Structural Monitoring System Research Articles

Analysis of Long-Term Durability Monitoring Data of High-Piled Wharf with Anode-Ladder Sensors Embedded in Concrete

The structural durability monitoring (SDM) system for a high-piled wharf in the Tianjin Port of China is devised and deployed with an anode ladder sensor, which provides a means to monitor the state of steel corrosion for the wharf. In this project, the anode-ladder sensors are installed on the reinforced concrete components of the front platforms. After the installation, the monitoring data from the anode-ladder sensor was collected for 40 months. Based on the monitoring data for 40 months, it is proven that the resistance of concrete will be significantly affected by the change in temperature, and the increase in temperature will cause the drop in concrete resistance. Furthermore, the data of the anode-ladder sensor set in the track beam of the front platform shows that the current in anode A1 and A2 relative to the cathode exceeded 15 μA during the four-month period, which is abnormal. The main reason is the influence of concrete temperature and humidity. Therefore, the monitoring data for current, voltage, resistance, and temperature should be combined to determine the activation state of the anode, and cannot be determined simply using the current value.

Actual quantification of probabilities for selected bridge failure scenarios

Considering risk analysis for civil structures, it is essential to determine the possible course of failure. There are numerous scenarios of different likelihood of occurrence. Several scenarios of the failure of bridge structures are proposed in the paper. Moreover a brief analysis of three failure scenarios for the stays of cable-stayed bridges is presented. The analysis applies to modern 21st century bridges built using state-of-the-art technologies. These are bridges with 60 ÷ 375 m long cable-stayed spans. As a result of the analysis, the probabilities of the occurrence of failure events were estimated. The considered scenarios should influence the layout of a structural monitoring system, if such system was proposed for the bridge.

Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means

The Structural Health Monitoring evaluates the situation of aeronautical, civil or mechanical structures and provides a forecast of its remaining useful life, acting in decision making, being able to intervene in critical situations. It has emerged as a viable economic alternative for monitoring structures and preventing failures. Thus, this system is defined as a prophylactic measure, reliable and effective against structural failure. This work exposes the theoretical basis and a new technique for detection of failures in pipes by acoustic means, following the International Standard ISO10534-1 (1996) in the sampling. This method of fault detection using acoustic means requires considerably less training data than is usually used in the literature, with approximately 85% less data. The results presented in this work showed how it is possible and effective to detect failure in pipes by acoustic means using an artificial immune system for structural monitoring, with a 100% precision in the detection of failure.

System design and demonstration of performance monitoring of a butterfly‐shaped arch footbridge

This paper presents an overview of the structural performance monitoring system for a butterfly-shaped arch footbridge located at The Hong Kong Polytechnic University. The system is the product of a multidisciplinary academic collaboration of the pooling expertise of four academic departments, namely, structural engineering, traffic engineering, surveying, and optical communication engineering. This system consists of 88 sensors of 13 types, including an advanced Brillouin optical time-domain analysis technology, an optical fiber Bragg grating technology, a global navigation satellite system (GNSS), and high-performance video cameras. The paper summarizes the modular design, subsystem framework, and advanced features of the monitoring system. Preliminary monitoring results, such as high spatial resolution distributed temperature/strain measurement, global heat-transfer analysis, pedestrian traffic analysis, dynamic characteristics, and GNSS measurements, are introduced. A fire accident is employed to demonstrate the function and value of the monitoring system and provided a rare opportunity to detect damages in a real structure. The monitoring system not only monitors the performance of the bridge in real-time but also provides a multifunctional platform, including an Internet-based software for research and teaching and a touchscreen visualization platform for public education.

Energy Efficient Scheduling Algorithm for Structural Health Building Monitoring System(Shbm) to Increase the Battery Lifetime

A structural health building monitoring system is invented, achieved and attempted using Wireless Sensor Networks .Ambient vibration of the structure is audited and is implemented to identify the health status of the structure. With WSN, low cost monitoring is possible without intruding with the action of the structure. In this cluster head is heavily loaded since it gathers information from all other member nodes and integrate then and forward to sink either directly or through intermediate other cluster head. I near future cluster head drain and we need to select another cluster head via re clustering. The re clustering also led to energy loss to select the cluster head based on weight age value.WSN consumes less power and it is used to track slow targets .WSNs covers only small distance. WSN has high failure rate and we have to plant excessive nodes. Hence redundant nodes dissipate more power. To avoid this, sleep mode and scheduling algorithm is used in this paper. The objective of the paper is to design a scheduling algorithm to trace data with target rate. The proposed scheme is a kind of an adaptive/Cyclic on–off scheduling scheme in where sensors nodes use only local information to make scheduling decisions.

