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Edge computing places the computational services and resources closer to the user proximity, to reduce latency, and ensure the quality of service and experience. Low latency, context awareness and mobility support are the major contributors to edge-enabled smart systems. Such systems require handling new situations and change on the fly and ensuring the quality of service while only having access to constrained computation and communication resources and operating in mobile, dynamic and ever-changing environments. Hence, adaptation and self-organisation are crucial for such systems to maintain their performance, and operability while accommodating new changes in their environment. This article reviews the current literature in the field of adaptive edge computing systems. We use a widely accepted taxonomy, which describes the important aspects of adaptive behaviour implementation in computing systems. This taxonomy discusses aspects such as adaptation reasons, the various levels an adaptation strategy can be implemented, the time of reaction to a change, categories of adaptation technique and control of the adaptive behaviour. In this article, we discuss how these aspects are addressed in the literature and identify the open research challenges and future direction in adaptive edge computing systems. The results of our analysis show that most of the identified approaches target adaptation at the application level, and only a few focus on middleware, communication infrastructure and context. Adaptations that are required to address the changes in the context, changes caused by users or in the system itself are also less explored. Furthermore, most of the literature has opted for reactive adaptation, although proactive adaptation is essential to maintain the edge computing systems' performance and interoperability by anticipating the required adaptations on the fly. Additionally, most approaches apply a centralised adaptation control, which does not perfectly fit the mostly decentralised/distributed edge computing settings. [ABSTRACT FROM AUTHOR]

Drive-by bridge monitoring utilizes measured responses from passing vehicles to perform damage detection of bridge, a methodology challenged by multiple factors and operational conditions. Recently, data-driven methods have been used to improve the accuracy of drive-by monitoring. This thriving research field requires (but lacks) publicly available datasets to improve and validate its monitoring and damage detection capabilities. To foster data-driven drive-by bridge damage assessment methods, this document presents an openly available dataset consisting of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. The dataset includes results for different monitoring scenarios, road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field. In addition, four recently published data-driven drive-by methods have been tested using the same dataset. [ABSTRACT FROM AUTHOR]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

Students' engagement is a fundamental challenge in large classrooms in higher education. In recent years, innovative technologies such as electronic learning and online polling platforms have made learning more engaging, effective, and interactive. By using these platforms, educators can create more inclusive and enriching learning environments. This paper presents a novel approach in which an online technology is employed to enhance students' learning experience. In this approach, features of an online polling platform, i.e., Slido, are employed to enhance students' engagement in an engineering module, i.e., 'Mechanics of Solids', which is recognised as a fundamentally challenging module with difficult subjects. This study investigates how the interactive features of such technologies, such as real-time polls, question and answer (Q&A) sessions, and quizzes, can provide a more active and practical learning environment by improving student engagement in the classroom. In total, six online polls were designed: one for ice-breaking, two on the topics of shear forces and bending moment, two on stresses, and one on deflection. Each poll was presented to the students, and they participated in them by scanning a QR code or typing the poll's code online. The rate of students' participation in polls is extensively discussed to show the effectiveness of the proposed method. The findings of this study show a significant increase in student participation in classroom activities compared to traditional methods. Student feedback also indicates a positive learning experience with the use of the proposed approach. It is shown that the proposed approach has the potential to transform the way engineering students engage with challenging subjects, leading to enhanced learning outcomes and a more positive learning experience. [ABSTRACT FROM AUTHOR]

