Sensor-based Structural Health Monitoring Research Articles

Remote Sensing-Based Structural Health Monitoring and Damage Assessment

With the rapid development of society and the economy, significant infrastructure, such as roads, buildings, high-speed railways, and bridges, have been built all over the world [...]

Sensor-Based Structural Health Monitoring of Asphalt Pavements with Semi-Rigid Bases Combining Accelerated Pavement Testing and a Falling Weight Deflectometer Test

The Structural Health Monitoring (SHM) of pavement infrastructures holds paramount significance in the assessment and prognostication of the remaining service life of roadways. In response to this imperative, a methodology for surveilling the surface and internal mechanical responses of pavements was devised through the amalgamation of Accelerated Pavement Testing (APT) and Falling Weight Deflectometer (FWD) examinations. An experimental road segment, characterized by a conventional asphalt pavement structure with semi-rigid bases, was meticulously established in Jiangsu, China. Considering nine distinct influencing factors, including loading speed, loading weight, and temperature, innovative buried and layout configurations for Resistive Sensors and Fiber-optic Bragg Grating (FBG) sensors were devised. These configurations facilitated the comprehensive assessment of stress and strain within the road structure across diverse APT conditions. The methodology encompassed the formulation of response baselines, the conversion of electrical signals to stress and strain signals, and the proposition of a signal processing approach involving partial filtering and noise reduction. In experimental findings, the asphalt bottom layer was observed to undergo alternate tensile strains under dynamic loads (the peak strain was ten με). Simultaneously, the horizontal transverse sensor exhibited compressive strains peaking at 66.5 με. The horizontal longitudinal strain within the base and subbase ranged between 3 and 5 με, with the base registering a higher strain value than the subbase. When subjected to FWD, the sensor indicated a diminishing peak pulse signal, with the most pronounced peak response occurring when the load plate was situated atop the sensor. In summary, a comprehensive suite of monitoring schemes for road structures has been formulated, delineating guidelines for the deployment of road sensors and facilitating sustained performance observation over extended durations.

Piezoelectric ceramic sensor-based structural health monitoring of diagonally braced H-shaped steel structures

In this study, we propose a piezoelectric ceramic sensor-based health monitoring method to monitor the stress state of diagonally braced H-shaped steel structures. Loading experiments were carried out on diagonally braced H-shaped steel under different working conditions. Subsequently, the amplitude and energy of piezoelectric signals under these two working conditions were compared and analyzed, and finite element analysis was performed using ABAQUS software to verify the results. The experimental results showed that with an increase in the web height or load, the time-domain waveform energy index of the H-shaped steel increased. Under different working conditions, such as a diagonally braced H-shaped steel member with a web height of 10 cm, when the pressure value was less than 10 N/mm2, the energy index increased by approximately 15.98% for every 1 N/mm2 increase in the pressure. When the pressure value was greater than 10 N/mm2 and less than 15 N/mm2, the energy index increased by approximately 1% for every 1 N/mm2 increase in pressure. Further, the energy index increased by approximately 8.4% for every 1 cm increase in the height of the web. Simultaneously, it can be seen from the results of the finite element analysis that the stress and strain at the induction position of the piezoelectric ceramic sensor increased with an increase in the external pressure. The study of structural health monitoring for diagonally braced H-shaped steel structures holds significant importance in ensuring the safety and reliability of these structures, achieving predictive maintenance, evaluating structural performance and energy efficiency, and optimizing structural maintenance.

Data anomaly detection with automatic feature selection and deep learning

As increasing sensor-based structural health monitoring (SHM) systems are implemented on civil infrastructures, sensor data reliability plays a crucial role in the assessment of operational performance of bridges. Sensors are heavily exposed to harsh environmental conditions during their operations and inevitably lead to possible unstable performance or failure. Thus, to accurately identify faulty sensors is a prerequisite to processing and analyzing the collected data for assessment purpose. Recently, researchers adopted the convolutional neural network (CNN) approach to identify faulty sensors, focusing on image features. Such approach may overlook some important detailed signal features and the time series approach may still be needed. However, algorithms based on time series tend to be time consuming because of the lengthy and high dimensional dataset. This may be effectively resolved using an automatic feature selection technique, namely Tsfresh, as proposed in this paper to select highly relevant signal features based on statistical tests of significance. A deep learning technique based on fully convolutional network (FCN) can then be efficiently employed for anomaly classification. The algorithm is validated using a dataset collected from a real cable-stayed bridge and results show that the proposed method significantly reduces the training time for the neural network, albeit with high classification accuracy.

