Scour Detection Research Articles

Investigating the influencing parameters with automated scour severity detection using Bayesian neural networks

ABSTRACT This research proposes a novel approach leveraging deep learning techniques to enhance scour severity prediction that aims to analyse various parameters’ effects on scour severity and scour patterns around piers under different bed conditions. The methodology involves integrating deep learning techniques, including Linear Discriminant Analysis-t-Distributed Stochastic Neighbour Embedding (LDA-t-SNE) and Bayesian Neural Networks (BNNs), to extract features, reduce dimensionality, and improve detection accuracy. This study conducted correlation analysis to understand the relationships between parameters such as drainage area, stream slope, pier characteristics, flow dynamics, and sediment properties and their influence on scour severity. Additionally, sensitivity analysis was performed to assess the impact of different pier shapes and bed conditions on scour severity. Our results demonstrate the effectiveness of the proposed approach, with metrics such as RMSE (%) values of 0.025 and MAE (%) values of 0.011 and 0.01, respectively, outperforming traditional scour detection methods, achieving an accuracy of 98% consistently affirming the superior accuracy and reliability of the model. This research provides valuable insights into proactive scour management and infrastructure resilience, offering practical solutions for safeguarding hydraulic structures against scour-induced risks in civil engineering applications.

Bridge scour detection method based on Siamese neural networks under bridge-vehicle-wave interaction

Bridge scour is one of the predominant factors in the failure of bridges located over waterways. Therefore, a novel bridge scour detection method is proposed in this study. Specifically, a three-dimensional bridge-vehicle-wave interaction model is developed to obtain vibration simulation data under multiple scour damage scenarios. The real bridge vibration signal and the model simulation data are then fed into the Siamese neural network. The Siamese neural network can be utilized to match the scour damage features of both and measure their similarity to capture the structural changes of the real bridge caused by scour damage. The effectiveness of the proposed method can be verified by the field test case, in which the prediction accuracy of the scouring location reaches 81.11%, and the prediction accuracy of the scouring damage level reaches 78.06%. Furthermore, the influence of vehicle speed and transducer arrangement on bridge scour detection accuracy is discussed. The results show that the proposed method has the potential to adequately extract and transfer the knowledge of scour patterns from the numerical model to detect the scour damage of real bridges.

Photoelectric sensors for wireless monitoring of bridge scour – laboratory investigation and field validation

Scour, or the erosion of bed material is a major cause of bridge failure across the world. Monitoring scour levels at bridge foundations reduces the risk of failure through timely condition-based maintenance. This paper evaluates the use of photoelectric sensors for scour detection through laboratory studies and subsequent field investigation. Two types of photoelectric sensors, namely diffusive-reflective and through-beam, were independently investigated. The sensors were installed at six distinct depths on a simulated bridge pier in a laboratory flume. Scour resulting from hydrodynamic action triggered the sensors at different levels, enabling scour depth detection. An inverse response from the sensors detected scour refill. Following successful laboratory tests, a photoelectric scour-sensing prototype was installed in a small creek in August 2019 which continued to monitor scour until April 2022. The prototype response confirmed laboratory results and continues to perform well under various field conditions such as rain, debris, and snow. The very low-cost system required minimal power and bandwidth, and the sensing component was robust to flow parameters. Long-term field studies are required to evaluate their susceptibility to biofouling and develop biofouling countermeasures.

Surrogate-assisted finite element model updating for detecting scour depths in a continuous bridge

In this study, the Kriging surrogate-assisted method is proposed to improve the computational efficiency in finite element model updating (FEMU) based scour depth detection for the first time. Unlike the conventional methods of FEMU that update the structural parameters by the frequency surrogate models, the proposed method further takes the mode shapes into account by constructing the modal assurance criterion (MAC) surrogate models. The bi-objective functions of the frequency discrepancies and MAC values are directly optimized by the nondominated sorting genetic algorithm II to circumvent the specification of weighting factors as required in single-objective optimization. The preferred solution is selected from the nondominated solutions by the criterion of minimum distance from the equilibrium point. As the sensitivity analysis indicates that the transverse vibration modes are sensitive to the bridge scour, the transverse mode shapes measured on the bridge piers are adopted in scour detection. The illustrative examples show that the proposed method can greatly reduce the computational time with a competitive estimation of the scour depth vector. The parametric study further demonstrates the ability of the proposed method to yield satisfactory results even using the modal parameters of the first two modes with three measurement points.