Simplified methods are often used for load estimations during the initial design of the foundations of offshore wind turbines (OWTs). However, the reliability of simplified methods for designing different OWTs needs to be studied. This paper provides a comparative study to evaluate the reliability of simplified approaches. The foundation loads are calculated for OWTs at the mudline level using a simplified approach and OpenFAST simulations and compared. Three OWTs, NREL 5 MW, DTU 10 MW, and IEA 15 MW, are used as reference models. An Extreme Turbulence Model wind load at a rated wind speed, combined with a 50-year Extreme Wave Height (EWH) and Extreme Operating Gust (EOG) wind load and a 1-year maximum wave height are used as the load combinations in this study. In addition, the extreme loads are calculated using both approaches for various metocean data from five different wind farms. Further, the pile penetration lengths calculated using the mudline loads via two methods are compared. The results show that the simplified method provides conservative results for the estimated loads compared to the OpenFAST results, where the extent of conservativism is studied. For example, the bending moment and shear force at the mudline using the simplified approach are 23% to 69% and 32% to 53% higher compared to the OpenFAST results, respectively. In addition, the results show that the simplified approach can be effectively used during the initial phases of monopile foundation design by using factors such as 1.5 and 2 for the shear force and bending moment, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a forced vibration analysis of open doubly curved sandwich panels subjected to a moving constant force. In this paper, the effect of softness of the core is considered by implementing a semi-layerwise theory. To this aim, the first-order shear deformation theory is adopted for the face sheets and a higher-order theory which was obtained based on 3D elasticity theory is considered for the core. The presented formulation is general and as the deepness parameter is accounted in the strain–displacement relations, the formulation can be used for a wide range of deep as well as shallow doubly curved shells. To obtain the dynamic response of the system, the finite element method (FEM) along with the Newmark method is used. The proposed element is a higher-order one with nine nodes and each node has fifteen degrees of freedom. The effect of various parameters such as length-to-thickness ratio, in-plane aspect ratio, boundary conditions, lamination scheme, and fiber orientation angles on the dynamic response of the structure is examined. Additionally, the critical velocity of the force at which the structure experiences maximum dynamic deflection is obtained in each case. The results show that as the length-to-thickness ratio of the structure increases, the dynamic magnification factor curve increases with respect to non-dimensional velocity. This study provides insights into the dynamic behavior of doubly curved sandwich panels with soft cores and can aid in the design of such structures for specific applications. The results of this study can also serve as a benchmark for future studies on the forced vibration behavior of doubly curved sandwich panels. [ABSTRACT FROM AUTHOR]

This paper proposes a new deep-learning framework for drive-by bridge condition monitoring. The proposed approach represents a bridge monitoring regime that enables the presence, type, location, and severity of bridge damage to be identified purely from measurements taken on a passing vehicle, without needing any pre-measured training data. The computational framework adopts a numerical vehicle--bridge interaction (VBI) model, which is automatically calibrated using only the vehicle vibration measurements. The calibrated model is used to generate labeled training data, eliminating the practical difficulties associated with collecting training data for damaged bridge scenarios. The numerically simulated data are used to train a convolutional neural network that can then classify the actual damage characteristics of a bridge. The method is tested using a laboratory-scale VBI model, and results show that the algorithm can accurately identify the presence of seized bearings and cracking in the bridge beams. The algorithm shows good accuracy when identifying the type and extent of damage. The location of bearing damage can generally be identified; however, the location of cracking is less accurate at lower damage levels. The proposed framework represents a significant improvement on existing techniques for indirect bridge monitoring. [ABSTRACT FROM AUTHOR]

Offshore wind energy is considered as one of the most promising resources of clean and renewable energy to replace fossil fuels. Additionally, its cost is expected to be lower than onshore wind energy as the technology matures. Offshore wind turbines (OWTs) normally operate in harsh ocean environments, which could impact their structural integrity. Scour erosion around foundations of OWTs can substantially change the overall stiffness of these structures and shorten their lifetime. Currently, there are a limited number of studies on countermeasures and their engineering requirements for decreasing the scouring effect; this is due to their different hydraulic circumstances, such as their stability, reliability, and resistance capacity. To this end, advancements in scour protection measures in the offshore energy sector are evaluated in this paper through a thorough and critical review following the PRISMA systematic literature mapping approach. This includes 68 papers on scour protection and over 30 scour protection designs for various types of wind turbine foundations. Here, we aim to provide an overview of the latest scouring protection measures and their comprehensive assessment, as well as their prospects and future challenges. The findings of this study will provide key insights into scour protection measures for OWTs and will subsequently contribute to the future growth of the offshore renewable energy sector. [ABSTRACT FROM AUTHOR]

Motorway incidents are frequent & varied in nature. Incident management on motorways is critical for both driver safety & road network operation. The expected duration of an incident is a key parameter in the decision-making process for control room operators, however, the actual duration for which an incident will impact the network is never known with true certainty. This paper presents a study which compares the ability of different machine learning algorithms to estimate the duration of motorway incidents on Ireland's M50 motorway, using an extensive historical incident database. Results show that the support vector machine has the best performance in most cases, but a different method may need to be used to improve accuracy in some situations. Results highlight the main challenges in accurately forecasting incident durations in real time & recommendations are made for improving prediction accuracy through systematic recording of various additional incident details. [ABSTRACT FROM AUTHOR]

