Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach

This study aims to investigate the performance of a new damage detection method proposed based on the combination of two signal processing techniques which are complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification (CEEMDAN-MUSIC). The proposed damage detection approach begins with determining the power density spectrum, namely, the pseudospectrum, from the acceleration response of a structure. Then, the CEEMDAN algorithm is used to decompose the vibration signal into a set of intrinsic mode functions (IMFs). Furthermore, the MUSIC algorithm is applied to the first IMF of the processed signal to determine the frequency pseudospectrum, prior to and post the damage states of the structure. The effectiveness of the proposed methodology is experimentally validated using a laboratory-scale model of a steel truss bridge exposed to a white noise excitation. The damage states of the truss bridge are implemented by replacing a specified diagonal element with reduced cross-sectional stiffness. The experimental results demonstrate the superiority of the CEEMDAN-MUSIC method in comparison with the performance of pure MUSIC and traditional frequency domain techniques. The advantages of the proposed technique are also discussed in terms of identifying the presence of the damage, addressing its location, and quantifying the damage levels which are summarized as the damage detection and characterization.

Hilbert transform and spectral kurtosis based approach in identifying the health state of retrofitted old steel truss bridge

Purpose This study aims to include the diagnosis of an old concrete deck steel truss rural road bridge in the damaged and retrofitted state through vibration response signals. Design/methodology/approach The analysis of the vibration response signals is performed in time and time-frequency domains using statistical features-root mean square, impulse factor, crest factor, kurtosis, peak2peak and Stockwell transform. The proposed methodology uses the Hilbert transform in combination with spectral kurtosis and bandpass filtering technique for obtaining robust outcomes of modal frequencies. Findings The absence or low amplitude of considered mode shape frequencies is observed both before and after retrofitting of bridge indicates the deficient nodes. The kurtosis feature among all statistical approaches is able to reflect significant variation in the amplitude of different nodes of the bridge. The Stockwell transform showed better resolution of present modal frequencies but due to the yield of additional frequency peaks in the vicinity of the first three analytical modal frequencies no decisive conclusions are achieved. The methodology shows promising outcomes in eliminating noise and visualizing distinct modal frequencies of a steel truss bridge. Social implications The findings of the present study help in analyzing noisy vibration signals obtained from various structures (civil or mechanical) and determine vulnerable locations of the structure using mode shape frequencies. Originality/value The literature review gave an insight into few experimental investigations related to the combined application of Hilbert transform with spectral kurtosis and bandpass filtering technique in determining mode frequencies of a steel truss bridge.

Uncertainties Consideration in Empirical Frequency Response Function Data for Damage Identification Based On Artificial Neural Network

The modern application of frequency response function (FRF) with artificial neural networks (ANN) has become one of the leading methods in vibration-based damage detection approach. However, since full-size empirically obtained FRF data is used as ANN input, a broad composition ANN input layer series would occur. Consequently, principal component analysis (PCA) is adopted to compress the FRF data magnitude. Despite this, PCA alone is unable to select the important FRF data features effectively, due to the exceedingly FRF data size in addition with existing uncertainties. Therefore, this study proposed the merger of a non-probabilistic analysis and ANN approach with PCA by considering the uncertainties effect and the inefficiency of using empirical FRF data. The empirical FRF data is obtained from a steel truss bridge structure. The results show that the PoDE values above 95% are measured at the particular executed damage locations and the DMI values show the damage severity at the actual damage locations. Overall, the results show that the proposed method is capable in considering the uncertainties effect on the empirical FRF data for structural damage identification.

Study on the Mechanical Characteristics of the Sleeper Slab Track on a Long-Span Steel Truss Bridge

This paper uses the long-span steel truss slab track as its research subject to analyze the new type of sleeper slab track structure with an experimental method. Firstly, a full-scale model was established in the laboratory to form a “rail–sleeper slab–self-compacting concrete cushion–steel beam” composite structure, and a fatigue test was performed on the track structure. The cyclic load was set up as a sine form with a range between 75 and 375 kN at a 5 Hz interval and 3 × 106 cycles. Based on the test, the performance of the track structure under cyclic train load was studied. Secondly, after every 106 loading cycles, the vertical static loading test and horizontal resistance test of the track structure were carried out to obtain the strain and displacement under different loading cycles. Finally, after 3 × 106 cycles of sine cyclic loading, the horizontal ultimate resistance test of the track structure was carried out to study its horizontal failure mode. The aims of this paper were to verify the applicability of the sleeper slab track, identify the mechanical properties, and determine the unfavorable position. The findings can provide an important reference for the practical use of the sleeper slab track structure.

Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study

Signal processing is one of the essential components in vibration-based approaches and damage detection for structural health monitoring. Since signals in the real world are often nonlinear and non-stationary, especially in extended and complex structures, such as bridges, the Hilbert–Huang transform is used for damage assessment. In recent years, the empirical mode decomposition technique has been gradually used in structural health monitoring and damage detection. In this article, the application of complete ensemble empirical mode decomposition with adaptive noise technique is investigated to identify the presence, location, and severity of damage on a steel truss bridge model. The target is built at laboratory conditions and experimentally subjected to white noise excitations. By employing complete ensemble empirical mode decomposition with adaptive noise technique, four key features extracted from the intrinsic mode functions, including energy, instantaneous amplitude, unwrapped phase, and instantaneous frequency, are assessed to localization, quantification, and detection of damage both quantitatively and qualitatively. In addition, to further explore the sensitivity of the damage detection approach based on the complete ensemble empirical mode decomposition with adaptive noise technique method, several improved damage indices are proposed based on the combinations of two statistical time-history features, including kurtosis and entropy features with the energy and instantaneous amplitude features of the analyzed signal. The experimental results from the damage indices based on the extracted features demonstrate the robustness, superiority, and more sensitivity of the complete ensemble empirical mode decomposition with adaptive noise technique method in addressing the damage location, classifying the severity, and detecting the damage compared to empirical mode decomposition and ensemble empirical mode decomposition techniques.

Analysis on the structural type of large-span steel truss bridge specially designed for cables

When the installation of cables and pipelines needs to go across rivers, bridges are usually adopted to support the cables and pipelines for crossing the rivers. The measure can make full use of the space resources and have no effect on the flow pattern of rivers. For this reason, analysis on the structural-type design of a large-span steel truss bridge specially used for cables has been performed. The numerical results indicate that the stayed-cable bridge with steel truss beam and concrete main tower has better performance and improved structural type caparisoned with that of the beam and arch bridges, and the construction of the major beam can be without the temporary support.