A Machine Learning-based Damage Prediction Techniques for Structural Health Monitoring

Nowadays, the Structural Building Health Damage Monitoring System (SBHDMS) is a crucial technology for predicting the civil building structures' health. SBHDMS contains abnormal changes in the buildings in terms of damage levels. Natural Disasters like Earthquakes, Floods, and cyclones affect the unusual changes in the buildings. If the building undergoes any natural disaster, the sensors capture the vibration data or change the buildings' structure. Due to the vibration data, these unusual changes can be analyzed. Here sensors or Machine Learning based Building Damage Prediction (MLBDP) are used for capturing and collecting the vibration data. This paper proposes a Novel Rough Set based Artificial Neural Network with Support Vector Machine (RAS) metaheuristic method. RAS method is used to predict the damaged building's vibration data levels captured by the sensors. For the feature reduction subset, we use one of the essential pre-processing method called the Rough set theory (RST) strategy. RAS has two contributions. The first one is the Support Vector Machine (SVM) classification method used for identifying the structures of the buildings. The artificial Neural Network (ANN) method used to predict the buildings' damage levels is the second contribution. The proposed method (RAS) is accurately predicting the conditions of the construction building structure and predicting the damage levels, without human intervention. Comparing the results states that the proposed method accuracy is better than SVM's classification methods, ANN. The prediction analysis depicts that the RAS method can effectively detect the damage levels.

Investigation of Structural Stress Monitoring System on Excavator under Impact Loads

Displacement and pressure sensors were used to measure the displacement and pressure variations of the hydraulic cylinders in an excavator under impact loads. Taking the measured cylinder displacement as a driven boundary condition, the kinematics simulation is completed in the LMS Virtual Lab. Taking the measured cylinder thrust and the simulation results of the kinematics as driven boundary conditions, the dynamic simulation of the excavator is carried out. The CAE dynamic stress analysis of the boom and arm of the excavator is performed using ABAQUS inertial release approach under the loads on the sub-structure obtained from the above multi-body dynamic simulation and the measured data, it provides a reference for the material selection of each part of excavator boom. The good agreement between the analytical and testing results demonstrates that the present method is accurate, efficient, cost effective, and able to monitor the stress status and access the health of an excavator instead of using field testing techniques.

Damage detection in railway bridges using traffic-induced dynamic responses

This paper aims at detecting damage in railway bridges based on traffic-induced dynamic responses. To achieve this goal, an unsupervised automatic data-driven methodology is proposed, consisting of a combination of time series analysis methods and multivariate statistical techniques. Damage-sensitive features of train-induced responses are extracted and allow taking advantage, not only of the repeatability of the loading, but also, and more importantly, of its great magnitude, thus enhancing the sensitivity to small-magnitude structural changes.The efficiency of the proposed methodology is validated in a long-span steel-concrete composite bowstring-arch railway bridge with a permanent structural monitoring system installed. An experimentally validated finite element model was used, along with experimental values of temperature, noise, and train loadings and speeds, to realistically simulate baseline and damage scenarios.The proposed methodology proved to be highly sensitive in detecting early damage, even when it consists of small stiffness reductions that do not impair the safety or use of the structure, and highly robust to false detections. The analysis and validation allowed concluding that the ability to identify early damage, imperceptible in the original signals, while avoiding observable changes induced by variations in train speed or temperature, was achieved by carefully defining the modelling and fusion sequence of the information. A single-value damage indicator, proposed as a tool for real-time structural assessment of bridges without interfering with the normal service condition, proved capable of characterizing multi-sensor data while being sensitive to identify local changes.