The article discusses the recent surge in investments in railway infrastructure worldwide and the importance of ensuring the safety and integrity of these infrastructures. It highlights advancements in structural condition monitoring for railway infrastructures, including the integration of artificial intelligence and machine learning algorithms, as well as the use of unmanned aerial vehicles for inspections. The article also provides a summary of 10 specific contributions in the field, covering topics such as rail surface defect detection, overhead contact line monitoring, object detection using computer vision, and digitalization of freight wagons. These advancements contribute to the efficiency, reliability, and sustainability of railway transportation. [Extracted from the article]

The damages in reinforced concrete (RC) beams due to reinforcement corrosion is a major problem in the RC industry. Accurate prediction of the residual bearing capacity of RC beams can effectively prevent structural failures or unwanted over-costs of inspections and rehabilitations. This paper proposes a novel machine learning-based prediction framework that combines the adaptive neural fuzzy inference system (ANFIS) with several metaheuristic algorithms for the effective estimation of the flexural strength capacity. Five optimization algorithms are employed for auto-selection of the optimum ANFIS parameters, including differential evolution (DE), genetic algorithm, particle swarm optimization, artificial bee colony, and firefly algorithm (FFA). A comprehensive experimental database of the flexural capacity of corroded steel reinforced concrete beams obtained from the literature, consisting of 177 tests, is used as a case study to evaluate the prediction performance of the proposed hybrid models. The results demonstrate that the proposed hybrid models transcend the previously developed models, while the optimized ANFIS using FFA represents the highest accuracy and strong stability among the proposed models. It is concluded that the proposed framework using ANFIS-FFA can be effectively employed as a useful tool for the accurate estimation of the flexural strength capacity of corroded reinforced concrete beams. [ABSTRACT FROM AUTHOR]

In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed. [ABSTRACT FROM AUTHOR]

This paper presents a free vibration analysis of deep doubly curved laminated composite sandwich shells with soft core under various boundary conditions, using a novel C 0 efficient nine-node quadrilateral element. The proposed model is formulated based on the extended higher-order sandwich panel theory (EHSAPT) in which the compressibility as well as in-plane stresses of the core are considered. Accordingly, the first-order shear deformation theory is used to model the face sheets, while third- and second-order functions are assumed to model the in-plane and transverse displacements of the core, respectively, maintaining an interlaminar displacement continuity. The important feature of this model in comparison with other layerwise models is that by increasing the number of lamina layers, the number of variables is fixed and does not increase, which makes this model an appropriate choice for facilitating engineering analysis. The equations of motion and relevant boundary conditions for sandwich structure are derived using Hamilton's principle. Convergence and comparison studies are carried out to demonstrate the efficiency and accuracy of the solution. The method is validated against the results found in the literature based on various analytical and numerical methods. The comparison studies indicate that the developed finite element model is more accurate, fast convergence and simpler in comparison with results found in the literature and it is valid for both thin and thick sandwich plates and shells. Finally, a parametric study is performed to illustrate the effect of various parameters on vibration characteristics of cylindrical, spherical and hyperbolic paraboloid sandwich panels along with sandwich plates with different boundary conditions which may be used as benchmark results for future studies. [ABSTRACT FROM AUTHOR]

