Niamh Gibbons

Management of structural monitoring data of bridges using BIM

Engineering and Physical Sciences Research Council, Innovate UK (CSIC Innovation and Knowledge Centre (Grant ID: EP/L010917/1))

Robust fibre optic sensor arrays for monitoring early-age performance of mass-produced concrete sleepers

This study investigates integrating fibre optic sensing technology into the production process of concrete railway sleepers. Robust fibre Bragg grating strain and temperature sensor arrays were developed specifically for this application and were designed for long-term monitoring of sleeper performance. The sensors were used to monitor sleeper production and to help gain a deeper understanding of their early-age behaviour which can highly influence long-term performance. In total, 12 sleepers were instrumented and strain data were collected during the entire manufacturing process including concrete casting and curing, prestressing strand detensioning and qualification testing. Following the production process, sleepers were stored temporarily and monitored for 4 months until being placed in service. The monitoring results highlight the intrinsic variability in strain development among identical sleepers, despite high levels of production quality control. Using prestress loss as a quality control indicator, the integrated sensing system demonstrated that sleepers were performing within Eurocode-based design limits prior to being placed in service. A three-dimensional nonlinear finite element model was developed to provide additional insight into the sleepers’ early-age behaviour. Based on the fibre Bragg grating–calibrated finite element model, more realistic estimates for the creep coefficient were provided and found to be 48% of the Eurocode-predicted values.

Monitoring, Modeling, and Assessment of a Self-Sensing Railway Bridge during Construction

© 2018 American Society of Civil Engineers. This study shows how integrating fiber optic sensor (FOS) networks into bridges during the construction stage can be used to quantify preservice performance. Details of the installation of a large FOS network on a new steel-concrete composite railway bridge in the United Kingdom are presented. An overview of the FOS technology, installation techniques, and monitoring program is also presented, and the monitoring results from several construction stages are discussed. A finite-element (FE) model was developed and a phased analysis was carried out to simulate strain development in the bridge during consecutive construction stages. The response of the self-sensing bridge to the time-dependent properties of the concrete deck was evaluated by comparing FOS measurements to predicted results according to several model code formulations implemented in the FE model. The preservice strain distribution due to dead loading is typically assumed to act uniformly along the bridge length; however, the monitoring results revealed that the distribution was highly variable as a result of the complex interactions between gravity loading, bridge geometry, time-dependent concrete properties, and temperature effects. Moment utilization of the main girders and composite beams, during preservice conditions, was assessed and found to be between 19.3 and 24.9% of the respective design cross-section capacities. Quantifying preservice performance via integrated sensing also provided a critical baseline for the bridge, which enables future data-driven condition assessments.

Research data supporting “Evaluating the Early-Age Behaviour of Full-Scale Prestressed Concrete Beams using Distributed and Discrete Fibre Optic Sensors”

Strain data collected during the casting, curing, detensioning and storage of 4 prestressed concrete bridge beams manufactured at EXPLORE Industrial Park, Worksop, UK. The data was collected/generated for the ME01 project being carried out in the Department of Engineering at the University of Cambridge. The ME01 project is a fibre optic instrumentation and dynamic monitoring programme at Norton Bridge, UK, part of the Stafford Area Improvements Programme. The strain data was collected using fibre optic monitoring technologies based on Brillioun Optical Time Domain Reflectometry (BOTDR) and fibre Bragg gratings (FBG).

Research data supporting “Development of Self-Sensing Concrete Sleepers for Next-Generation Rail Infrastructure”

Strain data collected during the casting, curing, and detensioning of prestressed concrete sleepers manufactured by CEMEX UK in Birmingham. The data was collected for the ME01 project being carried out in the Department of Engineering at the University of Cambridge. The ME01 project is a fibre optic instrumentation and dynamic monitoring programme at Norton Bridge, UK, part of the Stafford Area Improvements Programme. The strain data was collected using fibre optic monitoring technologies based on fibre Bragg gratings.

Research data supporting "Management of structural monitoring data of bridges using BIM"

Strain data collected during the casting and curing of a concrete deck on a single span prestressed concrete rail-over-water bridge near Stafford UK. The data was collected for the ME01 project being carried out in the Department of Engineering at the University of Cambridge and used as a case study in a BIM model as part of the BIM Plus project also underway in the Department of Engineering at the University of Cambridge. The ME01 project is a fibre optic instrumentation and dynamic monitoring programme at Norton Bridge, UK, part of the Stafford Area Improvements Programme (Staffordshire Alliance). The strain data was collected using fibre optic monitoring technologies based on fibre Bragg gratings and was later processed and converted into mechanical strain.

Research data supporting the paper "Integrated fibre-optic sensor networks as tools for monitoring strain development in bridges during construction"

Strain data collected during the casting and curing of a concrete deck on a single span half-through composite rail-over-rail bridge near Stafford UK. The data was collected for the ME01 project being carried out in the Department of Engineering at the University of Cambridge. The ME01 project is a fibre optic instrumentation and dynamic monitoring programme at Norton Bridge, UK, part of the Stafford Area Improvements Programme (Staffordshire Alliance). The strain data was collected using fibre optic monitoring technologies based on fibre Bragg gratings.

Masonry arch bridges – towards a hierarchical assessment framework

Masonry arch bridges are estimated to account for more than 40% of European bridges. Often more than 100 years old these bridges have performed well in service and are arguably the most durable and sustainable bridge type. However, the gradual deterioration of materials with time, coupled with the increase in loading from modern road and rail vehicles, make re-assessment, maintenance, repair and strengthening inevitable in order to ensure that safety, performance and serviceability are sustained at an acceptable level. While numbers of masonry arch bridges in the US are lower these issues are equally relevant. Bridge managers and owners use a variety of assessment algorithms to identify safe load capacities. The underlying theory behind different assessment approaches varies. The required bridge data also varies from method to method as do the dimensional models used. This diversity of assessment methods explains, in part, the resulting scatter of assessment ratings achieved for a single bridge. What is often more difficult to rationalize is the anomaly of an apparently higher level, or more rigorous, assessment algorithm resulting in a lower assessment rating. UCD is working with the National Roads Authority in Ireland to rationalize a hierarchical framework of assessment algorithms whereby increasing assessment effort is rewarded by demonstrable convergence toward the ultimate capacity of the bridge. The aim is to develop an algorithm hierarchy for masonry arch bridges. As part of this work 12 masonry arch bridges, characteristic of the range of bridges on the Irish road network, have been assessed using three assessment methods. This paper presents a discussion of the assessment approaches, the assessment ratings achieved and the trends in assessment ratings of the different assessment approaches vis-a-vis each other and also measured responses, to passing weighed vehicles, for two of the bridges.