Improving Seaport Wharf Maintenance and Safety with Structural Health Monitoring System in High Salt and Humidity Environments

Due to the harsh working conditions, the durability of the seaport wharf structure is poor compared with similar hydraulic structures. According to the structural characteristics of coastal port wharf and the particularity of a high salt and high humidity environment, the stress features of the coastal wharf structure are analyzed, and the health inspection indicators of the wharf structure are proposed. A fiber grating sensor-based structural health monitoring system for coastal high-pile piers is established. A corresponding system for detecting structural health is designed according to the standard structural section of the wharf’s front platform. The corresponding monitoring implementation scheme, sensor selection, and performance parameters are proposed. Finally, the realization technology and related indicators of data acquisition and transmission subsystem are given. The experimental results indicate that the waveform of the structural response to the wave load has a consistent sine wave pattern with the actual wave load. The maximum strain of the berthing pier appears at 4.35 m and 6.14 m, and the elevations reach 4.66 με and 5.31 με, respectively. The strain at other positions also has an obvious change trend. The experimental results provide some help for the research of the wharf health monitoring system.

A missing sensor measurement data reconstruction framework powered by multi-task Gaussian process regression for dam structural health monitoring systems

The sensor-based structural health monitoring (SHM) systems are widely embedded in the new-constructed and rehabilitated dam. Due to the harsh working environment, poor installation, and sampling error, sensor fault often inevitably occurs. In this paper, rather than using conventional Gaussian process regression(GPR) to reconstruct missing data from multiple sensors independently, we propose a multi-task GPR (mGPR) paradigm for capturing the correlation among various sensors to reconstruct missing data from faulty sensors as a whole. In this framework, for a particular sensor, the missing data is reconstructed by the approach which not only learns other known data from this sensor but also learns the whole known measurements from other sensors. The proposed paradigm is quite beneficial for dam SHM systems since the missing data from the faulty sensor(s) can be efficiently and accurately learned by the whole historical data including both faulty and normal sensors. The usefulness of the proposed paradigm is demonstrated through three measurement items including air temperature, dam displacements, and crack opening displacements collected from two dams in long-term service. We investigate two missing data scenarios with distinct positions in sensors. The experimental results show our proposed mGPR has significantly better performance than conventional multiple GPR for all the tested measurement items, especially in the scenarios that the missing part occurs at the beginning or the end of the dataset. It is also shown the multi-task learning paradigm powered by mGPR is considerable to address missing data reconstruction for dam SHM systems.

Structural Health Monitoring System Based on FBG Sensing Technique for Chinese Ancient Timber Buildings.

Due to the long-term service, Chinese ancient timber buildings show varying degrees of wear. Thus, structural health monitoring (SHM) for these cultural and historical treasures is desperately needed to evaluate the service status. Although there are some FBG sensing-based SHM systems, they are not suitable for Chinese ancient timber buildings due to the differences in architectural types, structural loads, materials, and environment. Besides, a technical gap in Fiber Bragg grating (FBG) sensing-based column inclination monitoring exists. To overcome these weaknesses, this paper develops an FBG sensing-based structural health monitoring system for Chinese ancient Chuan-dou-type timber buildings that aims at monitoring structural deformation, i.e., beam deflection and column inclination, temperature, humidity, and fire around the building. An in-situ test and simulation analyses were conducted to verify the effectiveness of the developed SHM system. To validate the long-term-operation of the developed SHM system, monitoring data within 15 months were analyzed. The results show good agreement between the developed SHM system in this paper and other methods. In addition, the SHM system operated well in the first year after its deployment. This implies that the developed SHM system is applicable and effective in the health state monitoring of Chinese ancient Chuan-dou-type timber buildings, laying a foundation for damage prognosis of such types of timber buildings.

