Bridge Vibration Monitoring Research Articles

Bridging the Gap: commodifying infrastructure spatial dynamics with crowdsourced smartphone data

Structural information deficits about aging bridges have led to several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of widespread deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive and has produced structural information, i.e., modal frequencies, in crowdsensing applications. Even so, current methods cannot extract spatial vibration characteristics with uncontrolled datasets that are needed for damage identification. Here we present an extensive real-world study with crowdsourced smartphone-vehicle trips within motor vehicles in which we estimate absolute value mode shapes and simulate damage detection capabilities. Our method analyzes over 800 trips across four road bridges with main spans ranging from 30 to 1300 m in length, representing about one-quarter of bridges in the United States. We demonstrate a bridge health monitoring platform compatible with ride-sourcing data streams that check conditions daily. The result has the potential to commodify data-driven structural assessments globally.

Laser-upgraded coal tar for smart pavements in road and bridge monitoring applications

The implementation of an intelligent road network system requires many sensors for acquiring data from roads, bridges, and vehicles, thereby enabling comprehensive monitoring and regulation of road networks. Given this large number of required sensors, the sensors must be cost-effective, dependable, and environmentally friendly. Here, we show a laser upgrading strategy for coal tar, a low-value byproduct of coal distillation, to manufacture flexible strain-gauge sensors with maximum gauge factors of 15.20 and 254.17 for tension and compression respectively. Furthermore, we completely designed the supporting processes of sensor placement, data acquisition, processing, wireless communication, and information decoding to demonstrate the application of our sensors in traffic and bridge vibration monitoring. Our novel strategy of using lasers to upgrade coal tar for use as a sensor not only achieves the goal of turning waste into a resource but also provides an approach to satisfy large-scale application requirements for enabling intelligent road networks.

The footbridge Jesípek – application of radar interferometry for dynamic response evaluation

Recent advances in radar systems have led to the development of radar interferometry (RI) methods for contactless vibration monitoring of large-scale structures, i.e. bridges, water tower reservoirs, and factory chimneys. Interferometric radars are devices capable to measure with 200 Hz sampling frequency and relative movement precision of 0.1 mm up to 0.01 mm. The major part of this paper describes an in-situ footbridge experiment near Hradec Králové. Radar interferometry devices were deployed along with ordinary techniques compounded by accelerometers, wiring, and acquisition station. Experiment was focused on the evaluation of basic dynamic structural properties such as natural frequencies and mode shapes. A lot of attention was also given to the result comparison of these measurement methods. Test results have confirmed the applicability of RI for bridge vibration monitoring.

Periodic-Filtering Method for Low-SNR Vibration Radar Signal

Radar is a non-contact, high-precision vibration measurement device and an important tool for bridge vibration monitoring. Vibration information needs to be extracted from the radar phase, but the radar phase information is sensitive to noise. Under low signal-to-noise ratio (SNR) data acquisition conditions, such as low radar transmission power or a long observation distance, differential phase jump errors occur and clutter estimation becomes difficult, which leads to inaccurate inversion of vibration deformation. Traditional low-pass filtering methods can filter out noise to improve SNR, but they require oversampling. The sampling rate needs to be several times higher than the Doppler bandwidth, which is several times higher than the vibration frequency. This puts high data acquisition requirements on radar systems and causes large data volumes. Therefore, this paper proposes a novel vibration signal filtering method called the periodic filtering method. The method uses the periodicity feature of vibration signals for filtering without oversampling. This paper derives the time-domain and frequency-domain expressions for the periodic filter and presents a deformation inversion process based on them. The process involves extracting the vibration frequency in the Doppler domain, suppressing noise through periodic filtering, estimating clutter using circle fitting on the data complex plane, and inverting final deformation with differential phase. The method is verified through simulation experiments, calibration experiments, and bridge vibration experiments. The results show that the new periodic filtering method can improve the SNR by five times, resolve differential phase jumps, and accurately estimate clutter, thus getting submillimeter-level vibration deformation at low SNR.

