“…The mel-frequency cepstral coefficients method was designed to mimic the logarithmic perception of loudness and pitch of the human auditory system, which has been introduced to detect and monitor damages in the field of structural health monitoring. 37–39 In detail, MFCCs 40,41 are the results of a cosine transform of the real logarithm of the short-term energy spectrum expressed on a mel-frequency scale, as the procedure shown in Figure 2.Figure 2.Flowchart of mel-frequency cepstral coefficient features extraction.…”
Timber structures have been a dominant form of construction throughout most of history and continued to serve as a widely used staple of civil infrastructure in the modern era. As a natural material, wood is prone to termite damages, which often cause internal cavities for timber structures. Since internal cavities are invisible and greatly weaken structural load-bearing capacity, an effective method to timber internal cavity detection is of great importance to ensure structural safety. This article proposes an innovative deep neural network (DNN)–based approach for internal cavity detection of timber columns using percussion sound. The influence mechanism of percussion sound with the volume change of internal cavity was studied through theoretical and numerical analysis. A series of percussion tests on timber column specimens with different cavity volumes and environmental variations were conducted to validate the feasibility of the proposed DNN-based approach. Experimental results show high accuracy and generality for cavity severity identification regardless of percussion location, column section shape, and environmental effects, implying great potentials of the proposed approach as a fast tool for determining internal cavity of timber structures in field applications.
“…The mel-frequency cepstral coefficients method was designed to mimic the logarithmic perception of loudness and pitch of the human auditory system, which has been introduced to detect and monitor damages in the field of structural health monitoring. 37–39 In detail, MFCCs 40,41 are the results of a cosine transform of the real logarithm of the short-term energy spectrum expressed on a mel-frequency scale, as the procedure shown in Figure 2.Figure 2.Flowchart of mel-frequency cepstral coefficient features extraction.…”
Timber structures have been a dominant form of construction throughout most of history and continued to serve as a widely used staple of civil infrastructure in the modern era. As a natural material, wood is prone to termite damages, which often cause internal cavities for timber structures. Since internal cavities are invisible and greatly weaken structural load-bearing capacity, an effective method to timber internal cavity detection is of great importance to ensure structural safety. This article proposes an innovative deep neural network (DNN)–based approach for internal cavity detection of timber columns using percussion sound. The influence mechanism of percussion sound with the volume change of internal cavity was studied through theoretical and numerical analysis. A series of percussion tests on timber column specimens with different cavity volumes and environmental variations were conducted to validate the feasibility of the proposed DNN-based approach. Experimental results show high accuracy and generality for cavity severity identification regardless of percussion location, column section shape, and environmental effects, implying great potentials of the proposed approach as a fast tool for determining internal cavity of timber structures in field applications.
“…It is well known that chloride migrates with moisture in concrete [ 56 , 57 ], and it is important to monitor chloride permeability and moisture level in a concrete structure [ 58 , 59 ]. In this research, the chloride permeability of concrete was determined by the electric flux method illustrated as Figure 9 .…”
Antimony (Sb) is a trace element applied widely in modern industry. A large number of tailing solid wastes are left and accumulated in the mining area after purifying the precious antimony from the antimony ores, causing serious pollution to the environment. The major aim of this study is to investigate the feasibility of utilizing antimony tailing coarse aggregate (ATCA) as a complete substitute for natural coarse aggregate (NCA) in high-strength concrete. Concrete specimens with 25%, 50%, 75%, and 100% ATCA replacing the NCA in conventional concrete were prepared for evaluating the performance of ATCA concrete. The investigators find that ATCA concrete has good workability, and the mechanical properties and long-term behavior (shrinkage and creep) of ATCA concrete with all replacement levels are superior to those of NCA concrete. The durability indices of ATCA concrete, such as the frost-resistant, chloride permeability, and resistance to carbonation, are better than those of NCA concrete. While the alkali activity and cracking sensitivity behavior of ATCA concrete seem to be decreased, nevertheless, the difference is not significant and can be neglected. The researchers demonstrate that all of the control indices of ATCA concrete meet the requirements of the current industry standards of China. Overall, ATCA can be used in concrete to minimize environmental problems and natural resources depletion.
“…The corresponding numerical simulation was developed with a focus on the acoustic-structure coupling, and the acoustic boundary conditions were satisfied through a perfectly matched layer (PML) [25]. Liqiong Zheng et al used Melfrequency cepstral coefficients (MFCCs) as the features of percussion-induced acoustics, and support vector machine (SVM)-based machine learning was utilized to classify results [26]. Dongdong Chen et al used power spectrum density (PSD) to process percussive sound, and a decision tree machine (DTM) learning approach was used to classify results [27].…”
Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases. A non-destructive method to detect pipeline ponding using percussion acoustic signals and a convolution neural network (CNN) is proposed in this paper. During the process of detection, a constant energy spring impact hammer is used to apply an impact on the pipeline, and the percussive acoustic signals are collected. A Mel spectrogram is used to extract the acoustic feature of the percussive acoustic signal with different ponding volumes in the pipeline. The Mel spectrogram is transferred to the input layer of the CNN and the convolutional kernel matrix of the CNN realizes the recognition of pipeline ponding volume. The recognition results show that the CNN can identify the amount of pipeline ponding with the percussive acoustic signals, which use the Mel spectrogram as the acoustic feature. Compared with the support vector machine (SVM) model and the decision tree model, the CNN model has better recognition performance. Therefore, the percussion-based pipeline ponding detection using the convolutional neural network method proposed in this paper has high application potential.
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