On the topology design of large wireless sensor networks for distributed consensus with low power consumption

Sensor-based structural health monitoring systems are commonly used to provide real-time information and detect damage in complex structures. In particular, wireless structural health monitoring systems are of low cost but, since wireless sensors are powered with batteries, a low power consumption is critical. A common approach for wireless structural health monitoring is to use a distributed computation strategy, which is usually based on consensus algorithms. Power consumption in such wireless consensus networks depends on the number of connections of the network. If sensors are randomly connected, there is no control on the power consumption. In this article, we present a novel strategy to connect a large number of wireless sensors for distributed consensus with low power consumption by combining small networks with basic topologies using the Kronecker product.

A smart pipe energy harvester excited by fluid flow and base excitation

This paper presents an electromechanical dynamic modelling of the partially smart pipe structure subject to the vibration responses from fluid flow and input base excitation for generating the electrical energy. We believe that this work shows the first attempt to formulate a unified analytical approach of flow-induced vibrational smart pipe energy harvester in application to the smart sensor-based structural health monitoring systems including those to detect flutter instability. The arbitrary topology of the thin electrode segments located at the surface of the circumference region of the smart pipe has been used so that the electric charge cancellation can be avoided. The analytical techniques of the smart pipe conveying fluid with discontinuous piezoelectric segments and proof mass offset, connected with the standard AC–DC circuit interface, have been developed using the extended charge-type Hamiltonian mechanics. The coupled field equations reduced from the Ritz method-based weak form analytical approach have been further developed to formulate the orthonormalised dynamic equations. The reduced equations show combinations of the mechanical system of the elastic pipe and fluid flow, electromechanical system of the piezoelectric component, and electrical system of the circuit interface. The electromechanical multi-mode frequency and time signal waveform response equations have also been formulated to demonstrate the power harvesting behaviours. Initially, the optimal power output due to optimal load resistance without the fluid effect is discussed to compare with previous studies. For potential application, further parametric analytical studies of varying partially piezoelectric pipe segments have been explored to analyse the dynamic stability/instability of the smart pipe energy harvester due to the effect of fluid and input base excitation. Further proof between case studies also includes the effect of variable flow velocity for optimal power output, 3-D frequency response, the dynamic evolution of the smart pipe system based on the absolute velocity-time waveform signals, and DC power output-time waveform signals.

Developments in damage assessment by Marie Skłodowska-Curie TRUSS ITN project

The growth of cities, the impacts of climate change and the massive cost of providing new infrastructure provide the impetus for TRUSS (Training in Reducing Uncertainty in Structural Safety), a €3.7 million Marie Skłodowska-Curie Action Innovative Training Network project funded by EU’s Horizon 2020 programme, which aims to maximize the potential of infrastructure that already exists (http://trussitn.eu). For that purpose, TRUSS brings together an international, inter-sectoral and multidisciplinary collaboration between five academic and eleven industry institutions from five European countries. The project covers rail and road infrastructure, buildings and energy and marine infrastructure. This paper reports progress in fields such as advanced sensor-based structural health monitoring solutions – unmanned aerial vehicles, optical backscatter reflectometry, monitoring sensors mounted on vehicles, … – and innovative algorithms for structural designs and short- and long-term assessments of buildings, bridges, pavements, ships, ship unloaders, nuclear components and wind turbine towers that will support infrastructure operators and owners in managing their assets.

Sensor-Free Stress Estimation Model for Steel Beam Structures Using a Motion Capture System

As a new paradigm for safety assessment of beam structures, this paper proposes a sensor-free monitoring model to estimate the stress distribution of steel beam structures with uncertain loads. The model consists of three steps: 1) the use of a sensor- and wire-free measurement step using a motion capture system; 2) a deformed shape estimation step employing cubic smoothing spline interpolation; and 3) a stress distribution and maximum stress estimation step using the radius of curvature of the estimated deformed shape or a finite-element method. Therefore, unlike the conventional sensor-based structural health monitoring with lengthy cables, the model can determine the maximum stress in a non-contact manner without the deterioration of the measurement accuracy. The sensor-free stress estimation method was applied to estimate the stress distribution of a steel beam structure subjected to static loads applied at the mid-span of the beam by a hydraulic jack. The validity of the proposed method was confirmed by comparing the estimated stresses with stresses measured in static loading tests.

