Estimating deformations of laboratory structures subjected to loadings using images collected with phone cameras

A Fundamental civil engineering knowledge is the ability to understand, explain and calculate deformations of/in civil structures. New civil engineers acquire this knowledge at universities in material science, engineering mechanics and introduction to structural engineering modules. Structural deformations can be calculated when knowing material properties, geometry and boundary conditions of a structure and loads that are applied on/to it. Inverse engineering can be employed if deformations of a structure are known, but some other parameters are not known. Theoretical formulae are then tested using laboratory test beds, usually beams. Laboratory technicians are responsible for the acquisition of deformation measurements of laboratory tests. Their time is often limited and so is the number of available measurement collection devices such as dial gauges and strain gauges. If devices that do not log measured deformations are employed, information of structural deformations or response might be lost or readings might not be collected at required intervals. However, all laboratories do not offer luxury of sensors that are available at hand and technicians, which would have time to install sensors and collect data when needed, especially if this has to be done within a short period of notice. This paper introduces a low-cost vision-based system for deformation estimations of laboratory structures

Condition assessment of structures using smartphones: a position independent multi-epoch imaging approach

Applications of vision-based technologies are becoming more prevalent in deformation monitoring of civil structures, especially bridges. Feature recognition, detection and tracking algorithms are developed to analyse structural response. For example, movements of structural features such as bolts in steel bridges can be tracked when a truck crosses a bridge. In order to measure small structural movements, good quality and high resolution images are needed. Developments in smartphone technologies have resulted in very good quality on board cameras. Bridge inspectors could use smartphone technologies during visual inspections as they are readily available. Cameras have been used in structural deformations monitoring, however, the challenge is to make sure that the camera is placed in the same location to allow accurate comparison. This study explores if multi-epoch imaging approach can be used to collect accurately structural displacements when capturing images of a structure from different positions. A laboratory beam served as a testbed. Smartphones placed at different positions were used to capture deformations of the beam in healthy and damaged states. Structural feature were selected, and their location were estimated from images. Feature locations from all smartphones were transformed to the reference coordinate system as derived from one smartphone. Results show that feature locations can be accurately transformed to the reference coordinate system, from which difference between undamaged and damaged states of the beam can be recognized

Data-driven modeling of long temperature time-series to capture the thermal behavior of bridges for SHM purposes

Bridges experience complex heat propagation phenomena that are governed by external thermal loads, such as solar radiation and air convection, as well as internal factors, such as thermal inertia and geometrical properties of the various components. This dynamics produces internal temperature distributions which cause changes in some measurable structural responses that often surpass those produced by any other load acting on the structure or by the insurgence or growth of damage. This article advocates the use of regression models that are capable of capturing the dynamics buried within long sequences of temperature measurements and of relating that to some measured structural response, such as strain as in the test structure used in this study. Two such models are proposed, namely the multiple linear regression (MLR) and a deep learning (DL) method based on one-dimensional causal dilated convolutional neural networks, and their ability to predict strain is evaluated in terms of the coefficient of determination R 2. Simple linear regression (LR), which only uses a single temperature reading to predict the structural response, is also tested and used as a benchmark. It is shown that both MLR and the DL method largely outperform LR, with the DL method providing the best results overall, though at a higher computational cost. These findings confirm the need to consider the evolution of temperature if one wishes to setup a temperature-based data-driven strategy for the SHM of large structures such as bridges, an example of which is given and discussed towards the end of the article.</p

Performance of signal processing techniques for anomaly detection using a temperature-based measurement interpretation approach

This is the final version. Available on open access from Springer via the DOI in this record.This study investigates the effectiveness of four signal processing techniques in supporting a data-driven strategy for anomaly detection that relies on correlations between measurements of bridge response and temperature distributions. The strategy builds upon the regression-based thermal response prediction methodology which was developed by the authors to accurately predict thermal response from distributed temperature measurements. The four techniques that are investigated as part of the strategy are moving fast Fourier transform, moving principal component analysis, signal subtraction method and cointegration method. The techniques are compared on measurement time-histories from a laboratory structure and a footbridge at the National Physical Laboratory. Results demonstrate that anomaly events can be detected successfully depending on the magnitude and duration of the event and the choice of an appropriate anomaly detection technique

A multiple camera position approach for accurate displacement measurement using computer vision

This is the final version. Available on open access from Springer via the DOI in this recordEngineers can today capture high-resolution video recordings of bridge movements during routine visual inspections using modern smartphones and compile a historical archive over time. However the recordings are likely to be from cameras of different makes, placed at varying positions. Previous studies have not explored whether such recordings can support monitoring of bridge condition. This is the focus of this study. It evaluates the feasibility of an imaging approach for condition assessment that is independent of the camera positions used for individual recordings. The proposed approach relies on the premise that spatial relationships between multiple structural features remain the same even when images of the structure are taken from different angles or camera positions. It employs coordinate transformation techniques, which use the identified features, to compute structural displacements from images. The proposed approach is applied to a laboratory beam, subject to static loading under various damage scenarios and recorded using multiple cameras in a range of positions. Results show that the response computed from the recordings are accurate, with 5% discrepancy in computed displacements relative to the mean. The approach is also demonstrated on a full-scale pedestrian suspension bridge. Vertical bridge movements, induced by forced excitations, are collected with two smartphones and an action camera. Analysis of the images shows that the measurement discrepancy in computed displacements is 6%.Sustainable Futures, Nottingham Trent Universit