The initial design phase for offshore wind farms does not require complete geotechnical mapping and individual cone penetration testing (CPT) for each expected turbine location. Instead, background information from open source studies and previous historic records for geology and seismic data are typically used at this early stage to develop a preliminary ground model. This study focuses specifically on the interpolation and extrapolation of cone penetration test (CPT) data. A detailed methodology is presented for the process of using a limited number of CPTs to characterise the geotechnical behavior of an offshore site using artificial neural networks. In the presented study, the optimised neural network achieved a predictive error of 0.067 . Accuracy is greatest at depths of less than 10 m. The pitfalls of using machine learning for geospatial interpolation are explained and discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid tuned mass damper (TMD) and tuned liquid damper (TLD) system (TMD + TLD) for vibration control of multi-degree of freedom structures under seismic excitations. The proposed system is introduced using equations of motions of a five-degree of freedom structure which is modeled as a shear-type with a lateral degree of freedom at each floor under harmonic and earthquake excitations. The proposed hybrid TMD + TLD system is introduced to the building structure in the model. Several case studies are investigated to evaluate the performance of the proposed system. In the first case, both TMD and TLD are tuned to the first frequency of the structure, and the TMD is fixed to the top floor, and the TLD is fitted to the fourth floor of the primary structure. In the second case, the TMD is tuned to the first frequency of the structure, whereas the TLD is tuned to the second frequency of the structure. All the cases are investigated under harmonic force and real earthquake ground motions. The earthquakes selected in this study are classified as low, intermediate, and high-frequency content earthquakes. Results indicated that with comparatively less mass ratio, the earthquake-induced structural vibration is successfully suppressed when the TMD is tuned to the 1st frequency, and the TLD is tuned to the 2nd frequency of the structure. The focus of the investigation is to develop a practical framework to demonstrate the efficiency of such a hybrid damper. [ABSTRACT FROM AUTHOR]

In this paper, a field study is carried out to monitor the natural frequencies of Malahide viaduct bridge which is located in the north of Dublin. The bridge includes a series of simply supported spans, two of which collapsed in 2009 and were replaced. The replaced spans are stiffer than most of the others and these differences resulted in higher natural frequencies. An indirect bridge monitoring approach is employed in which acceleration responses from an instrumented train are used to estimate the natural frequencies of each span of the viaduct showing the locations of the two replaced spans with higher stiffness. For the indirect approach, an Ensemble Empirical Mode Decomposition (EEMD)-based Hilbert Huang Transform (HHT) technique is employed to identify the natural frequency of each span. This is carried out by analysing the Instantaneous Frequencies (IFs) from the calculated intrinsic mode functions. The average of the IFs calculated using 41 runs of the instrumented train (with varying carriage mass and speed for each run) are used to estimate the natural frequencies. To assess the feasibility of the indirect approach, a bespoke set of direct measurements was taken using accelerometers attached successively on each span of the viaduct. The free and forced vibrations from each span are used to estimate the first natural frequencies. The frequencies obtained from drive-by measurements are compared to those from direct measurements which confirms the effectiveness of indirect approaches. In addition, the instantaneous amplitudes of the drive-by signals are used to indicate the location of the stiffer spans. Finally, the accuracy and robustness of the indirect approaches for monitoring of multi span bridges are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges. [ABSTRACT FROM AUTHOR]

This paper uses simulated deflections on a bridge taken from a model of an instrumented moving vehicle in an attempt to back calculate the distribution of flexural rigidity along the bridge length and hence to evaluate bridge condition. Eight sensors are simulated at different locations on the vehicle. It is assumed that measured deflections are absolute, i.e. they have been corrected for vehicle motion and that the characteristics of the vehicle are known. The unit load theorem is used to relate the measured deflections to the reciprocals of flexural rigidity at every location on the bridge. A Blackman window filter is used to regularise the problem and to improve the conditioning of an ill-conditioned system of equations. In addition, a moving average filter is used to reduce the influence of noise and dynamics, which are sources of inaccuracy in this study. This method is shown to give good results in indicating the presence and intensity of bridge damage and contains some weak information on location. [ABSTRACT FROM AUTHOR]

Summary: This paper demonstrates the potential to use measured deflection responses of a bridge to random traffic as a means of monitoring its structural health. Three displacement transducers are simulated on the bridge at different locations, providing absolute deflection data. The stiffness profile through the length of the bridge is calculated using these data. Vehicle axle loads are calculated as well as the relative bridge stiffness profile using a combined iteration approach. Axle spacings for each vehicle are currently measured in Bridge Weigh‐in‐Motion systems and are presumed to be known here. After estimating axle weights for each passing vehicle, the profile of bridge stiffness is calculated from batches of 1,000 vehicles with a focus on detecting any local damage. A moving average filter and a filtering method based on the Blackman window are used to reduce dynamic effects and the influence of noise. This stiffness profile approach is shown to be suitable for both damage location and quantification. [ABSTRACT FROM AUTHOR]

