Damage localization in civil infrastructure, such as large-scale reinforced concrete (RC) pedestrian bridges, is essential for conducting precise maintenance and avoiding catastrophic failures. In this study, multiple one-dimensional convolutional neural networks (1-D CNNs) are developed for automatically extracting implicit damage-sensitive features from the structural raw dynamic responses to localize damage in the pile foundations of pedestrian bridges considering uncertainties such as environmental and operational variations (EOVs) inherent in dynamic responses. For this purpose, transient dynamics numerical computation models are established to simulate the multi-point dynamic response of the structure under different typical damage scenarios, forming the baseline dataset. Then, on-site vibration tests are conducted on the structural prototype. Ambient vibrations of the real intact bridge are considered EOVs and integrated into the baseline dataset, forming the test dataset. Additionally, the intact structural dynamic response with measured EOVs replaces the simulated intact structural dynamic response in the baseline dataset to form a reference dataset. The network architectures based on one-dimensional convolutional layers proposed in this paper are trained on the baseline dataset and reference datasets to obtain baseline and reference models. Subsequently, model performance evaluation is conducted on the test dataset, and the results indicate a significant decrease in the performance of damage models based on a single deep learning when EOVs are present. However, integrating the baseline and reference models achieves zero false negative/positive predictions which is safety-oriented and an exemplary classification accuracy of up to 97.2 %.

@en

The Stiffness Damage Test (SDT), a cyclic test in compression, is considered as a reliable tool for assessing concrete structures affected by ASR. Depending on the extent of ASR damage in concrete, loading levels up to 40% of the compressive strength may contribute to increasing internal damage during testing. Nevertheless, previous research found that no additional damage was induced by the SDT. This confirmed the non-destructive character of the SDT making it valid to determine the compressive strength on the same test specimens following the SDT. However, other research suggests that loading levels above 15% of the compressive strength could lead to load-induced damage in the first load cycle. The implication of the non-destructive character and the loading level of the SDT needs more attention, especially when testing anisotropically ASR-damaged concrete structures. This paper thus presents a critical evaluation of the non-destructive character of the SDT by utilizing Acoustic Emission (AE) measurements. The SDT was used to evaluate an ASR affected concrete structure after 60 years in use. Several cores from cantilever slabs were extracted enabling damage assessment of the concrete structure in use. AE allowed to measure crack occurrence with a higher accuracy. Therefore, the critical load level could be more accurately identified using AE. The magnitude of enhancing internal damage during the SDT is related to the extent of ASR. From this study it can be concluded that the non-destructive character of the stiffness damage test depends the critical load level in relation to the internal degree of damage, which can be determined by means of Acoustic Emission.

@en

To date, there is no comprehensive approach available that can explicitly model the complete transient waveforms of acoustic emissions (AE) induced by fracture processes in brittle and quasi-brittle materials like concrete. The complexity of AE modelling arises from the intricate coupling between the local discontinuity of material fracturing and the global continuity of elastic wave propagation in solids. Among others, the lattice type models are promising approaches, as they are known to be a matured modelling approach to simulate the fracturing process in concrete-like materials. Nevertheless, like other discrete element methods (DEM), they are currently limited to describing the number and rate of AE events (broken elements) in the fracture process and cannot explicitly model wave generation and propagation. In this study, we propose a lattice modeling framework to simulate the propagation of complete waveforms of fracture-induced AE signals in concrete. A proportional-integral-derivative (PID) control algorithm is incorporated in an explicit time integration procedure to reduce dynamic noise from spurious oscillations. Additionally, a Rayleigh damping-based calculation method and corresponding calibration procedure are proposed to model the attenuation of AE signals due to material damping. Using the developed approach, we systematically investigate the feasibility of lattice models for elastic wave propagation simulation, the dependence of lattice mesh sizes and the choice of numerical damping parameters. These results lead to a systematic framework which can be employed in simulating wave propagation with attenuation using DEM models in general including lattice models.

@en

This study presents a damage pattern recognition approach for corroded steel beams strengthened by CFRP anchorage system based on acoustic emission clustering analysis. The proposed method includes four steps: acoustic emission signal acquisition, feature extraction, clustering analysis, and damage pattern recognition. Four corroded beams with different corrosion levels and strengthening schemes were tested under four-point bending loading. The acoustic emission signals were collected during the loading process and analyzed using Gaussian mixture model clustering method. The results showed that the collected AE data were analyzed using clustering analysis, successfully distinguishing the distinct damage patterns associated with each mode. The AE signals exhibited distinct characteristics for different damage modes: concrete matrix damage had high-frequency and low-energy characteristics, CFRP-matrix debonding showed intermediate values for all parameters, and CFRP tearing had longer durations, lower peak frequencies, and high-energy characteristics. Besides, the study identified three stages of the damage process: an initial stage with fewer low-intensity AE signals, a damage development stage characterized by an increase in concrete-matrix damage and CFRP – matrix debonding signals, and a continuous damage growth stage with significant AE signals associated with three damage modes. Furthermore, the degree of corrosion significantly influenced the cumulative AE energy of damage modes. Lower degrees of corrosion led to higher cumulative energy from concrete matrix damage and CFRP-matrix debonding. These findings provide valuable insights for understanding the damage evolution and failure mechanisms of CFRP-strengthened corroded beams. The use of AE techniques for damage pattern recognition can enhance the evaluation and design of CFRP anchorage systems, leading to more effective rehabilitation strategies for corroded structures.

