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• A novel method is proposed to evaluate the fiber content in fiber-reinforced concrete material. • The proposed method simultaneously utilizes percussion-induced homologous heterogeneous data. • The presented data fusion approach can improve the convergence speed of deep learning networks. • The integrated 1D-DAECNN can achieve concrete fiber content recognition under high noise conditions. With the development of materials science, fiber-reinforced concrete (FRC) has attracted much attention in building engineering areas and gradually alternated the traditional regular concrete in recent years. Since the fiber content significantly affects the performance of FRC material, it is of great importance to detect the fiber content in practical applications. Thus, this research proposes a novel percussion-based recognition method to evaluate the fiber content in FRC material, which contains a simple but effective time series data fusion approach and an adroitly integrated one-dimensional denoising autoencoder-enhanced convolutional neural network (1D-DAECNN). Particularly, compared with most current existing percussion-based recognition methods, which utilize single acoustic signals, the proposed method simultaneously adopts the homologous heterogeneous data obtained respectively from the microphone and embedded piezoceramic transducer after one-time percussion. Then, the homologous heterogeneous data are processed by the presented time series data fusion approach to achieve time domain waveform features fusion, which aims to improve the convergence speed of deep learning networks. After that, the 1D-DAECNN is constructed to identify the fiber content in FRC material. Via the front-end denoising autoencoder (DAE) and the back-end one-dimensional convolutional neural network (1D-CNN) in the integrated network, the noise suppression capability of the 1D-DAECNN can be improved, and the manual feature extraction can be avoided during the training process. Finally, the effectiveness of the proposed method is demonstrated via laboratory tests on five FRC specimens. The results indicate that, compared with the conventional percussion-based method, the convergence speed and recognition precision of the proposed method is significantly improved even under noise conditions. [ABSTRACT FROM AUTHOR]

This paper studies the hydration characterization of steel fibre-reinforced cement-based material (SFRCM) in very early age (0–20 h) using electromechanical impedance (EMI) technique and three-dimensional meso-scale hydration models. Compared with the conventional researches, this study not only conducts an EMI-based hydration monitoring experiment, but also presents a novel numerical method to comprehensively reveal the effects of different mixtures on the hydration characterization of SFRCM. To perform the hydration monitoring, piezoelectric-based spherical smart aggregate (SSA) sensors were embedded in SFRCM specimens to sample admittance signals. The measurements were conducted every hour after completing cast, and a total of 20 h was recorded. Accordingly, a novel three-dimensional meso-scale hydration model that assumes a kind of meso-structure containing aggregate particles and steel fibres is first developed using finite element method. The feasibility of this developed model for characterizing SFRCM hydration was verified by contrastively analyzing quantification metrics obtained experimentally and numerically. Eventually, the amount of aggregate particles and geometry of steel fibres were further demonstrated to potentially affect the hydration process. [ABSTRACT FROM AUTHOR]

• This study proposes a novel EMI-based non-destructive method for evaluation of fibre content inside SFC structures. • A concept of FuzzyEn is introduced for fusion of conductance and susceptance to construct sample features. • The proposed method improves the limitations of the current methods that normally apply to near-surface area evaluation. The uneven distribution of fibres in concrete is a key factor that affects the mechanical properties of the components/structures. Many approaches have been developed to detect the fiber content of concrete. However, most of the existing methods are expensive, destructive, and mainly suitable for near-surface areas. To evaluate fibre content inside the steel-fiber concrete (SFC), this study proposes an electromechanical impedance (EMI)-based non-destructive method using embeddable sensing technology. The proposed method employs the embeddable sensors to sample EMI siganls, the concept of fuzzy entropy (FuzzyEn) to extract the signal features and an ensemble machine learning algorithm known as the gradient boosting decision tree (GBDT) to classify different labels. To evaluate the fiber content inside SFC, firstly, lead zirconate titanate (PZT)-based smart spherical aggregates (SSAs) are embedded into the host components/structures with different fiber contents, and a series of admittance (reciprocal of impedance) spectra of the SSAs are sampled as the dataset. Subsequently, the conductance and susceptence (real and imaginary parts of the admittance spectra) are calculated using their corresponding FuzzyEn values as two sample features. Finally, with the help of the grid search cross-validation method, the GBDT classifier is trained to classify different labels corresponding to the fiber contents. An experimental study was conducted to validate the feasibility of the proposed method, and the results confirmed the ability of the method to evaluate fiber contents inside SFC structures. The embeddable sensing technology assisted method can significantly improve the limitations of the current methods that normally apply to near-surface area evaluation. [ABSTRACT FROM AUTHOR]