Physics-Informed Optimization for Dynamic Soil–Structure Interaction Analysis of a Pile Partially Embedded in Nonlayered Soils

This study proposes a novel physics-informed optimization approach to account for nonlayered soil conditions for the dynamic soil–structure interaction analysis that is critical for structural health monitoring involving soil–structure interaction. This approach can estimate soil properties based on a few measurements and then predict the natural frequency of structures, a key element that has been very often utilized for structural health monitoring and evaluation. A case study for frequency-based bridge scour detection has been presented for the demonstration of this approach, where a bridge pile is partially embedded in complex nonlayered soils. With this case study, we show that the nonlinear frequency–scour depth relationship can be accurately estimated using 2–4 measured data points, which is in favor of situations where conducting a field test is difficult or the demand for conducting field measurements needs to be reduced. The impact of errors resulting from measurement uncertainty on the performance of this approach is found to be insignificant. This physics-informed optimization approach thus can help analyze dynamic responses of engineering structures in complex nonlayered soil conditions involving soil–structure interaction.

Scour Detection with Monitoring Methods and Machine Learning Algorithms—A Critical Review

Foundation scour is a widespread reason for the collapse of bridges worldwide. However, assessing bridges is a complex task, which requires a comprehensive understanding of the phenomenon. This literature review first presents recent scour detection techniques and approaches. Direct and indirect monitoring and machine learning algorithm-based studies are investigated in detail in the following sections. The approaches, models, characteristics of data, and other input properties are outlined. The outcomes are given with their advantages and limitations. Finally, assessments are provided at the synthesis of the research.

Laboratory Investigation on Detecting Bridge Scour Using the Indirect Measurement from a Passing Vehicle

For bridges with surface foundations, scour is one of the main reasons for bridge failures. In regard to structural health monitoring, vibration-based scour detection techniques have received increasing attention over the past two decades. Scour occurs below the water surface in rivers or sea, leading to difficulty in equipment installation and maintenance. Recently, the concept of “drive-by” SHM using the indirect measurement of passing vehicle responses has been developed rapidly due to its convenience and low cost. This paper proposes a method to detect scour using the vehicle responses under an operational vehicle speed. The wavelet transform was applied to vehicle accelerations to obtain the wavelet energy. It was found that the wavelet energy increases with the increase in the scour damage level. However, the wavelet energy may also be affected by the on-site operating environments, such as sensor noise and other variabilities, which interferes with the identification of scour in practice. Hence, in this work, a statistical-wavelet-based approach was presented to effectively detect the presence of scour and even its location. The feasibility of the proposed approach is verified in both numerical simulation and lab experiments. The results show that the proposed method has a good potential to detect scour using indirect measurements.

Methods to detect and measure scour around bridge foundations

Bridges are indispensable structures vital to the operation of road and rail transportation networks. Crossing rivers and artificial waterways, however, presents a risk to their foundations due to scour actions. Scour is the number one cause for bridge failures and may occur beneath any bridge, large or small, with supports located within the waterway. This paper provides a summary of present scour detection and measurement equipment and associated assessment methodologies. In this regard, particular emphasis is placed on structural health monitoring better to evaluate the presence and influence of potential scour. A Sensitivity Analysis on a newly introduced monitoring system is also assumed. Furthermore, much research has been undertaken to create a technology that can instantly identify and detect bridge scour, improving survey reliability through prior inspection and prompt intervention. This research will explore and evaluate bridge scour detection methods employed and suggest a possible path for developing the detection system to identify scour depth effectively and efficiently. Finally, our key aim is to minimize human effort in identifying and bridge scour by using a quick, easy-to-use, cost-effective process, resulting in fewer injuries and economic savings.

Piezoelectric rod sensors for scour detection and vortex-induced vibration monitoring