Smart wireless sensing and assessment for civil infrastructure

Recently, there has been increasing need for adopting smart sensing technologies to structural health monitoring (SHM) applications for civil infrastructure. In this paper, the state of the art in smart wireless sensing and assessment techniques for civil structures are reviewed focusing on full-scale applications. Three types of smart wireless sensing technologies are discussed: wireless acceleration sensor-based SHM, wireless impedance-based SHM and an optics-based non-contact actuation and sensing technique. At first, vibration-based SHM using a dense array of wireless acceleration sensors is implemented to a cable-stayed bridge. The modal identification of the bridge and cable tension estimation are carried out using the ambient acceleration data. Measured data during a typhoon is also discussed. Secondly, impedance-based SHM using piezoelectric active sensors is presented focusing on hardware and software issues. A wireless impedance sensor node is presented for local SHM and neural network-based smart assessment algorithm is proposed to detect multi-type damages. Finally, a wireless power and data transmission method using laser and optoelectronic technologies is presented for non-contact measurement of guided waves and impedance, and subsequent damage detection. This method is embodied in a small printed circuit board, and the performance is validated on a lab-scale steel truss member.

구조물 건전성 모니터링을 위한 증강현실 기반 센서 위치인식 및 데이터시각화 기술

최근 초고층 및 초장대와 같은 대형 구조물들이 시공되고 있으며, 이에 대한 구조물 건전성 모니터링 기술들이 연구되고 있다. 하지만 기존의 기술들은 계측 센서의 관리와 센서로부터 계측된 데이터에 효율적으로 access하지 못하고 있다. 본 논문에서는 구조물 건전성 모니터링을 위한 증강현실 기반 센서 위치인식 및 데이터 시각화 기술을 소개한다. 모바일 디바이스에 내장된 GPS를 통하여 센서와 사용자 간의 거리를 파악하게 된다. 뿐만 아니라, 센서로부터 계측된 데이터는 위치정보시스템 서버에 저장되며, RSS방식을 통해 전송되어 사용자가 모바일 디바이스를 통해 쉽게 계측 데이터를 가시화 할 수 있게 된다. 이 기술을 이용하여 사용자는 센서의 위치인식을 통해 계측센서를 관리하고, 계측 데이터를 시각화하여 시간과 공간에 제약 없이 구조물의 건전성을 모니터링 할 수 있게 된다. In recent years, numerous mega-size and complex civil infrastructures have been constructed worldwide. For the more precise construction and maintenance process management of these civil infrastructures, the application of a variety of smart sensor-based structural health monitoring (SHM) systems is required. The efficient management of both sensors and collected databases is also very important. Recently, several kinds of database access technologies using Quick Response (QR) code and Augmented Reality (AR) applications have been developed. These technologies provide software tools incorporated with mobile devices, such as smart phone, tablet PC and smart pad systems, so that databases can be accessed very quickly and easily. In this paper, an AR-based structural health monitoring technique is suggested for sensor management and the efficient access of databases collected from sensor networks that are distributed at target structures. The global positioning system (GPS) in mobile devices simultaneously recognizes the user location and sensor location, and calculates the distance between the two locations. In addition, the processed health monitoring results are sent from a main server to the user's mobile device, via the RSS (really simple syndication) feed format. It can be confirmed that the AR-based structural health monitoring technique is very useful for the real-time construction process management of numerous mega-size and complex civil infrastructures.

Application of structural health monitoring in civil infrastructure

The emerging sensor-based structural health monitoring (SHM) technology has a potential for cost-effective maintenance of aging civil infrastructure systems. The author proposes to integrate continuous and global monitoring using on-structure sensors with targeted local non-destructive evaluation (NDE). Significant technical challenges arise, however, from the lack of cost-effective sensors for monitoring spatially large structures, as well as reliable methods for interpreting sensor data into structural health conditions. This paper reviews recent efforts and advances made in addressing these challenges, with example sensor hardware and health monitoring software developed in the author's research center. The hardware includes a novel fiber optic accelerometer, a vision-based displacement sensor, a distributed strain sensor, and a microwave imaging NDE device. The health monitoring software includes a number of system identification methods such as the neural networks, extended Kalman filter, and nonlinear damping identificaiton based on structural dynamic response measurement. These methods have been experimentally validated through seismic shaking table tests of a realistic bridge model and tested in a number of instrumented bridges and buildings.