Using artificial neural networks for GNSS observations analysis and displacement prediction of suspension highway bridge

Bridges are playing a major role in the socio-economic development of any country over the world. Suspension highway bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance, and deformation prediction for these structures is important and vital. One of the main objectives of monitoring the structural deformation is predicting the deformation values, which will help to avoid sudden failure and accidents in the future. Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have proven successful solution in many engineering applications and problems. This paper investigates an integrated monitoring system using GNSS observations for studying the deformation behavior and displacement prediction for suspension highway bridge, taking into consideration the effect of wind, temperature, humidity and traffic loads during the operational and short-term measurements. Due to the complexity of the mathematical processing of large GNSS monitoring data for obtaining reliable results, adequate model of several alternatives should be chosen. One of the main objectives of this paper is to investigate the optimum predictive model for analysis of GNSS observations and displacement prediction. Several models are applied and compared for prediction of suspension bridge displacement for both kinematic and dynamic models. The resulting predicted displacement values by applying artificial neural networks (ANNs) and ANFIS provide a significant improvement for predicting the structure deformation values for suspension highway bridges from GNSS observations.

An Investigation on the Performance of Soft Computing Techniques for Point Displacement Modeling for Suspension Bridge Using GNSS Technique

Bridges are playing a major role in the socio-economic development of any country over the world. Suspension bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance and deformation prediction for these types of structures is important and vital. Time of observations for the purpose of structural deformation can vary from a few hours, days to several months or even years. This paper investigates the performance of several soft computing techniques for point displacement modeling using GNSS technique during the process of monitoring the structural deformation of suspension highway bridge, taking into consideration the effect of wind, temperature, humidity and traffic loads during the operational and short-term measurements. Due to the availability of a large amount of positions data generated from GNSS data positions for monitoring the deformation of such structure, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) should be chosen. One of the main objectives of this paper is to investigate the optimum predictive soft computing model for processing GNSS positions and points displacement prediction. Several mathematical models and two cases of data amount (66.67% and 50% of all available data) for dynamic and kinematic state are applied and compared for prediction of suspension bridge displacement with confidence interval with a probability ρ = 0.95, Δ = ± 2σ. The resulting point displacement values by applying ANNs and ANFIS, which used a confidence interval with a probability of ρ = 0.95, Δ = ± 2σ when using 66.67% of all data, are more accurate and reliable than any other applied methods, and therefore, ANNs and ANFIS can provide a significant improvement of understanding and predicting the structure deformation values where conventional mathematical modeling techniques were not as accurate or capable especially in dynamic prediction of displacements.

Advanced deep learning model-based impact characterization method for composite laminates

The aim of this study was to develop a structural health monitoring system for smart composite structures through the use of signal processing, deep learning algorithms, and optimization theory. Piezoelectric ribbon sensors were implemented in the preparation of smart composite structures to create a smart composite fabric that can be embedded in composite laminates to enable self-monitoring. A discrete wavelet transform was applied to the impact signals to convert them into input image data for the predictive convolutional neural network-based models. Optimal values of the hyperparameters were derived based on Bayesian optimization theory. Data augmentation was also employed to secure sufficient data for impact characterization model training. Lastly, the performance of each optimized neural network model was investigated by comparing the test errors under each applied condition.

Cost‐Effective and Ultraportable Smartphone‐Based Vision System for Structural Deflection Monitoring

This work demonstrates the viability of using a smartphone‐based vision system to monitor the deflection of engineering structures subjected to external loadings. The video images of a test structure recorded by a smartphone camera are processed using the state‐of‐the‐art subset‐based digital image correlation (DIC) algorithm to extract the vertical image displacement of discrete calculation points defined on the test object. The measured vertical image displacement can be converted to deflection (vertical displacement) by easy‐to‐implement scaling factor determination approaches. For accuracy validation, laboratory experiments using a cantilever beam subjected to external loadings were performed. The deflection and inherent frequency of the test cantilever beam measured by the proposed smartphone‐based vision system were compared with those measured by conventional dial gauges and a dynamic strain gauge. The relative errors were estimated as 1% and 0.15% for deflection and inherent frequency, respectively. Outdoor real bridge deflection monitoring tests were also carried out on an overpass with subway passing by, and the measured deflection‐time curves agree well with actual situations. The cost‐effective, ultraportable, and easy‐to‐use smartphone‐based vision system not only greatly decreases the hardware investment and complexity in deflection measurement system construction, but also increases the convenience and efficiency of deflection monitoring of engineering structures.

Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers

The damping estimation of an eight-story steel building equipped with oil dampers is examined, carried out by adopting a proposed framework, which consists of an enhanced strain-energy method and an improved direct method for model updating. The building is located at Tohoku Institute of Technology and is equipped with a structural monitoring system that measures its seismic response, including floor acceleration and displacement and force of oil dampers. The enhanced strain-energy method is first developed and employed to assess the supplemental damping and stiffness provided by oil dampers, herein quantified in the form of equivalent damping ratios and natural frequencies. Then, modal characteristics extracted from the earthquake measurements are modified accordingly and utilized for the building model updating, in which mass and stiffness matrices are corrected by the improved direct method. The updated model accurately reproduces the target modal data, especially measured mode participation factors, and is further used for the building response predictions. Through prediction validations, the precision of the modified modal parameters is verified. Finally, a large earthquake event is chosen to demonstrate the effectiveness of the proposed framework for the damping estimation of the investigated building.

Application of the Virtual Reality Technology in Structural Safety Monitoring of Shield Tunnels

Many structural health monitoring (SHM) systems of actual tunnels are underused, with abstract data, chaotic information and the diagnostic results beyond comprehension. It is necessary to establish the visual connection between the visualization of monitoring network and massive structural information of tunnels. To address this issue, this study proposed a structural visualization method based on the panoramic virtual reality technology, combining the panoramic spherical image and the real-time monitoring data of tunnel obtained from SHM system. First, a software system for structural safety monitoring of an actual shield tunnel was developed on the basis of the combination of the panoramic visual technology and B/S (Browser/Server) architecture system. Second, the panoramic modelling method for specific tunnel structure environment was discussed in detail, and then the mechanism of data transmission and information access in panoramic virtual reality environment was investigated deeply. Finally, in view of the demands of practical application, the function modules of the abovementioned software system were described. Some key issues of how to develop each software module using the panoramic virtual reality technique were analysed e.g., online geographic information and cloud platform management. The results show that the panoramic virtual reality technology completely meets the needs of real-time processing, information management, virtual roaming and maintenance management in tunnel structural safety monitoring.

Machine learning paradigm for structural health monitoring

Structural health diagnosis and prognosis is the goal of structural health monitoring. Vibration-based structural health monitoring methodology has been extensively investigated. However, the conventional vibration–based methods find it difficult to detect damages of actual structures because of a high incompleteness in the monitoring information (the number of sensors is much fewer with respect to the number of degrees of freedom of a structure), intense uncertainties in the structural conditions and monitoring systems, and coupled effects of damage and environmental actions on modal parameters. It is a truth that the performance and conditions of a structure must be embedded in the monitoring data (vehicles, wind, etc.; acceleration, displacement, cable force, strain, images, videos, etc.). Therefore, there is a need to develop completely novel structural health diagnosis and prognosis methodology based on the various monitoring data. Machine learning provides the advanced mathematical frameworks and algorithms that can help discover and model the performance and conditions of a structure through deep mining of monitoring data. Thus, machine learning takes an opportunity to establish novel machine learning paradigm for structural health diagnosis and prognosis theory termed the machine learning paradigm for structural health monitoring. This article sheds light on principles for machine learning paradigm for structural health monitoring with some examples and reviews the existing challenges and open questions in this field.

Assessment of the Piezoelectric Response of an Epoxy Resin/SbSINanowires Composite Filling FDM Printed Grid.

This paper shows a piezoelectric response from an innovative sensor obtained by casting epoxy-SbSI (antimony sulfoiodide) nanowires nanocomposite to a grid structure printed using a fuse deposition modeling (FDM) method. The grid is shown to be a support structure for the nanocomposite. The applied design approach prospectively enables the formation of sensors with a wide spectrum of shapes and a wide applicability. The voltage signal obtained as a result of the piezoelectric effect reached 1.5V and 0.5V under a maximum static stress of 8.5 MPa and under a maximum dynamic stress of 22.3 kPa, respectively. These values are sufficient for potential application in sensor systems. The effect of a systematic increase in the voltage signal with subsequent cycles was also observed, which similarly allows the use of these sensors in monitoring systems for structures exposed to unfavorable cyclical loads. The obtained results also show that the piezoelectric signal improves with increase in strain rate.