This paper examines the nature of traffic loading in recurrent congested traffic conditions on a long-span suspension bridge. Traffic flow and percentage of trucks are extracted from image data and a cluster analysis performed to classify the data into four clusters. One cluster (MTHF, medium truck percentage and high flow) is identified that incorporates almost 50% of the hours of traffic data scattered throughout the day. Site-specific load assessment confirms that this MTHF cluster is the most critical for the bridge considered, the Forth Road Bridge in Scotland. For non-recurrent congestion, another cluster (HTLF, high percentage of trucks and low flow) is shown to govern but this finding is highly site-specific, depending on the relative frequency of the different types of congestion. A comparison of the maximum hourly/daily MTHF load effect of the cable force for five notional bridges shows that a 100% increase in the bridge span generates an increase of about 65% in the characteristic load effect. [ABSTRACT FROM AUTHOR]

This paper investigates the use of drive-by train measurements for railway track monitoring. An in-service Irish Rail train was instrumented while using accelerometers and a global positioning system. The measurements were taken over two months and the train bogie accelerations from 60 passes on the Dublin-Belfast line were used for this study. A 6 km section of the line is the particular focus, where the maintenance measurements from a Track Recording Vehicle (TRV) were available. The Hilbert transform is used to obtain the instantaneous amplitudes of the acceleration signals. A new representation of the signal is proposed to show the signal energy level as a function of train location. It is shown that the forward speed of the train has a significant influence on the energy level of the signals. Therefore, a two-step speed correction is applied to the data. First, data from passes with forward speed below a certain limit are removed from the data set. Subsequently, a scaling factor is defined for the remaining signals and the energy levels of those signals are scaled while using online speed measurements. The scaled amplitudes are compared with the TRV data. It is shown that the energy levels of the signals match the TRV measurements very well. [ABSTRACT FROM AUTHOR]

This paper investigates the estimation of foundation damping in a monopile supported offshore wind turbine. The soil-structure interaction is modelled using the commercial geotechnical Finite Element (FE) software, Plaxis 3D. This allows for a more rigorous consideration of the soil response and its effect on the overall dynamic behaviour of the system. A free vibration test is simulated by applying and removing a constant horizontal static load at the top of the tower. The structure starts free vibration when the load is removed. The free decay displacement response is measured at the point of loading. The well-known logarithmic decrement method is used for the estimation of overall damping from the free decay response. The damping is estimated at different time steps along the signal to provide an instantaneous damping. It is shown that the damping varies with the amplitude of the decaying displacement response. [ABSTRACT FROM AUTHOR]

The Ibrahim time domain (ITD) identification technique was one of the first techniques formulated for multiple output modal analysis based on impulse response functions or general free decays. However, the technique has not been used much in recent decades due to the fact that the technique was originally formulated for single input systems that suffer from well-known problems in case of closely spaced modes. In this paper, a known, but more modern formulation of the ITD technique is discussed. In this formulation the technique becomes multiple input by adding some Toeplitz matrices over a set of free decays. It is shown that a special participation matrix can be defined that cancels out whenever the system matrix is estimated. The participation matrix becomes rank deficient if a mode is missing in the responses, but if any mode is present in one of the considered free decays, the participation matrix has full rank. This secures that all modes will be contained in the estimated system matrix. Finally, it is discussed how correlation functions estimated from the operational responses of structures can be used as free decays for the multiple-input ITD formulation, and the estimation errors of the identification technique are investigated in a simulation study with closely spaced modes. The simulation study shows that the multiple-input formulation provides estimates with significantly smaller errors on both mode shape and natural frequency estimates. [ABSTRACT FROM AUTHOR]

