Dynamic deformation measurement in structural inspections by Augmented Reality technology

Fernando Moreu,Jiaqi Xu,Elijah Wyckoff,John-Wesley Hanson,Derek Doyle 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.6

Structural Health Monitoring (SHM) researchers have identified Augmented Reality (AR) as a new technology that can assist inspections. Post-seismic structural inspections are conducted to evaluate the safety level of the damaged structures. Quantification of nearby structural changes over short-term and long-term periods can provide building inspectors with information to improve their safety. This paper proposes a Time Machine Measure (TMM) application based on an Augmented Reality (AR) Head-Mounted-Device (HMD) platform. The primary function of TMM is to restore the saved meshes of a past environment and overlay them onto the real environment so that inspectors can intuitively measure dynamic structural deformation and other environmental movements. The proposed TMM application was verified by demo experiments simulating a real inspection environment.

System identification of an in-service railroad bridge using wireless smart sensors

Robin E. Kim,Fernando Moreu,Billie F. Spencer, Jr 국제구조공학회 2015 Smart Structures and Systems, An International Jou Vol.15 No.3

Railroad bridges form an integral part of railway infrastructure throughout the world. Toaccommodate increased axel loads, train speeds, and greater volumes of freight traffic, in the presence ofchanging structural conditions, the load carrying capacity and serviceability of existing bridges must beassessed. One way is through system identification of in-service railroad bridges. To dates, numerousresearchers have reported system identification studies with a large portion of their applications beinghighway bridges. Moreover, most of those models are calibrated at global level, while only a few studiesapplications have used globally and locally calibrated model. To reach the global and local calibration, bothambient vibration tests and controlled tests need to be performed. Thus, an approach for systemidentification of a railroad bridge that can be used to assess the bridge in global and local sense is needed. This study presents system identification of a railroad bridge using free vibration data. Wireless smartsensors are employed and provided a portable way to collect data that is then used to determine bridgefrequencies and mode shapes. Subsequently, a calibrated finite element model of the bridge provides globaland local information of the bridge. The ability of the model to simulate local responses is validated bycomparing predicted and measured strain in one of the diagonal members of the truss. This researchdemonstrates the potential of using measured field data to perform model calibration in a simple andpractical manner that will lead to better understanding the state of railroad bridges.

System identification of an in-service railroad bridge using wireless smart sensors

Kim, Robin E.,Moreu, Fernando,Spencer, Billie F. Techno-Press 2015 Smart Structures and Systems, An International Jou Vol.15 No.3

Railroad bridges form an integral part of railway infrastructure throughout the world. To accommodate increased axel loads, train speeds, and greater volumes of freight traffic, in the presence of changing structural conditions, the load carrying capacity and serviceability of existing bridges must be assessed. One way is through system identification of in-service railroad bridges. To dates, numerous researchers have reported system identification studies with a large portion of their applications being highway bridges. Moreover, most of those models are calibrated at global level, while only a few studies applications have used globally and locally calibrated model. To reach the global and local calibration, both ambient vibration tests and controlled tests need to be performed. Thus, an approach for system identification of a railroad bridge that can be used to assess the bridge in global and local sense is needed. This study presents system identification of a railroad bridge using free vibration data. Wireless smart sensors are employed and provided a portable way to collect data that is then used to determine bridge frequencies and mode shapes. Subsequently, a calibrated finite element model of the bridge provides global and local information of the bridge. The ability of the model to simulate local responses is validated by comparing predicted and measured strain in one of the diagonal members of the truss. This research demonstrates the potential of using measured field data to perform model calibration in a simple and practical manner that will lead to better understanding the state of railroad bridges.

Total reference-free displacements for condition assessment of timber railroad bridges using tilt

Ali I. Ozdagli,Jose A. Gomez,Fernando Moreu 국제구조공학회 2017 Smart Structures and Systems, An International Jou Vol.20 No.5

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.

Measuring displacements of a railroad bridge using DIC and accelerometers

Adam Hoag,Neil A. Hoult,W. Andy Take,Fernando Moreu,Hoat Le,Vamsi Tolikonda 국제구조공학회 2017 Smart Structures and Systems, An International Jou Vol.19 No.2

Railroad bridges in North America are an integral but aging part of the railroad network and are typically only monitored using visual inspections. When quantitative information is required for assessment, railroads often monitor bridges using accelerometers. However without a sensor to directly measure displacements, it is difficult to interpret these results as they relate to bridge performance. Digital Image Correlation (DIC) is a non-contact sensor technology capable of directly measuring the displacement of any visible bridge component. In this research, a railroad bridge was monitored under load using DIC and accelerometers. DIC measurements are directly compared to serviceability limits and it is observed that the bridge is compliant. The accelerometer data is also used to calculate displacements which are compared to the DIC measurements to assess the accuracy of the accelerometer measurements. These measurements compared well for zero-mean lateral data, providing measurement redundancy and validation. The lateral displacements from both the accelerometers and DIC at the supports were then used to determine the source of lateral displacements within the support system.