A Novel GB-SAR System Based on TD-MIMO for High-Precision Bridge Vibration Monitoring

Ground-based synthetic aperture radar (GB-SAR) is a highly effective technique that is widely used in landslide and bridge deformation monitoring. GB-SAR based on multiple input multiple output (MIMO) technology can achieve high accuracy and real-time detection performance. In this paper, a novel method is proposed to design transmitting and receiving array elements, which increases the minimum spacing of the antenna by sacrificing several equivalent phase centers. In MIMO arrays, the minimum antenna spacing in the azimuth direction is doubled, which increases the variety of antenna options for this design. To improve the accuracy of the system, a new method is proposed to estimate channel phase errors, amplitude errors, and position errors. The position error is decomposed into three directions with one compensated by the phase error and two estimated by the strong point. Finally, we validate the accuracy of the system and our error estimation method through simulations and experiments. The results prove that the GB-SAR system performs well in bridge deformation and vibration monitoring with the proposed method.

Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring

Bridges are the essential components in lifeline transportation systems, and their safe operation is of great importance. Information on structural damage could assist in timely repairs and reduce downtime. With the latest advancements in sensing technology, collecting vibration data from bridges has become more accessible. However, effective vibration processing is still a challenge, given the high dimensionality and massive size of vibration data. Existing studies have shown that machine/deep learning techniques can be valuable tools for this task. However, the learning and computational capacities of these models are challenged in the presence of large sensor arrays. We propose Trident as a novel deep learning framework that enables automatic damage feature extraction by simultaneously learning from temporal and three-dimensional (3D) spatial variations of 6D input data in instrumented bridges. Trident is equipped with 3 ConvLSTM3D branches to achieve this goal. A 3D steel truss bridge subject to dynamic traffic loads is monitored for its vibrations to evaluate Trident’s robustness in finding damaged elements. A damage dataset of 52,800 vehicle passing simulations is generated leveraging a database of 528 passenger vehicles in the United States, obtained from the National Highway and Traffic Safety Administration. Bayesian optimization is utilized to tune the model’s hyperparameters, achieving a test Node Average Geometric Mean Accuracy of 86%. This level of performance is promising given the high dimensionality and complexities of the output space in vibration-based monitoring. Trident’s concept can be extended to other vibration monitoring tasks with different time series data and damage labeling strategies.

A double-mass block acceleration sensor based on FBG

A double-mass block acceleration sensor based on Fiber Bragg Grating (FBG) is proposed to meet the needs of acceleration sensors in vibration monitoring. The proposed sensor is composed of FBG, beam and double mass block, and the FBG is fixed in the middle of the upper surface of the dual mass block by epoxy glue. Through theoretical analysis and experimental verification, the resonant frequency, sensitivity and lateral anti-interference ability of the sensor have been investigated. The experimental results show that the natural frequency of the sensor is 1000 Hz, the sensitivity is 26.7 pm/g when the frequency is 250 Hz, and the lateral anti-interference degree is less than 6%. And the sensor structure in this paper is simple and easy to fabricate. Therefore, it has a great application prospect in the vibration monitoring of bridges, roads and buildings.