@en

Externally bonded (EB) carbon fiber reinforced polymer (CFRP) is widely used in structural strengthening and retrofitting. Premature debonding of the FRP severely limits the efficiency of CFRP utilization. The application of CRRP anchorage system offers a solution to the debonding problem. However, the understanding of damage mode identification of this combined system still remains elusive. Acoustic emission (AE) technique is employed to identify the damage mode of this CFRP anchorage system, due to its high sensitivity and the ability to detect damage in real-time. The objective of the current study is to identify the failure mechanisms of CFRP-strengthened beam by applying advanced pattern recognition techniques to the collected AE data. Firstly, four-point test of CFRP-strengthened beam was carried out until failure with simultaneous recording of AE signals. Then, correlation analysis was adopted to select the AE characteristic parameters, and principal component analysis (PCA) was used for dimensionality reduction. Lastly, the AE signals of the CFRP-strengthened beam was clustered to track the evolutionary behavior of the different damage modes by Gaussian mixture model (GMM) algorithm. Three main damage modes of CFRP-strengthened beam were identified by GMM clustering: concrete cracking, debonding of CFRP sheet and fracture of CFRP sheet. This study explores the damage evolution mechanism of combined system and provides a basis for achieving health monitoring of CFRP-strengthened structures.@en

Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel deep learning algorithm called deep residual network (DRN), a structural health monitoring (SHM) strategy is proposed for AE source localization through classifying and recognizing the AE signals generated in different sub-regions of critical areas in structures. Hammer hits and pencil-leak break (PLB) tests were carried out on a steel-concrete composite slab specimen to register time-domain AE signals under multiple structural damage conditions. The obtained time-domain AE signals were then converted into time-frequency images as inputs for the proposed DRN architecture using the continuous wavelet transform (CWT). The DRNs were trained, validated and tested by AE signals generated from different source types at various damage states of the slab specimen. The proposed DRN architecture shows an effective potential for AE source localization. The results show that the DRN models pre-trained by the AE signals obtained in the undamaged specimen are able to accurately classify and identify the locations of different types of AE sources with 3–4.5 cm intervals even when multiple cracks with widths up to 4–6 mm are present in the wave travel paths. Moreover, the influence factors on the model performance are investigated, including structural damage conditions, sensor-to-source distances and AE sensor mounting positions; in accordance with the parametric analyses, recommendations are proposed for the engineering application of the proposed SHM strategy.

@en

Vibration-based structural damage detection (SDD) has been a subject of intense research in structural health monitoring (SHM) for large civil engineering structures over the decades. The performance of the conventional SDD approaches predominantly relies on the rational choices of the damage feature and classifier. Hand-crafted features or fixed classifiers would not be the optimal choice for all structural damaged scenarios. This paper proposes a novel, quick and precise real-time SDD framework for high-pile wharf foundations using a combination of finite element modeling and 1D convolutional neural networks (CNNs). The distinct advantage of this method lies in extracting the damage-related features from the raw displacement response directly and automatically, and the computational complexity of the compact 1D CNNs is significantly lower because the data processing involves only simple 1D operations. The results show that the presented 1D CNNs have a superior ability to accurately identify the occurrence and location of damage in real time. In addition, the comprehensive performance of the CNNs trained by the displacement response dataset in component form is significantly better than that based on the dataset in absolute value form. The results also demonstrated that although the proposed CNNs are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure, the vertical component still has a positive effect on the improvement of the generalization and robustness of the CNNs.@en

This paper presents the results of an experimental study on the flexural behavior of carbon fibre reinforced polymer (CFRP) anchorage system strengthened reinforced concrete (RC) beams under the coupled effects of corrosion damage and sustained loading. The test beams were subjected to combined accelerated corrosion in 5% NaCl solution and sustained loads for 25, 50 and 100 days at 0% and 50% load levels of the virgin beam ultimate load capacity. The failure modes, load carrying capacity, deflection, ductility and strain response of the beams were investigated in detail. The results indicated that CFRP anchorage systems enhanced the yield and ultimate load of the corrosion-damaged beams. The use of CFRP anchorage system restored the ultimate load of corroded beams between 87.6% and 104.8% and the yield load between 81.9% and 92.7% with respect to those of the virgin beam. In contrast, the ductility and energy absorption index suffered a decline. CFRP-strengthened beams showed a reduction of 4.5%–28.9% for the ductility index compared with their counterparts without CFRP anchorage system. Sustained loads resulted in more considerable reductions in load-bearing capacity, greater loss of rebars mass, wider width of corrosion cracks, indicating a significant coupling effect between sustained loading and corrosion damage. Three typical failure modes of the CFRP-strengthened beams were observed and explained in the paper, thus revealing the failure mechanism of CFRP-strengthened beams. In the engineering practice of CFRP anchorage system, the coupled effects of corrosion damage and sustained loading on the strengthened systems should be taken into accountant comprehensively.