There is potential for significant savings if the safety of existing bridges can be more accurately assessed. For long‐span bridges, congestion is the governing traffic load condition. The current methods of simulating congestion make assumptions about the axle‐to‐axle gaps maintained between vehicles. There is potential for improvement in congestion models if accurate data on axle‐to‐axle gaps can be obtained. In this study, the use of a camera to collect this information is put forward. A new image processing technique is proposed to detect wheels in variable light conditions. The method is based on a pseudo‐wavelet filter that amplifies circles, in conjunction with an algorithm that weights features in the image according to their circularity. This new approach is compared with the Hough transform, template matching and the deformable part‐based model (DPM) methods previously developed. In a sample set of 80 images, 96.9% of wheels are detected, considerably more than with the Hough transform and template matching methods. It also provides the same level of accuracy as DPM without requiring a training process. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the feasibility of detecting local damage in a bridge using Laser Doppler Vibrometer (LDV) measurements taken from a vehicle as it passes over the bridge. Six LDVs are simulated numerically on a moving vehicle, collecting relative velocity data between the vehicle and the bridge. It is shown that Instantaneous Curvature (IC) at a moving reference, which is the curvature of the bridge at an instant in time, is sensitive to local damage. The vehicle measures Rate of Instantaneous Curvature (RIC), defined as the first derivative of IC with respect to time. A moving average filter is found to reduce the effects of noise on the RIC data. A comparison of filtered RIC measurements in healthy and damaged bridges shows that local damage can be detected well with noise-free measurements and can still be detected in the presence of noise. [ABSTRACT FROM AUTHOR]

This paper describes a new procedure for bridge damage identification through drive-by monitoring. Instantaneous curvature (IC) is presented as a means to determine a local loss of stiffness in a bridge through measurements collected from a passing instrumented vehicle. Moving reference curvature (MRC) is compared with IC as a damage detection tool. It is assumed that absolute displacements on the bridge can be measured by the vehicle. The bridge is represented by a finite element (FE) model. A Half-car model is used to represent the passing vehicle. Damage is represented as a local loss of stiffness in different parts of the bridge. 1% random noise and no noise environments are considered to evaluate the effectiveness of the method. A generic road surface profile is also assumed. Numerical simulations show that the local damage can be detected using IC if the deflection responses can be measured with sufficient accuracy. Damage quantification can be obtained from MRC. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm for bridge damage detection based on the mode shapes estimated from a passing vehicle. The bridge response at the moving coordinate is measured from an instrumented vehicle with laser vibrometers and accelerometers. A modified version of the Short Time Frequency Domain Decomposition method is applied to the measured responses. The bridge mode shape is estimated with high resolution, which is appropriate for damage detection. A damage index based on mode shape squares is used to detect the presence and location of the damage. A numerical case study of a half-car model passing over a bridge is described in this paper, which validates the performance of the proposed approach. Several damage scenarios are considered including different locations and severities. It is shown that the presence and location of the damage can be detected with acceptable accuracy when the vehicle is moving very slowly. In addition, the performance of the method using higher vehicle speeds is investigated and shows that the approach works well for speeds up to 8 m/s. The sensitivity of the algorithm to measurement noise is also studied by adding several levels of noise to the responses measured on the vehicle. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Indirect bridge monitoring methods, using the responses measured from vehicles passing over bridges, are under development for about a decade. A major advantage of these methods is that they use sensors mounted on the vehicle, no sensors or data acquisition system needs to be installed on the bridge. Most of the proposed methods are based on the identification of dynamic characteristics of the bridge from responses measured on the vehicle, such as natural frequency, mode shapes, and damping. In addition, some of the methods seek to directly detect bridge damage based on the interaction between the vehicle and bridge. This paper presents a critical review of indirect methods for bridge monitoring and provides discussion and recommendations on the challenges to be overcome for successful implementation in practice. [ABSTRACT FROM AUTHOR]

This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels. [ABSTRACT FROM AUTHOR]

A vibration-based bridge scour detection procedure using a cantilever-based piezoelectric energy harvesting device (EHD) is proposed here. This has an advantage over an accelerometer-based method in that potentially, the requirement for a power source can be negated with the only power requirement being the storage and/or transmission of the data. Ideally, this source of power could be fulfilled by the EHD itself, although much research is currently being done to explore this. The open-circuit EHD voltage is used here to detect bridge frequency shifts arising due to scour. Using one EHD attached to the central bridge pier, both scour at the pier of installation and scour at another bridge pier can be detected from the EHD voltage generated during the bridge free-vibration stage, while the harvester is attached to a healthy pier. The method would work best with an initial modal analysis of the bridge structure in order to identify frequencies that may be sensitive to scour. Frequency components corresponding to harmonic loading and electrical interference arising from experiments are removed using the filter bank property of singular spectrum analysis (SSA). These frequencies can then be monitored by using harvested voltage from the energy harvesting device and successfully utilised towards structural health monitoring of a model bridge affected by scour. [ABSTRACT FROM AUTHOR]