As extreme events increase in frequency, flow-disrupting large-scale structures become ever more susceptible to collapse due to local scour effects. The objective of this study was to validate the functionality of passive, flow-excited scour sensors that can continue to operate during an extreme event. The scour sensors, or piezo-rods, feature continuous piezoelectric polymer strips embedded within and along the length of slender cylindrical rods, which could then be driven into the soil where scour is expected. When scour erodes away foundation material to reveal a portion of the piezo-rod, ambient fluid flow excitations would cause the piezoelectric element to output a voltage response corresponding to the dynamic bending strains of the sensor. The voltage response is dependent on both the structural dynamic properties of the sensor and the excitation fluid’s velocity. By monitoring both shedding frequency and flow velocity, the exposed length of the piezo-rod (or scour depth) can be calculated. Two series of experimental tests were conducted in this work: (1) the piezo-rod was driven into sediment around a mock pier to collect scour data, and (2) the piezo-rod was used to monitor its own structural response by collecting vortex-shedding frequency data in response to varied flow velocities to establish a velocity–frequency relationship. The results showed that the piezo-rod successfully captured structural vortex-shedding frequency comparable to state-of-practice testing. A one-dimensional numerical model was developed using the velocity–frequency relationship to increase the accuracy of voltage-based length prediction of the piezo-rod. Two-dimensional flow modeling was also performed for predicting localized velocities within a complex flow field. These velocities, in conjunction with the velocity–frequency relationship, were used to greatly improve length-predictive capabilities.

Comprehensive Application of Multi-beam Sounding System and Side-scan Sonar in Scouring Detection of Underwater Structures in Offshore Wind Farms

In recent years, the installed capacity and power generation of offshore wind power have continued to grow rapidly, operation and maintenance testing has faced many technical challenges. Among them, the monitoring of the scour status of the underwater structure of the wind turbine and the complete basic data of the wind turbine's full life cycle management system are of great significance to ensuring the long-term safe and stable operation of the offshore wind farm. This paper introduces the application of a combined multi-beam bathymetric system and side-scan sonar in the scour detection of underwater structures of wind farms based on an example of offshore wind farm detection in the East China Sea. The combination of the two can effectively achieve complementary advantages and obtain more detailed and accurate water depth data and underwater structure images. It provides reliable basic data support for the operation and maintenance of offshore wind farms and the establishment of life cycle management system.

Mitigating effects of temperature variations through probabilistic-based machine learning for vibration-based bridge scour detection

This paper presents a novel approach to mitigating the effect of temperature variations on the bridges’ dynamic modal properties for more reliably detecting scour damage around bridge piles based on the vibration-based measurements. The novelty of the presented approach lies in its ability to reasonably remove the impacts on the modal properties of bridges, particularly caused by changes in material properties and structural boundary conditions due to temperature variations without explicitly modeling these complex effects. The main idea is to adopt the probabilistic-based machine learning method, Gaussian Process Model, to learn the correlation between the changes of modal properties of a monitored bridge and the corresponding temperature variations from in situ sensor measurements, and probabilistically infer the bridge scour based on the modified vibration measurements, which have mitigated the identified impacts of temperature variations, by applying Bayesian inference through the Transitional Markov Chain Monte Carlo simulation. The proposed approach and its applicability are presented and validated through the numerical simulation of a prototype bridge, demonstrating its potential for practical application for mitigating effects of temperature variations or other environmental impacts for vibration-based Structural Health Monitoring. The limitation of the presented study and future research needs are also discussed.

Scour Damage Detection and Structural Health Monitoring of a Laboratory-Scaled Bridge Using a Vibration Energy Harvesting Device.

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.

Influence of soil characteristics on natural frequency-based bridge scour detection

The concept of detecting scour severity by analyzing the change in the Predominant Natural Frequency (PNF) of a bridge pier has been gaining increasing interest in recent years. Previous studies primarily focus on this topic using less cohesive soils such as highly erodible sands, whereas no discussions have been reported on the influence of soil characteristics, especially those of cohesive soils, on the PNF. This missing knowledge gap is critical for this application as cohesive soils are an essential part of the soil-pier interaction to determine PNFs. This study aims to fill this knowledge gap by investigating three issues that are related to soil characteristics: 1) the effect of soil types on the PNF variation; 2) the questionable issue regarding the pier diameter effect for soil-pier dynamic modeling using the Vesic analytical expression; and 3) contradictory statements in the existing studies regarding the influence of the soil’s elastic modulus on the PNF variation. For the purpose, a series of lab-scale tests is first conducted, and a Winkler-based numerical model is then developed and validated against the experimental results to investigate the effect of soil characteristics on the PNFs measured from systems with cohesive soils and those with less cohesive soils. We found that the soil characteristics affect the PNF by providing a different lateral stiffness to the soil-pier interaction. The strength of the soil-pier interaction mainly depends on the lateral stiffness of each type of soils. In-depth discussions are also made to clarify the pier diameter effect on the predicted PNFs from both less cohesive and cohesive soils. It was clarified that the distribution of the soil’s elastic modulus determines whether the pier diameter effect needs to be considered using the Vesic analytical expression. Further simulations are finally conducted with more complex and realistic field soil conditions to mediate the contradictory statements regarding the influence of the soil’s elastic modulus. The simulation results indicated that the soil’s elastic modulus significantly influences the PNFs. The PNF variations differ under different elastic moduli of soils and distributions of elastic moduli with soil depths.