Data-driven approaches for measurement interpretation: analysing integrated thermal and vehicular response in bridge structural health monitoring

A comprehensive evaluation of a structure's performance based on quasi-static measurements requires consideration of the response due to all applied loads. For the majority of short- and medium-span bridges, temperature and vehicular loads are the main drivers of structural deformations. This paper therefore evaluates the following two hypotheses: (i) knowledge of loads and their positions, and temperature distributions can be used to accurately predict structural response, and (ii) the difference between predicted and measured response at various sensor locations can form the basis of anomaly detection techniques. It introduces a measurement interpretation approach that merges the regression-based thermal response prediction methodology that was proposed previously by the authors with a novel methodology for predicting traffic-induced response. The approach first removes both environmentally (temperature) and operationally (traffic) induced trends from measurement time series of structural response. The resulting time series is then analysed using anomaly detection techniques. Experimental data collected from a laboratory truss is used for the evaluation of this approach. Results show that (i) traffic-induced response is recognized once thermal effects are removed, and (ii) information of the location and weight of a vehicle can be used to generate regression models that predict traffic-induced response. As a whole, the approach is shown to be capable of detecting damage by analysing measurements that include both vehicular and thermal response

Performance evaluation of rail trackbed stiffness: pre and post stabilisation

Excessive deflection of a rail in response to axle loading can lead to discomfort for passengers and increased wear of both railway structures and trains. These oscillations are often caused by poor trackbed stiffness which may be due to either soft subgrade and/or contaminated ballast. A variety of trackbed stabilisation (TBS) techniques are available to remediate soft subgrades and increase the safety of tracks within the railway network. Traditional TBS methods require track removal, which is expensive, disruptive and often inefficient maintenance works. Micro-piling, using screw piles installed between sleepers, is an innovative low disruption TBS technique. This paper investigates the performance of a soft subgrade and contaminated ballast section of rail line in the UK, before and after screw pile TBS. Pre and post remediation, a computer vision-based system was used to measure rail vertical deflections during train passages and then analysed to quantify the trackbed stiffness. Additionally, 3D finite element models are created and validated by the site measurements. The finite element models are used to simulate a range of different scenarios exploring how changes to the TBS piling layout and/or further works, such as ballast improvement could add further improvements or design efficiencies. Site measurements show TBS reduced rail deflection by 20–30%, indicating that micro-piling is an effective technique for soft subgrades. The finite element analysis revealed the efficiency of micro-piling is highly dependent on the conditions of ballast, strength of the ground at the pile toe, and the pile arrangement. When the aforementioned are optimised the rail deflection could be reduced to approximately 50% of the pre TBS condition

The effect of temperature variation on bridges - a literature review

Bridges are commonly subjected to complex load scenarios in their lifetime. Understanding the response of bridges under such load scenarios is important to ensure their safety. While static and dynamic loads from vehicles and pedestrians influence the instantaneous response of bridges, studies show that thermal load from diurnal and seasonal temperature variation influences its long-term response and durability. This study addresses the effects of thermal load variation on bridges and briefly reviews methods of measuring such effects. The findings show that thermally induced deformations in bridges are of magnitude equal or larger than that induced by vehicle induced load. This study highlights the significance of measuring temperature responses of bridges for their robust structural health monitoring

The resilience of vision-based technology for bridge monitoring: measuring and analysing resilience of interdependent STE systems resilient sensing systems for infrastructure monitoring

Robust monitoring of bridges is necessary to ensure its serviceability and traffic safety. A reliable sensing system to measure bridge responses is the key to such monitoring approaches. This research discusses the resilience of vision-based monitoring (VBM) for accurate and reliable bridge monitoring. VBM deploys cameras to capture the movement of bridges and uses suitable image processing algorithms to derive information on bridge health. VBM as a low cost and user-friendly monitoring system for accurate response measurement, simultaneous multiple target tracking and hardware adaptability is assessed based on literature. Measures to make VBM resilient in adverse field conditions are also discussed. Overall findings emphasise the accuracy and reliability of VBM for holistic and cost-efficient monitoring of bridges

Energy Investigation Framework: understanding buildings from an energy perspective view.

According to the United Nations Global Status Report 2017, buildings and construction account for 36% of the global energy use and 39% of energy related carbon dioxide emissions [1]. A prudent design and accurate construction of energy efficient buildings are mandatory to reduce both global energy consumption and carbon dioxide emissions. The desired outcome can be achieved by (i) increasing the construction of low energy buildings, which are designed to sustain desired indoor temperature with a minimal energy input, (ii) and improving the energy performance of existing buildings. Some buildings underperform by gaining or losing more heat than needed. This study introduces a framework for investigating building energy performance. Thermography investigation, building modelling, characterization of thermal bridges and future prediction for overheating are encapsulated in the proposed framework. A sport changing facility, which was designed as a low energy building, serves as a demonstrator for the application of the framework. The energy investigation framework revealed that the facility is underperforming. According to the building model, the main reason for the poor building performance is thermal bridging (presence of steel members), which increases gas consumption and wall heat-loss by 18% and 11%, respectively. Other contributors to heat lose/gain are cracks in the building envelop, weak mortar joints and uninsulated hot water pipes. Furthermore, the future temperature data, which is input to the building model, suggests that the entire facility is under the risk of overheating