Optimal Placement of Piezoelectric Actuators and Sensors for Detecting Damage in Plate Structures

We propose a novel approach for optimal actuator and sensor placement for active sensing-based structural health monitoring. Of particular interest is the optimization of actuator—sensor arrays making use of ultrasonic wave propagation for detecting damage in thin plate-like structures. Using a detection theory framework, we establish the optimum configuration as the one that minimizes Bayes risk. The detector incorporates a statistical model of the active sensing process that accounts for both reflection and attenuation features, implements pulse-echo and pitch-catch actuation schemes, and takes into account line-of-site. The optimization space was searched using a genetic algorithm with a time-varying mutation rate. For verification, we densely instrumented a concave-shaped plate and applied artificial, reversible damage to a large number of randomly generated locations, acquiring active sensing data for each location. We then used the algorithm to predict optimal subsets of the dense array. The predicted optimal arrangements proved to be among the top performers when compared to large sets of randomly generated arrangements.

Sensor Self-diagnosis Using a Modified Impedance Model for Active Sensing-based Structural Health Monitoring

The active sensing methods using piezoelectric materials have been extensively investigated for the efficient use in structural health monitoring (SHM) applications. Relying on high frequency structural excitations, the methods showed the extreme sensitivity to minor defects in a structure. Recently, a sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric (PZT) active sensors and actuators in SHM applications has been proposed. In this investigation, previously developed impedance models were revisited in order to investigate the effects of sensor and/or bonding defects on the admittance measurement. New parameters for sensor quality assessment of a PZT and coupling degradation effects between a PZT and bonding layer were incorporated into the traditional electromechanical impedance model for better estimation of the electromechanical impedance signatures and sensor diagnostics. The feasibility of the modified impedance model for sensor self-diagnosis using the admittance measurements was demonstrated by a series of parametric studies using a simple example of PZT-driven single degree of freedom spring-mass-damper system. This paper summarizes the description of the proposed modified electromechanical impedance model, parametric studies for impedance-based sensor diagnostics, and several issues that can be used as a guideline for future investigation.

Damage detection based on damping analysis of ambient vibration data

Enabling an automated, remote and rapid detection of structural damage, sensor-based structural health monitoring is becoming a powerful tool for maintenance of civil engineering structures. In this study, a baseline-free, time-domain damage detection method was developed for concrete structures, which is based on analysis of nonlinear damping from measured structural vibration responses. The efficacy of the proposed method was demonstrated through a large-scale concrete bridge model subjected to different levels of seismic damage caused by shaking table tests. By applying the random decrement signature technique, the proposed method successfully identified, from its ambient vibration responses, nonlinear damping of the bridge associated with the seismic damage. The amount of the nonlinear damping increases as the seismic damage becomes more severe. This paper also compares the damage detection results with those obtained by stiffness-based methods, demonstrating a strong correlation between the increase in nonlinear damping and the decrease in structural stiffness associated with the increase in damage severity. Copyright © 2008 John Wiley & Sons, Ltd.

Video and Seismic Sensor-Based Structural Health Monitoring: Framework, Algorithms, and Implementation

This paper presents the design and application of novel multisensory testbeds for collection, synchronization, archival, and analysis of multimodal data for health monitoring of transportation infrastructures. The framework for data capture from vision and seismic sensors is described, and the important issue of synchronization between these modalities is addressed. Computer-vision algorithms are used to detect and track vehicles and extract their properties. It is noted that the video and seismic sensors in the testbed supply complementary information about passing vehicles. Data fusion between features obtained from these modalities is used to perform vehicle classification. Experimental results of vehicle detection, tracking, and classification obtained with these testbeds are described