Critical insights for advanced bridge scour detection using the natural frequency

Scour around bridge foundations is regarded as one of the predominant causes of bridge failures. The concept of vibration-based real-time bridge scour detection has been explored in recent years by investigating the change in the natural frequency spectrum of a bridge or a bridge component with respect to the scour depth. Despite the progress, significant issues still remain unsolved in the application of this concept. This paper investigates three unsolved issues in the current framework of scour detection using the natural frequency spectrum: the physical meaning of the measured predominant natural frequency, the location of sensor installation, and the influence of the shape of scour holes, which are easily neglected but critical to the further implementations of the natural frequency spectrum-based bridge scour detection. Firstly, in-depth discussions of these three major issues were made separately by numerically modeling the scour progression of a typical and documented laboratory test. Laboratory tests were then performed to validate the conclusions made in the discussions. It was found that for an eigenproblem of the system with soil-structure interaction, the physical meaning of the natural frequency obtained from modal analysis can be understood by comparing the modal natural frequency with the natural frequency calculated from the dynamic response of the test component in that system. The results also verified that the obtained predominant natural frequency of the pier body greatly varies with the location of the pier body where a sensor is mounted for signal pickup. The shape of the scour hole affects the predominant natural frequency of the pier, causing difficulties in practical measurements. To address such a problem, a new criterion was proposed to identify the depth of unsymmetrical scour holes for the first time, which is of practical significance to advance the natural frequency spectrum-based scour detection framework.

Vibration-based bridge scour detection: A review

Scour around bridge foundations is regarded as one of the predominant causes of bridge failures. Traditional methods primarily employ underwater instruments to detect bridge scour depths, which thus have difficulties in instrument installations and operations. The concept of scour detection derived from vibration-based damage detection has been explored in recent years to address such difficulties by investigating the natural frequency spectrum of a bridge or a bridge component. This paper presents a comprehensive review of existing studies on scour detection using the natural frequency spectrum of a bridge or a bridge component. Underlying mechanisms, laboratory and field tests, numerical studies, and data processing schemes are reviewed to summarize the state of the art, which are absent but urgently needed. Updates on recently developed scour monitoring sensors are also provided to complement the introduction. Based on the review, in-depth discussions in existing studies are made regarding a few controversial and unsolved issues to shed light on future research, highlighting issues such as the soil–structure interaction, locations of the sensor installation, and the influence of shapes of scour holes.

Design and evaluation of a high sensitivity spiral TDR scour sensor

Bridge scour accounts for more than half of the reported bridge failures in the United States. Scour monitoring technology based on time domain reflectometry (TDR) features the advantages of being automatic and inexpensive. The senior author’s team has developed a few generations of a TDR bridge scour monitoring system, which have succeeded in both laboratory and field evaluations. In this study, an innovative spiral TDR sensor is proposed to further improve the sensitivity of the TDR sensor in scour detection. The spiral TDR sensor is made of a parallel copper wire waveguide wrapped around a mounting rod. By using a spiral path for the waveguide, the TDR sensor achieves higher sensitivity than the traditional straight TDR probes due to longer travel distance of the electromagnetic (EM) wave per unit length in the spiral probe versus traditional probe. The performance of the new TDR spiral scour sensor is validated by calibration with liquids with known dielectric constant and wet soils. Laboratory simulated scour-refilling experiments are performed to evaluate the performance of the new spiral probe in detecting the sediment–water interface and therefore the scour-refill process. The tests results indicate that scour depth variation of less than 2 cm can be easily detected by this new spiral sensor. A theory is developed based on the dielectric mixing model to simplify the TDR signal analyses for scour depth detection. The sediment layer thickness (directly related to scour depth) varies linearly with the square root of the bulk dielectric constant of the water–sediment mixture measured by the spiral TDR probe, which matches the results of theoretical prediction. The estimated sediment layer thickness and therefore scour depth from the spiral TDR sensor agrees very well with that by direct physical measurement. The spiral TDR sensor is four times more sensitive than a traditional straight TDR probe.