@en

This study investigated the relationship between the acoustic emission (AE) signals parameter sequence fractal characteristics and the damage evolution information of corroded reinforced concrete (RC) beams under four-point bending. Strength deterioration behavior and AE data can be obtained by coupling the four-point bending test and the AE monitoring. The results show that AE ringing counts of corroded and uncorroded beams had prominent fractal characteristics. The fractal dimension values of corroded RC beams all showed a fluctuating rise to a peak and then a sharp drop before the failure. The damage index corresponding to the peak point decreases with the increase of corrosion degree. Fractal dimension peak point could be used as an early warning point for corroded RC beams' failure. Moreover, the AE fractal dimension analysis can effectively reflect the pattern of crack development, which have an important value for evaluating the process of corroded RC beams rupture.

@en

Accurate assessment of the overall mechanical behavior of masonry, composed of bricks and mortar joints, remains challenging due to its inhomogeneous and orthotropic nature. In this study, the feasibility of various mean-field homogenization schemes for the three-dimensional orthotropic elastic properties of masonry is comprehensively investigated. Three kinds of masonry patterns are considered, including the stack bonded pattern, the running bonded pattern and the double-leaf Flemish bonded pattern that has received limited attention so far. Special attention is paid to the homogenization schemes which have not been applied to the masonry case, such as Lielens’ interpolative double inclusions (D-I) and the interaction direct derivative (IDD) schemes. After a comparison between the well-known mean-field homogenization schemes, an improved micro-mechanical model is proposed by combining the advantages of the IDD and D-I models. The validation of the proposed model is conducted through a comparison against experimental data from literature and numerical results obtained via finite element analyses (FEA). The results show that the proposed model can accurately evaluate the orthotropic elastic properties of the three masonry typologies for a wide range of stiffness ratios between brick and mortar, ranging from 1 to 1000. The proposed model also shows better performance than the classical schemes especially when the stiffness ratios between brick and mortar are higher than 10, which is of major importance for the application of mean-field homogenization based multiscale methods to the nonlinear analysis of masonry. Furthermore, the presented homogenization method can be of interest for other anisotropic materials, e.g., laminate materials.

@en

Flow resistance, velocity distribution, and turbulence intensity are significantly influenced by aquatic vegetations (AV) in riparian zones. Understanding the hydraulics of flow with planted floodplains is of great significance for determining the velocity distribution profile and supporting the fluvial processes management. However, the traditional flume experiment method is inefficient. Therefore, the multigroup simultaneous flume test method was carried out to describe the flow patterns affected by emerged rigid (reed and wooden stick) and submerged flexible vegetations (grass and chlorella). The Acoustic Doppler Velocimeter (ADV) was utilized to measure the velocity at one point for different experimental conditions. The results showed that hydraulic features were influenced by different types of vegetation. Furthermore, the relative depth (z/h) was a determining factor of those variations. In addition, the time-averaged velocity distributions of planted floodplains are not logarithmic. Instead, they represented “s-shape” profiles. In detail, for the vegetated floodplains, reed and wood followed an s-shape profile, but for grass and chlorella, they followed reverse s-shape profile. For all cases, turbulence is not isotropic and the change law of turbulence intensity is different in different sections. The flow resistance, turbulence intensities, and Reynold stresses influenced by different types of vegetation were also analyzed.

@en

Water energy is a conventional source of source that is widely available in nature. It can be easily converted into high quality secondary energy-electric energy through hydroelectric power stations. Hydropower is not only a widely used conventional energy source, but also a renewable energy source. Moreover, hydroelectric power has no pollution to the environment. Therefore, water energy is an inexhaustible and high-quality energy source among many energy sources in the world. However, vegetation can change flow structure and turbulence characteristics, impacting the use of water energy. In previous researches, few studies have focused on the comparison of velocity and turbulence influenced by various vegetations. Therefore, laboratory experiments were carried out to investigate hydrodynamics affected by submerged rigid vegetation (reed and wooden stick) and merged flexible vegetation (grass and chlorella) under different conditions. The time-averaged velocity distributions of planted floodplain are not logarithmic. Instead, reed and wooden stick followed an "S-shape"profile, but for grass and chlorella, they presented reverse S-shape profile. For all cases, turbulence is not isotropic and the change law of turbulence intensity is different in different sections.

@en