusses the possibilities associated between the existing available applied research in wireless sensors and the always increasing need to identify/address existing Railroad Bridges performance under actual traffic. In one hand, universities and research centers today are developing the advance theory and framework for the use of wireless sensors in structures health monitoring .To implement wireless sensors in the areas where quantitative / objective data is required. Timber trestle Bridges are carrying most of the main line traffic in the US, especially in the South Regions, and a significant number of those are in need of maintenance and or replacement. The maintenance and/or replacement of timber Bridges is based on visual/direct and individual inspections, which can not assess objective/quantitative data of the bridge performance under loading. Different bridge inspectors will not be able to determine objective parameters for different Bridges, and even more, different loading conditions. The dynamic response of the timber trestle bridge continues to be an isolated parameter that can not be compared with the existing maintenance/inspection program. The authors gathered data obtained by connecting wireless sensors to existing timber trestle Bridges which were identified to show excessive longitudinal and transversal displacement under regular traffic loading. The paper includes the description on the implementation process of wireless sensors under real on-site conditions, critique to the results and their validity, and proposed improvements in the entire data acquisition, comprised in concrete proposals. It finally summarizes recommendations for future/potential use in the Railroad timber trestle Railroad bridge maintenance, inspection and assessment.
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possibilities of using sensing technology for Railroad Bridges maintenance and repair
IABSE Symposium Weimar 2007. Improving Infrastructure WorldwideInternational Association for Bridge and Structural Engineering, 2007Co-Authors: Fernando Moreu, Tomonori NagayamaAbstract:This paper describes possible applications of sensing technology for Railroad Bridges maintenance, repair and replacement, and identifies potential areas of development of sensing technology towards applications which could be used today by Railroad bridge engineers. The novel experience carried out on the field under Railroad traffic brings to the table areas of improvement and development. Ideas and suggestions are included on how to develop this potential ready-to-be tool for bridge maintenance and replacement.
Roya Nasimi - One of the best experts on this subject based on the ideXlab platform.
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3d displacement monitoring of Railroad Bridges using unmanned aerial vehicles uavs
Structural Health Monitoring-an International Journal, 2019Co-Authors: Roya Nasimi, David Mascarenas, Fernando MoreuAbstract:Railroad network is the main freight transportation systems in the US, with 40 percent of the total freight traffic being transported by rail. More than 50% of the Railroad Bridges in North America are over 100 years old. Nowadays, train loadings on Bridges are higher than what they were designed to be in the past. Railroad Bridges have been capable to adapt to new loads and successfully ensure safe operations by proper maintenance and repair since their inception, including continuous monitoring to proactively prevent any disruption in the transportation. Railroad managers have indicated that measuring the transverse displacement of the Bridges under train crossing events can be used to quantify their proper performance and to inform management or repair decisions with objective data. Traditionally, Linear variable differential transformer (LVDTs) and other types of sensors are used to obtain the displacement data of the train, but these methods need human forces for their installation. Additionally, some parts of the Bridges are not accessible or easy to reach for these types of inspections. Thus, aerial and remote sensing have become more popular. However, current methods for using Unmanned Aerial Vehicles (UAVs) to monitoring Railroad Bridges rely on cameras or lasers which are either technologically complex or economically unaffordable at large scale. This paper explains the development of a new hybrid sensing method to obtain total dynamic displacement using UAV. Sensors collecting displacements include lasers, cameras, and dynamic 3D cloud data acquisition. Displacement of the bridge is collected with the use of a low-cost laser sensor on the UAS with a camera attached to it. Then, the new algorithm extracts the hovering of the UAS and correct the laser displacement to obtain the displacement of the bridge under load. The results are compared to LVDT displacement for validation. The paper also identifies the priorities of North American Railroad owners regarding how the proposed technology can be implemented for new cost-effective management of Railroad Bridges maintenance.
