Real-time Structural Health Monitoring System Research Articles

Advancing structural health monitoring: A vibration-based IoT approach for remote real-time systems

In the modern world, civil engineering structures are of paramount importance. Early damage detection through continuous health monitoring, instead of conventional periodic inspection, is the promising way forward for healthy structures with the least maintenance requirement. The damage is identified at the initial stage, so the cost associated with its repair can be reduced. Considering the immense scale of civil infrastructure, currently, available structural health monitoring systems are not feasible for mass application as they are prohibitively expensive. Hence, it is the need of the hour to develop a remote real-time Structural Health Monitoring System (SHMS). In the current study, a system is proposed that utilizes a vibration-based structural health monitoring approach and extracts dynamic parameters from acceleration data. The system consists of a node tasked with data collection and transmission and a server assigned with data post-processing and results display. The node comprises the following main hardware components: ESP32 as a microcontroller unit and ADXL345 as an accelerometer. The system was developed with the capability of multiple node integration, onboard data storage to eliminate sampling rate inconsistencies, dual trigger modes to achieve a high signal-to-noise ratio (SNR), power usage optimization to prolong stand-by time, and wireless data transmission along with automated post-processing. To minimize the cost of the system, Internet of Things (IoT) technology was adopted to develop the system.

Inter-Story Drift Ratio Detection of High-Rise Buildings Based on Ambient Noise Recordings

The inter-story drift ratio (IDR) is a crucial parameter in structural health monitoring to judge the safety, stability, and serviceability of buildings. Real-time, continuous, and widely applicable detection on IDRs is essential. However, the current methods present some challenges in conducting such detection, including adverse effects from weather, the requirement for large amounts of space, and fragile instruments. This study proposes an alternative method to overcome these defects to measure IDRs and evaluate the structural conditions using ambient noise recordings. Ambient noise is a random and continuous wave signal with various sources and is modified by its propagating medium. Taking the Zhonghe Building on the campus of Tongji University, Shanghai, China, as an example, 24 three-component seismometers were deployed to capture and record the ambient noise continuously from 20 November 2021 to 9 December 2021. Using analysis of the polarization parameters of ambient noise during the building’s most dangerous time and ordinary time, a deflection curve, IDRs, and harmful IDRs of the Zhonghe Building during the most dangerous time were calculated. The computed maximum drift was 0.06 m, the maximum IDR was 1.140×10−3, and the maximum harmful IDR was −2.573×10−4. These results were compared with the relevant specifications in China, and it was found that the structure was in good condition. The study proposes an alternative method to measure IDRs with high applicability and continuity in real time and underscores the need for further research to achieve a localized and real-time structural health monitoring system.

Real-time structural health monitoring system based on streaming data

In this paper, a novel real-time structural health monitoring (SHM) system based on streaming data is proposed. In contrast to a traditional SHM system, the proposed system implements a series of optimizations for data transmission and processing to reduce the latency and better satisfy the real-time requirement. The concept of the watermark in the streaming system is adopted to address the problem of when to trigger the time window calculation under the real-time requirement. Moreover, a well-designed parallel mechanism is used to satisfy the multistage computation requirement in the parallel data stream. A case study in which the proposed system is applied to the Shanghai Tower is presented. The peak picking method is used as an example in the test environment to track the latency of each main operation under different parallelism schemes. The results show that computing in parallel effectively reduces the latency and provides a reference for integrating the random decrement technique (RDT), stochastic subspace identification (SSI), or other more complex but effective algorithms in parallel into the system in the future. The total latency under the test environment from data generation to data transmission to the web server is approximately only 200-400 ms, which indicates the excellent real-time performance of the system.

Structural monitoring and remaining fatigue life estimation of typical welded crack details in the Manhattan Bridge

Manhattan Bridge is a suspension structure that connects Manhattan to Downtown Brooklyn. This bridge is over 110 years old and subjected to daily repeated axles of transit trains and other vehicular traffic. Visual inspections of the bridge revealed manifestation of many cracks in the welded details due to the out-of-plane forces at the connections between transit and floor beams. The research reported herein, pertains to the fatigue life evaluation and reliability assessment of typical cracked welded details. The bridge was instrumented with a real-time structural health monitoring system based on FBG sensors for remote monitoring of the cracked connections. Both deterministic and probabilistic approaches were employed for prediction of the remaining fatigue life of typical cracked details. For the same fatigue reliability index of 2.3, the deterministic and probability based methods yielded remaining fatigue life of 369 and 120 years, respectively, for the typical welded connections details of the bridge.

Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images.

This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with a method based on Castigliano’s theorem.

Structural Health Monitoring of Underground Structures in Reclamation Area Using Fiber Bragg Grating Sensors.

The long-term structural performance of underground structures in reclamation areas is very sensitive to the vibrations caused by vehicles passing above the structures and environmental factors (e.g., tide levels, rainfall and temperature). In the present study, an integrated remote real-time structural health monitoring system using fiber Bragg grating sensors was developed to assess the structural performance of underground structures. Using a composite road box-type structure project in a reclamation area in Southern China as a case study, the developed real-time system was implemented to investigate the effects of changes in tide levels, rainfall, temperature and vehicle induced vibrations on crack propagation in the structure. The results show that the change in tide levels has little influence on the change in crack width in the structure, whereas variations in temperature could significantly influence the crack width with an average Pearson correlation of around 0.8. In addition, the crack width generally decreases with an increase in rainfall. Furthermore, a relatively low frequency (<25 Hz) induced by the traffic could result in a relatively larger crack width.

