Journal of Civil Structural Health Monitoring

By using a piezoelectric actuator, a real steel bridge can be accelerated locally to detect the effect of small damage like fatigue crack and connection damage as a response to change of vibration. Power spectrum density (PSD) function of the response can give us the indication of the extent of damage before and after the damage. Detailed analysis of PSD before and after damage can give us the location and size (area) of damage. This paper shows the experimental result of damage detection by using local excitation method on a real bridge.

This paper presents the results of a long-term concrete monitoring campaign in an underground tunnel in Brussels. The system consists of several pairs of embedded ultrasonic piezoelectric transducers arranged in a pitch-catch configuration which have been placed in the concrete ceiling of the tunnel in areas where old concrete was demolished and then repaired. The monitoring system is fully automated and sends the recorded signals to a cloud-based system in our university where they are post-processed to extract indicators of structural changes in the monitored regions, and send automated email reports. A first period of six months is studied, during which the monitored areas have been repaired with skim mortar. The post-processing of the measured signals allows to identify clearly the time of repair in each zone and the evolution of the hardening process of the repair mortar. A second monitoring period of one year is then studied where it is found that despite the proposed improvement to the time stretching technique used to filter out the effects of changing environmental conditions, our indicators are still showing variations in periods when the temperature is very high in the tunnel. A method based on observed statistical correlations between the indicators computed in the different regions is then proposed and shown to be very efficient to remove the remaining variability and make the system very robust to environmental changes. Extreme value statistics is also presented as a tool to establish relevant thresholds for alarm-triggering with a very low level of potential false alarms. With all these developments, the monitoring system can automatically detect structural changes in the tunnel in real time while being robust to unavoidable changes in the environmental conditions in the tunnel.

Several steel bridges in Egypt were built during the late nineteenth to early twentieth centuries. At this time, riveted construction was the method used for building up members and for the connection of one member to another. In this paper, field measurements were performed to find the actual dimension of an existing old riveted steel bridge (El-Ministerly Bridge, Egypt). The inspection of the steel bridge did not find any clear cracks. Finite element models were created to predict the response of the steel bridge. The finite element model was used to identify the location of the stress concentration. A static field test was performed using a 49-ton truck to evaluate actual strain measurements in different locations over the bridge. The strain measurement was used to validate the finite element model while was able to predict the experimental data closely. As an application of the use of the finite element model, evaluate the bridge was executed using AASHTO standards, Egyptian code (ECP) and S–N curves from the literature. It was observed that all stress ranges for this bridge were less than the ECP limits (Fsr) and the estimated remaining fatigue life is about 11 years if it is environmentally protected.

High-quality prototype observations are the basis for a comprehensive analysis of dam structural behavior. However, missing values, especially consecutive missing values (CMVs), are a major barrier. This paper innovatively proposes the concepts of data enhancement (DE) and quadratic imputation (QI) to address CMVs of dam deformation. The Temporal-Spatio Extreme Learning Machine (TsELM) is the core of DE, which exploits the short-term superiority of temporal model to estimate part of the missing segment, providing more reliable modeling information for the imputation of the remaining data. Afterward, the DE components and historical data are incorporated into the training sample and ELM-based QI is performed to obtain the complete simulation results. Analysis shows that the hierarchical imputation method requires fewer parameters and is conducive to constructing a unified imputation framework. Meanwhile, the imputation accuracy of the method is higher than that of traditional models, and it is applicable to dam projects with different deformation sampling frequencies.

Distributed fiber optic sensors (DFOS) were used, for the first time, to monitor lateral buckling of a rail under axial loading with different boundary conditions. In the experiments, two types of DFOS systems were used to collect the distributed strain data, and the performance of the two systems was compared. The distributed strain data were used to develop a fitted strain plane at each cross-section along the rail, and axial strain and bending curvature were derived from the fitted strain plane. A data extrapolation method using the distributed curvature data was developed to evaluate the actual boundary conditions at the ends of the rail since they did not match assumed ideal behaviour. The distributed rail deflection along the length of rail was calculated by integrating distributed curvature data and the results were compared with the deflection measured by linear potentiometers (LPs).