• An adjustable spatial resolution imaging method is proposed for damage detection in concrete. • The proposed method can image the damage by using few embedded piezoceramic transducers. • The spatial resolution can be adjusted by changing the number of utilized mother wavelets. • The effectiveness of the proposed method is discussed in contrast to the DAS method. The main challenge faced by the conventional delay and sum (DAS) damage imaging method for concrete is that the spatial resolution will be degraded when using few embedded piezoceramic transducers. To resolve this issue, an improved DAS imaging method with an adjustable spatial resolution is proposed based on multiple wavelet decomposition in this paper. First, the scattering stress waves received by the sparse embedded piezoceramic transducer array are decomposed by different mother wavelets and reconstructed according to the spectrum signature of the excitation signals. Then, the reconstructed scattering signals between each transmitter and sensor pair are combined to design the imaging function to enhance the spatial resolution of the conventional DAS method. Experimental results indicate that the proposed method can effectively achieve high spatial resolution imaging for damage detection through signal post-processing, even using few embedded transducers. Moreover, the spatial resolution of the proposed method can be adjusted by changing the number of utilized mother wavelets. [ABSTRACT FROM AUTHOR]

• The special mechanism of FDJ can amplify vertical slip displacements between walls. • The kinematics principle of FDJ is thoroughly analyzed. • The scaled model experiments are conducted to validate the effectiveness of the FDJ. • A simplified numerical model of the FDJ with different parameters is presented. The development of a novel friction damped joint (FDJ), which can be installed between two precast concrete walls as a type of vertical connector, is presented in this paper. The specially designed internal mechanism of the proposed FDJ can amplify the vertical slip displacement between precast concrete walls whilst dissipating energy at the same time. Meanwhile, the FDJ can provide enough lateral strength and stiffness to resist vertical slips induced by wind loads and earthquakes. After experimental and analytical studies on the FDJ parameters, the energy dissipated by the proposed FDJ is found to be independent of the excitation frequency. Subsequently, to study the effectiveness of the FDJ conveniently, a simplified numerical model is proposed. The simulation results indicate that, the main features of the FDJ can be captured by the simplified numerical model. To further illustrate the advantage of the FDJ when considering damage-plasticity control, simulated precast walls with different joints are analyzed under the lateral loading. The results show that the FDJ can consume a significant amount of energy, thus protecting the precast walls from damage. The FDJ plays a great role in reducing the plastic damage that may be sustained by the main structure. [ABSTRACT FROM AUTHOR]

Timber structures have been widely utilized in the field of construction. Nevertheless, these structures are susceptible to environmental factors that can cause internal damage, including cavities, cracks, and corrosion, which can significantly compromise their structural integrity and safety. However, the detection of such internal damages is often challenging due to their concealed nature. Existing methods for detecting internal damage in timber structures suffer from low detection efficiency and recognition accuracy. This study proposes an end-to-end damage detection framework that integrates emphasized channel attention, propagation, and aggregation-time-delay neural network (ECAPA-TDNN) with percussion techniques. This framework is designed to evaluate the internal damage status of timber structures. The paper also introduces a method for creating internal voids within timber structures and fabricates timber specimens containing various sizes of internal damage. Vibration responses at different locations on the specimens are obtained using percussion techniques and serve as input data for the network. The results indicated that the ECAPA-TDNN architecture could effectively identifies damage within the timber structure and accurately distinguish between different sizes of damage, furnishing superlative recognition performance transcending prevailing benchmark models. This study elucidates the potential of percussion-based techniques and deep learning methods in the damage detection field, as well as the possibility for the proposed method to be extended to other applications, including transformer inspection, vehicle vibration monitoring, and medical treatment. [ABSTRACT FROM AUTHOR]