Development of a Vibration Measurement System for Bridges

Traditionally, the bridge survey has been carried out based on visual inspections, which usually do not detect hidden structural failures. This work describes the development of a structural monitoring system to measure the bridge's dynamical behavior, to support structural verification and retro-analysis procedures. Also, the motivation for this research is the absence of cost-effective methods in Brazil to perform this structural evaluation. To define the hardware requirements, a bibliographic review about modal analysis and structural identification is carried out. Beyond the hardware project of the electronic device has considered variables associated with the operational modal analysis, and observing the steps of construction of the sensors, microcontroller, and computational communication, as well as the budget restrictions. The laboratory calibration tests were performed in order to grant the device reliability. The performance evaluation has indicated that the device is able to acquire 303 samples per second, with 16 bits precision from the three-axis, as well as to support a +-8G range. The sensors calibration was accomplished using a shaker device and calibrated PCB accelerometer attached to a high-performance data acquisition system. The device validation was performed in a bridge located in Passo Fundo, southern Brazil, which has been detailed studied in the literature

Moving sensors for improved estimation of dynamic structures: Experimental validation

In the monitoring of structural systems, the use of multiple high-end sensors may prove to be economically prohibitive. The alternative approach would be to use fewer devices capable of moving across the span of the structural system. In the proposed approach, a velocity sensor that is able to move across the spatial domain and obtain point-wise velocity measurements is combined to a novel dynamic observer. Based on the measured velocities, a state estimator is developed, the gain of which depends on the motion of the sensor. The motion of the sensor is defined using Lyapunov redesign methods and depends only on the estimation error at the current sensor position. The guidance policy is performance based and steers the sensor to spatial regions of the structure with larger estimation errors. The proposed approach is validated with a one-dimensional flexible structure, mathematically described by an Euler–Bernoulli partial differential equation. The moving sensor is realized through the use of a laser scanning vibrometer that provides both the moving measurements and additional measurements against which the proposed approach will be validated. Once measurements over a large number of locations are acquired, the experimental results are fed to the algorithm that selects the instantaneous sensor location. Experimental results for linear and nonlinear beam cases are presented to show the feasibility and robustness of the proposed approach.

Feasibility of machine learning algorithms for classifying damaged offshore jacket structures using SCADA data

The best practise for structural damage detection currently relies on the installation of structural health monitoring systems for the collection of dedicated high frequency measurements. Switching to the employment of the wind turbine’s SCADA (Supervisory Control and Data Acquisition) signals and their commonly recorded low frequency statistics can lead to a reduction in the number of ad-hoc monitoring sensors and quantity of data required. In this paper, aero-hydro-servo-elastic simulations for a model of a turbine are used to assess its loads and any changes in the dynamics under healthy state and a damaged configuration case study. To prove the feasibility of the damage detection through low-resolution data, the statistics of the typically recorded signals from the SCADA and the structural monitoring systems are fed into a database for training and testing of classification algorithms. The ability of the machine learning models to generalise the classification for both stochasticity and uncertainties in the environmental conditions are tested. Decision tree-based classifiers showed the capability to capture the damage for the majority of the operating conditions considered. Though the setup of the traditional SCADA sensors had to be supplemented with an additional structural health monitoring sensor, the detection of the damage has been shown feasible by referring to low-frequency statistics only.

Research on wireless sensor network system for structural damage monitoring

In recent years, the application of wireless sensor networks in the field of structural monitoring has been widely concerned by experts and scholars in various countries. The throughput and real-time performance of the wireless sensor network should be considered when the structure health monitoring wireless sensor network is deployed. According to the characteristics and requirements of wireless structure damage monitoring, this paper analyzes the system from three aspects: monitoring system process, node design and damage diagnosis algorithm.