Bridge monitoring

Vardanega, Paul J.; Webb, Graham; Fidler, Paul; Huseynov, Farhad; Kariyawasam, Kkgkd; Middleton, Campbell

doi:10.1016/b978-0-12-823550-8.00023-8

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…Bridges are civil structures whose monitoring is extremely important in terms of safety; great effort has been made in this context, as witnessed by the literature [ 1 , 2 , 3 , 4 ]. Several parameters need to be detected for bridge health, including strain, acceleration, displacement, temperature, and inclination [ 5 ]. For what concerns the displacement, contact sensors such as linear voltage differential transformers [ 6 ], ultrasonic sensors [ 7 , 8 ], ultrawideband (UWB) sensors [ 9 ], and accelerometers [ 10 , 11 , 12 ] provide high accuracy and are light and easy to maintain.…”

Section: Introductionmentioning

confidence: 99%

Transversal Displacement Detection of an Arched Bridge with a Multimonostatic Multiple-Input Multiple-Output Radar

Pagnini,

Miccinesi,

Beni

et al. 2024

Sensors

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Interferometric radars are widely used for monitoring civil structures. Bridges are critical structures that need to be constantly monitored for the safety of the users. In this work, a frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar was used for monitoring an arched bridge in Catanzaro, Italy. Two measurements were carried out; a first standard measurement was made in a monostatic configuration, while a subsequent measurement was carried out in a multimonostatic configuration in order to retrieve the components of the deck displacement. A method that is able to predict the measurement uncertainty as a function of the multimonostatic geometry is provided, thereby aiming to facilitate the operators in the choice of the proper experimental setup. The multimonostatic measurement revealed a displacement along the horizontal direction that was four times higher than the one along the vertical direction, while the values reported in the literature correspond to a ratio of at most around 0.2. This is the first time that such a large ratio detected by radar has been reported; at any rate, it is compatible with the arched structure of this specific bridge. This case study highlights the importance of techniques that are able to retrieve at least two components of the displacement.

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Section: Introductionmentioning

confidence: 99%

Transversal Displacement Detection of an Arched Bridge with a Multimonostatic Multiple-Input Multiple-Output Radar

Pagnini,

Miccinesi,

Beni

et al. 2024

Sensors

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“…Improved accessibility and scalability of technology has generally increased instances of physical installations of sensors on critical bridge systems (Beck et al., 2001; Yuen & Katafygiotis, 2006). Common instrumentation examples include accelerometers (Guzman‐Acevedo et al., 2019; Moreu et al., 2017; Omidalizarandi et al., 2020), inclinometers (Otter et al., 2017; Sitton, Story, et al., 2017a; Vardanega et al., 2022), and strain gages (Kołakowski et al., 2011; Otter et al., 2017; Story & Fry, 2014b). These sensors record measurable quantities of bridge response caused by structural excitation, for example, excitation from a passing train or a vehicle‐bridge strike.…”

Section: Introductionmentioning

confidence: 99%

Parallel heterogeneous data‐fusion convolutional neural networks for improved rail bridge strike detection

Khresat,

Sitton,

Story

2024

Computer aided Civil Eng

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Low clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after a strike. This paper develops parallel heterogeneous data‐fusion convolutional neural networks (PHD‐CNN) operating on data collected from in‐service rail bridges that improves detection and classification of from overheight vehicles. Convolutional neural networks (CNNs) automatically extract features from multiple data streams from different sensor modalities. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. The study also provides practical implementation guidelines through a framework sensitivity characterization to examine the effects on performance of input data stream type, data set size, and CNN architecture complexity. Optimum networks detect, on average, 95% of bridge strikes with false positive rates less than 2%.

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“…There are many other problems, including the suspension of operations and servicing of bridges when the bridges need to be tested and inspected. In the event that this process may lead to financial damage, or even in some cases due to the high volume of traffic, there may not be a service stop for the bridges [21,22].…”

Section: Introductionmentioning

confidence: 99%

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

et al. 2022

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The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

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Bridge monitoring

Cited by 6 publications

References 56 publications

Transversal Displacement Detection of an Arched Bridge with a Multimonostatic Multiple-Input Multiple-Output Radar

Transversal Displacement Detection of an Arched Bridge with a Multimonostatic Multiple-Input Multiple-Output Radar

Parallel heterogeneous data‐fusion convolutional neural networks for improved rail bridge strike detection

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

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