Bài báo này trình bày một khuôn khổ để phát hiện thiệt hại tự động bằng cách sử dụng một chuỗi liên tục dữ liệu giám sát sức khỏe kết cấu. Nghiên cứu đã sử dụng các biến dạng đo được từ một bộ cảm biến tối ưu được triển khai trên một cầu truss bằng thép, có hai đường ray. Sự suy giảm tại các mối nối giữa thép đỡ và xà sàn, một khuyết tật phổ biến, là trọng tâm của nghiên cứu này; tuy nhiên, phương pháp đề xuất có thể được áp dụng để đánh giá tình trạng của nhiều thành phần và chi tiết kết cấu khác nhau. Khuôn khổ đã sử dụng Các Chế Độ Chính Quy (POMs) như là các đặc trưng thiệt hại và Mạng Nơron Nhân Tạo (ANNs) như một phương pháp tự động hóa để suy luận vị trí và cường độ thiệt hại từ các POMs. Sự biến đổi của POMs, thường phụ thuộc vào tải (input), cuối cùng đã được sử dụng như là các chỉ số thiệt hại. Sự biến đổi ở đầu vào đòi hỏi việc sử dụng ANNs để giúp tách rời các thay đổi của POM do biến đổi tải so với những thay đổi gây ra bởi các khuyết tật, những thay đổi có thể khiến cho khuôn khổ đề xuất độc lập với đầu vào, một tiến bộ quan trọng. Để phát triển một khuôn khổ phát hiện thiệt hại hiệu quả và tự động chỉ sử dụng đầu ra, quá trình làm sạch và chuẩn bị dữ liệu đã được thực hiện trước khi đào tạo ANNs. Các “kịch bản” thiệt hại đã được giới thiệu một cách nhân tạo vào các tập dữ liệu đầu ra (biến dạng) được ghi lại trong khi giám sát lượt tàu qua cầu được chọn. Thông tin này, theo đó, đã được sử dụng để đào tạo ANNs bằng cách sử dụng Toolbox Mạng Nơron của MATLAB. Các ANNs đã được đào tạo được kiểm tra đối với các sự kiện tải được giám sát và các kịch bản thiệt hại nhân tạo. Tính khả thi của khuôn khổ chỉ sử dụng đầu ra đã được nghiên cứu thông qua các nghiên cứu về cầu dưới các điều kiện hoạt động. Để tính toán các tác động của các khuyết tật tiềm năng tại các mối nối giữa thép đỡ và xà sàn, biên độ tín hiệu đo được đã được giảm nhân tạo tại các vị trí chọn lọc. Kết luận rằng khuôn khổ đề xuất có thể phát hiện thành công các khuyết tật giả mạo áp đặt lên các tín hiệu đo được trong điều kiện hoạt động.

Main complexities of classical health-monitoring techniques, such as modal strain energy (MSE) and modal curvature (MCR), are noise sensitivity and false alarming. On the other hand, the modern techniques, such as model updating, become very complex for a large number of variables and large search space. In this present study, a new health-monitoring technique based on the combination of MSE and model updating is presented for fast and accurate identification of structural damage. In this technique, the probable locations of an unknown damage are determined using MSE-based damage index (MSEDI). The effect of noise is reduced by passing the vibration data through Morlet wavelet filter. In this context, the peaks of the MSEDI in the wavelet domain are considered as the suspicious locations of damages. The exact location and the severity of the damage are then determined by applying model updating technique. To achieve this, reduction of stiffnesses at the probable locations is considered as the updating variable. The objective function is developed using MCR in wavelet domain and minimized by particle swarm optimization technique. The final updated values of the stiffnesses at those locations represent the actual locations and severity of the damage. The technique is applied on a simulated single-storied plane steel frame structure and a similar experimental model with welded joints for single and multiple damage scenarios. The damage was introduced either near to or away from the joints by making saw-cut grooves representing loss of stiffness. The results depict the effectiveness of the technique for potential application in real-life structures.

Long-span bridges constitute one of the most critical lifelines in countries where they are constructed since they shorten transportation by providing passage through large waterways, such as rivers, channels, dams, and the sea. Owing to its geographical location, Turkey is a transit country between Asia and Europe. As long-span bridges are subject to heavy traffic and seismic hazards in Turkey, monitoring their structural health and performing their maintenance in a timely and cost-effective manner is essential. These bridges pose maintenance challenges due to their sizes. Because of their high towers and hard-to-access cables in general, the most reliable method of monitoring the structural condition of such bridges under service is to build structural health monitoring (SHM) systems. This paper reports on the results of a study in which the SHM systems of long-span bridges in Turkey, which are among the largest bridges across the world, are described. The characteristics of these systems utilized are explained in detail. In addition, SHM data acquired on the Fatih Sultan Mehmet Bridge during a recent offshore event on 26th September 2019, the Silivri Earthquake (Mw 5.8), are analyzed. The findings are validated using experimental research results presented in the literature, and the comparison was indicated good agreement to identify the bridge’s dynamic characteristics. Finally, problems encountered in SHM systems because of extreme loads are explained, and recommendations are provided for future applications.

Aiming at the problem that earth-rock dam structure is susceptible to non-stationary signal interference in the process of collecting vibration information, this paper proposes a feature information extraction method based on the fusion of Dispersion Entropy Variational Mode Decomposition (DVMD) and Variance Dedication Rate (VDR) improved by Dispersion Entropy. First, multi-channel vibration signals are dynamically fused using the variance dedication rate to extract the complete vibration information of the dam body; then the entropy value of each modal component (Intrinsic Mode Function) under different decomposition layers is calculated by using Dispersion Entropy, and the entropy turning point is selected to determine the number of decomposition modes of DVMD, to compensate for the insufficiency of blind selection of decomposition modes in Variational Mode Decomposition. The entropy value turning point is selected to determine the number of decomposition modes of DVMD, which can make up for the deficiency of blindly selecting decomposition modes in Variational Mode Decomposition. To verify the accuracy and effectiveness of the method in this paper, three groups of simulated signals are constructed for numerical simulation, and it is found that its noise reduction effect is better than that of digital filtering, wavelet thresholding and Improved Variational Mode Decomposition, and the signal feature information can be effectively extracted. Combining the measured data of the embankment dam of HeLong dam site under the excitation of natural environment, the operational characteristic information of the dam body is analyzed and compared with the finite element simulation results, and the study shows that the DVMD–VDR method can efficiently extract the complete vibration characteristic information of the structure, which has a good engineering practicability, and it can provide the basis for the on-line monitoring of the structural operational status of the embankment dam.