IMPLEMENTASI ALGORITMA FAST FOURIER TRANSFORM PADA MONITOR GETARAN UNTUK ANALISIS KESEHATAN JEMBATAN

Jembatan merupakan bagian yang penting dalam suatu sistem jaringan jalan, karena pengaruhnya yang signifikan bila jembatan itu runtuh atau tidak berfungsi dengan baik, maka keruntuhan jembatan akan mengganggu kelancaran transportasi orang dan barang. Sistem monitor dikembangkan untuk dapat mengetahui kondisi struktur jembatan agar dapat mengantisipasi kegagalan struktur. Dimana dari hasil monitor ini akan dihasilkan data ferekuensi alami, tekanan beban dan lendutan. Data akselerasi Sumbu x,y,dan z akan diolah dengan algoritma Fast Fourier Transform (FFT), kemudian data ferekuensi yang telah diolah akan menghasilkan ferekuensi alami jembatan. Dari hasil penelitian menunjukkan bahwa monitoring getaran jembatan dapat ditampilkan dengan data akselerasi sumbu X, Y dan Z maupun FFT. Dengan beban 6 KG didapat akselerasi Sumbu x = 1.29, Y = 0.73 dan Z = 13.39, Ferkunesi Dasar X = 1.82, Y = 1.03 dan Z = 18.93. Untuk parameter lendutan dengan akurasi 99.66%, dengan beban 6Kg didapat Rata-rata error sebesar 11 mm (millimeter) dengan rata-rata persentase error sebesar 0.0396%. untuk parameter Teakann berat dengan akurasi 99,534% , dengan beban 6 Kg didapat Rata-rata persentasi error sebesar 0.1821 Kg dengan persentase error tertinggi sebesar 0.4566%. Kesimpulan yang didapatkan dari hasil penilitian bahwa setiap sensor untuk mengitung parameter kesehatan pada purwarupa Jembatan dengan bentang 65cm dari aspek getaran tergolong pada nilai kondisi baik dengan nilai getaran 1,82 Hz. Ditinjau dari aspek lendutandengan beban muatan 6KG, jembatan tidak baik, karena memiliki rata-rata lendutan adalah 10 mm atau 1 cm, dengan batas maksimal 0,65 cm.

High frequent total station measurements for the monitoring of bridge vibrations

Abstract Robotic total stations (RTS) are frequently used for the measurement of temperature induced bridge deformations or during load testing of bridges. In experimental setups, total stations have also been used for the measurement of dynamic bridge deformations. However, with standard configurations the measurement rate is not constant and on average an update rate of 7–10Hz can be achieved. This is not sufficient for the vibration monitoring of bridges considering their natural frequencies which are also in the same range. In this paper, we present different approaches to overcome these problems. In the first two approaches we demonstrate how the measurement rate to prisms can be increased to 20Hz to determine vertical deformations of bridges. Critical aspects like the measurement resolution of the automated target tracking and the correct sequence of steering commands are discussed. In another approach we demonstrate how vertical bridge vibrations can be measured using an image assisted total station (IATS) and corresponding processing techniques. The advantage of image-based methods is that structural features of a bridge like bolts can be used as targets. Therefore, no expensive prisms have to be mounted and access to the bridge is not required. All approaches are verified by laboratory investigations and their suitability is proven in a field experiment on a 74m long footbridge. In this field experiment the natural frequencies derived from the total station measurements are compared to the results of accelerometer measurements.

Wireless Sensor Placement for Bridge Health Monitoring Using a Generalized Genetic Algorithm

The optimal placement of wireless sensors is very different from conventional wired sensor placement due to the limited transmission range of the wireless sensors. This constraint on the inter-sensor distance makes the optimization problem difficult to solve with conventional gradient-based methods. In this paper, an improved generalized genetic algorithm (GGA) based on a self-adaptive dynamic penalty function (SADPF) is proposed for the optimal wireless sensor placement (OWSP) in bridge vibration monitoring. The mathematical model of the OWSP problem is established, and it considers both the bridge vibration monitoring requirements and the constraints of the data transmission range in wireless sensor networks (WSNs). SADPF, which can automatically adjust the amount of penalization for constraint violations according to the evolution generation number and the degree of violation, is then developed so that the wireless sensor placement can be optimized using GGA. Subsequently, the GGA is improved by implementing an elite conservation strategy, a worst elimination policy and a dual-structure coding system. Finally, a numerical experiment is presented with a long-span suspension bridge to demonstrate the feasibility and efficiency of the proposed method, and some indispensible discussions are also given. The results indicate that the wireless sensor configurations that are optimized by the improved SADPF-based GGA can simultaneously meet the data transmission demands in a WSN and fulfill the requirements for structural condition assessment. The developed SADPF can minimize the influence of the limited data transmission range on the search process for the OWSP. The improved SADPF-based GGA quickly and robustly converges to the global optimal solution.

