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

Toward enhancing community resilience: Life-cycle resilience of structural health monitoring systems

Resilience is becoming a vital quality, particularly as our built environment faces increasing risks due to the aging of our infrastructure and climate change. Moreover, the built environment is relying largely on information technology and becoming digitalized. Consequently, it is crucial to ensure in addition to the resilience of structures and infrastructure, the resilience of the structural health monitoring SHM systems which provide information and help sustain and monitor the functionality of the built environment. The process involved in securing resilience throughout the life cycle of monitoring systems includes 1) planning and preparation before acute shock events and daily stressors, 2) absorption of shock events and 3) recovery and adaption afterward, as well as a continuous adaption for the daily stressors. Furthermore, enhancing the resilience of SHM systems contributes to, as well as improves, the resilience of structures and infrastructures. In this article, the life cycle resilience of SHM systems is discussed including community participation in SHM via crowdsensing. Comparisons between the resilience of SHM systems are presented. Results are discussed, and recommendations are offered

Predicting thermal response of bridges using regression models derived from measurement histories

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Computers and Structures. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Structures Vol. 136 (2014), DOI: 10.1016/j.compstruc.2014.01.026This study investigates the application of novel computational techniques for structural performance monitoring of bridges that enable quantification of temperature-induced response during the measurement interpretation process. The goal is to support evaluation of bridge response to diurnal and seasonal changes in environmental conditions, which have widely been cited to produce significantly large deformations that exceed even the effects of live loads and damage. This paper proposes a regression-based methodology to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period. It compares the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR) and support vector regression (SVR) for application within the proposed methodology. The methodology is successfully validated on measurements collected from two structures – a laboratory truss and a concrete footbridge. Results show that the methodology is capable of accurately predicting thermal response and can therefore help with interpreting measurements from continuous bridge monitoring

Support vector regression for anomaly detection from measurement histories

Copyright © 2013 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Advanced Engineering Informatics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Advanced Engineering Informatics Vol. 27 (2013), DOI: 10.1016/j.aei.2013.03.002This research focuses on the analysis of measurements from distributed sensing of structures. The premise is that ambient temperature variations, and hence the temperature distribution across the structure, have a strong correlation with structural response and that this relationship could be exploited for anomaly detection. Specifically, this research first investigates whether support vector regression (SVR) models could be trained to capture the relationship between distributed temperature and response measurements and subsequently, if these models could be employed in an approach for anomaly detection. The study develops a methodology to generate SVR models that predict the thermal response of bridges from distributed temperature measurements, and evaluates its performance on measurement histories simulated using numerical models of a bridge girder. The potential use of these SVR models for damage detection is then studied by comparing their strain predictions with measurements collected from simulations of the bridge girder in damaged condition. Results show that SVR models that predict structural response from distributed temperature measurements could form the basis for a reliable anomaly detection methodology

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

Characterizing Bridge Thermal Response for Bridge Load Rating and Condition Assessment:A Parametric Study

Temperature is the main driver of bridge response. It is continuously applied and may have complex distributions across the bridge. Daily temperature loads force bridges to undergo deformations that are larger than or equal to peak-to-peak traffic loads. Bridge thermal response must therefore be accounted for when performing load rating and condition assessment. This study assesses the importance of characterizing bridge thermal response and separating it from traffic-induced response. Numerical replicas (i.e., fine element models) of a steel girder bridge are generated to validate the proposed methodology. Firstly, a variety of temperature distribution scenarios, such as those resulting from extreme weather conditions due to climate change, are modelled. Then, nominal traffic load scenarios are simulated, and bridge response is characterized. Finally, damage is modelled as a reduction in material stiffness due to corrosion. Bridge response to applied traffic load is different before and after the introduction of damage; however, it can only be correctly quantified when the bridge thermal response is accurately accounted for. The study emphasizes the importance of accounting for distributed temperature loads and characterizing bridge thermal response, which are important factors to consider both in bridge design and condition assessment

Structural Performance Evaluation of Bridges: Characterizing and Integrating Thermal Response

Bridge monitoring studies indicate that the quasi-static response of a bridge, while dependent on various input forces, is affected predominantly by variations in temperature. In many structures, the quasi-static response can even be approximated as equal to its thermal response. Consequently, interpretation of measurements from quasi-static monitoring requires accounting for the thermal response in measurements. Developing solutions to this challenge, which is critical to relate measurements to decision-making and thereby realize the full potential of SHM for bridge management, is the main focus of this research. This research proposes a data-driven approach referred to as temperature-based measurement interpretation (TB-MI) approach for structural performance evaluation of bridges based on continuous bridge monitoring. The approach characterizes and predicts thermal response of structures by exploiting the relationship between temperature distributions across a bridge and measured bridge response. The TB-MI approach has two components - (i) a regression-based thermal response prediction (RBTRP) methodology and (ii) an anomaly detection methodology. The RBTRP methodology generates models to predict real-time structural response from distributed temperature measurements. The anomaly detection methodology analyses prediction error signals, which are the differences between predicted and real-time response to detect the onset of anomaly events. In order to generate realistic data-sets for evaluating the proposed TB-MI approach, this research has built a small-scale truss structure in the laboratory as a test-bed. The truss is subject to accelerated diurnal temperature cycles using a system of heating lamps. Various damage scenarios are also simulated on this structure. This research further investigates if the underlying concept of using distributed temperature measurements to predict thermal response can be implemented using physics-based models. The case study of Cleddau Bridge is considered. This research also extends the general concept of predicting bridge response from knowledge of input loads to predict structural response due to traffic loads. Starting from the TB-MI approach, it creates an integrated approach for analyzing measured response due to both thermal and vehicular loads. The proposed approaches are evaluated on measurement time-histories from a number of case studies including numerical models, laboratory-scale truss and full-scale bridges. Results illustrate that the approaches accurately predicts thermal response, and that anomaly events are detectable using signal processing techniques such as signal subtraction method and cointegration. The study demonstrates that the proposed TB-MI approach is applicable for interpreting measurements from full-scale bridges, and can be integrated within a measurement interpretation platform for continuous bridge monitoring

Vision-based bridge monitoring using displacement curvatures

Resilience is about improved operational performance and safety by ensuring integrity and redundancy of infrastructure systems. Structural health monitoring (SHM) of old infrastructure like bridges is crucial to ensuring resilience. The fusion of affordable vision-based structural health monitoring (VBSHM) systems with effective damage detection techniques has the potential to provide cost-effective solutions to support condition assessments of such old bridges, - a necessary step towards ensuring their safety and consequently resilience. With VBSHM, distributed sensing along a bridge is attainable, after which image-processing can be used to obtain bridge response. One of such responses is curvature. The curvature technique involves fitting a curve to response from tracked targets, extracting their quadratic coefficients across all loading timesteps, and taking the maximum coefficient as bridge response. Feasibility of this technique is demonstrated on a numerical model of a truck-loaded bridge girder subjected to multiple damage scenarios. Noise is also added to replicate real-world scenarios. Damages can be detected and localised but are influenced by damage extent and measurement noise. The technique shows potential for field applications