Applications of smart piezoelectric materials in a wireless admittance monitoring system (WiAMS) to Structures—Tests in RC elements

The application of an innovative real-time structural health monitoring system is studied through tests performed on flexural and shear-critical reinforced concrete elements subjected to monotonic and cyclic loading. The test set-up involves a Wireless impedance/Admittance Monitoring System (WiAMS) that comprises specially manufactured small-sized portable devices to collect the voltage frequency responses of an array of smart piezoelectric transducers mounted on structural members of reinforced concrete constructions. Damage detection and evaluation is achieved using the in-situ measurements of the integrated piezoelectric sensors/actuators signals at the healthy state of the member and at various levels of damage during testing. Three different installations of Piezoelectric lead Zirconate Titanate (PZT) transducers are examined: (a) epoxy bonded PZTs on the surface of the steel reinforcing bars of the flexural elements, (b) PZTs embedded inside the concrete mass of the shear-critical beams and (c) externally epoxy bonded PZTs attached to the concrete surface of the tested elements. The smart piezoelectric materials have been pre-installed before testing based on the potential flexural and shear cracking of the elements. Quantitative assessment of the examined damage levels using values for the statistical damage index is also presented and discussed. Voltage signals and index values acquired from the PZTs’ measurements using the proposed wireless monitoring technique demonstrated obvious discrepancies between the frequency response of the healthy and the examined damage levels for every tested element. These differences clearly indicate the presence of damage, whereas their gradation reveals the magnitude of the occurred damage. Promising results concerning the prediction of the forthcoming fatal failures at early damage stages have also been derived.

A comparative study on crack identification of structures from the changes in natural frequencies using GA and PSO

Purpose – The early detection of cracks, corrosion and structural failure in aging structures is one of the major challenges in the civil, mechanical and aircraft industries. Common inspection techniques are time consuming and hence can have strong economic implications due to downtime. The paper aims to discuss these issues. Design/methodology/approach – As a result, during the past decade a number of methodologies have been proposed for detecting crack in structure based on variations in the structure's dynamic characteristics. This work showcases the efficacy of particle swarm optimization (PSO) and genetic algorithm (GA) in damage assessment of structures. Findings – Efficiency of these tools has been tested on structures like beam, plane and space truss. The results show the effectiveness of PSO in crack identification and the possibility of implementing it in a real-time structural health monitoring system for aircraft and civil structures. Originality/value – The methodology presented establishes the PSO as robust and competent tool over GA for crack identification using changes in natural frequencies.

Identification and Monitoring of Modal Parameters in Aircraft Structures Using the Natural Excitation Technique (NExT) Combined with the Eigensystem Realization Algorithm (ERA)

The early detection of cracks, fatigue, corrosion, and structural failure in aging aircraft is one of the major challenges in the aircraft industry. Common inspection techniques are time consuming and hence can have strong economic implications due to aircraft downtime. As a result, during the past decade a number of methodologies have been proposed for detecting structural damage based on variations in the structure’s dynamic characteristics. This paper describes the implementation of the natural excitation technique (NExT) combined with the eigensystem realization algorithm (ERA) to determine the dynamic characteristics of a T-34A Mentor acrobatic category aircraft and a modified DC-3 cargo/transport category aircraft. In-flight acceleration data were processed using NExT-ERA to monitor the predominant natural frequencies and associated mode shapes of the aircraft for varying flight conditions. The results show the effectiveness of this modal identification methodology and the possibility of implementing it in a real-time structural health monitoring system for aircraft.

Application of a Web-enabled real-time structural health monitoring system for civilinfrastructure systems

The system architecture of a novel structural health monitoring system that isoptimized for the continuous real-time monitoring of dispersed civil infrastructures ispresented. The monitoring system is based on a highly efficient multithreaded softwaredesign that allows the system to acquire data from a large number of channels,monitor and condition this data, and distribute it, in real time, over the Internet tomultiple remote locations. Bandwidth and latency issues that impact the operationof monitoring systems are discussed. The application of the monitoring systemunder discussion to a long span, flexible bridge in the metropolitan Los Angelesregion is described. The bridge had previously been instrumented with 26 strongmotion accelerometers. Sample ‘quick analysis’ results continuously provided by themonitoring system are presented and interpreted. System identification results,obtained through off-line batch processing, are presented for a data set from arecent earthquake that automatically triggered the recording capability of thesystem. It is shown that, using a time domain system identification approach,the bridge stiffness and damping matrices can be identified from the earthquakedata set and subsequently used to determine the bridge modal properties, suchas frequencies and damping ratios. In this approach the bridge is modeled as amulti-input/multi-output system with order compatible with the number of availablesensors. Implementation issues requiring further investigation are presented and discussed.