Long-Term Vibration Monitoring of Cable-Stayed Bridge Using Wireless Sensor Network

Wireless sensor networks provide a lot of advantages for vibration monitoring of bridges. The installation time and implementation cost of the monitoring system are greatly reduced by the adoption of this innovative technology. This paper presents a long-term vibration monitoring of the Hwamyung cable-stayed bridge in Korea using an Imote2-platformed wireless sensor network. First, the wireless vibration monitoring system of the bridge is briefly described by outlining the test history, the design of wireless sensor system, and the sensor deployment. Next, the vibration behaviors of the bridge are experimentally examined with respect to the variation of temperature, the wind loads induced by typhoons, and the change of bridge deck mass caused by pavement.

The Development Process Research of Wireless Bridge Vibration Monitoring

This research focuses on the study of the developing process of wireless sensor network (WSN) monitoring system. In addition, problems confronted during the developing process are also reviewed and solved. In the WSN platform development, Supernode and accelerometers are used to collect the acceleration data and examine the safety of bridges. A global-position-system (GPS) chip is also used to obtain high-precision time information to promote the accuracy of the examination. Moreover, IEEE 802.15.4 was chosen for the communication protocol in the WSN platform as it complies with the demand of low cost and low power consumption. For the experiment requirement and the stability of the WSN system, this research has undergone 3 versions of modification. Moreover, this system is employed to monitor the vibration of Chung-Sha Bridge in Taiwan for the field- implementation experiment.

Low-Frequency Wireless Accelerometer to Bridge Health Monitoring

According to the requirements and characteristics of the long-span bridge vibration monitoring, this paper presents one kind of low-frequency wireless accelerometer monitoring system based on PIC and MEMS (Micro-electromechanical Systems) acceleration sensor. The system was composed of sensor module, data acquisition and processing module, micro-processing module, wireless transmitting and receiving module, and a computer system. Model 1221 was the sensor chip, the measure data processed by MCU, and transmitted by wireless transmitting module based on Zigbee networks. As the date was received by the wireless receiving module, re process the sensing data by computer, to get the vibration parameter and assess the vibration. The system was validated with standard vibrator.

System identification of highway bridges from ambient vibration using subspace stochastic realization theories

In this study, the subspace stochastic realization theories (SSR model I and SSR model II) have been applied to a real bridge for estimating its dynamic characteristics (natural frequencies, damping constants, and vibration modes) under ambient vibration. A numerical simulation is carried out for an arch-type steel truss bridge using a white noise excitation. The estimates obtained from this simulation are compared with those obtained from the Finite Element (FE) analysis, demonstrating good agreement and clarifying the excellent performance of this method in estimating the structural dynamic characteristics. Subsequently, these methods are applied to the vibration induced by both strong and weak winds as obtained by remote monitoring of the Kabashima bridge (an arch-type steel truss bridge of length 136 m, and situated in Nagasaki city). The results obtained with this experimental data reveal that more accurate estimates are obtained when strong wind vibration data is used. In contrast, the vibration data obtained from weak wind provides accurate estimates at lower frequencies, and inaccurate accuracy for higher modes of vibration that do not get excited by the wind of lower intensity. On the basis of the identified results obtained using both simulated data and monitored data from a real bridge, it is determined that the SSR model II realizes more accurate results than the SSR model I. In general, the approach investigated in this study is found to provide acceptable estimates of the dynamic characteristics of highway bridges as well as for the vibration monitoring of bridges.

An Autonomous Wireless Sensing Module for Long-Term Structural Health Monitoring Applications


 
 
 Structural health monitoring (SHM) of civil infrastructure is among many areas with a need for low-cost autonomous wireless sensors capable of operating without maintenance for extended periods of time. This paper describes the 3AT-09 wireless sensing module developed by Green SHM Systems. The module incorporates a microcontroller, memory, digital accelerometer, tilt and temperature sensors, and a transceiver. It is powered by a combination of batteries and an energy harvesting device. The module can operate in two modes – active mode and sleep mode. Low power consumption in sleep mode, along with power management capabilities and the use of energy harvesting allows for a very long service life of the module. It is estimated that the module can be self-sufficient for at least 25 years when used as part of a typical SHM application with short active mode sessions. The autonomous wireless sensing module is designed as a platform for long- term monitoring applications. Different sensors and energy harvesting devices can be integrated with the module. Experimental results on the use of sensing modules for vibration monitoring of bridges and tall buildings are provided.